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BSc Final Year Dissertation

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BSc Final Year Dissertation Powered By Docstoc
					University of Sunderland

School of Computing and Technology
BSc (Honours) Degree in Computer Systems Engineering

Baseki Kentsenao Victor
Student Number: 079085656

Supervisor: David Brazier

Project Dissertation was carried out in fulfilment of regulations governing the award of Degree of BSc in Computer Systems Engineering, University of Sunderland 2008

Abstract
This dissertation details a project to design and implement a prototype system that would provide a best route for a power line. The system is therefore called the power line route optimizer. This system uses an optimization algorithm to enhance efficiency and cost effectiveness. The dissertation covers two research areas being; 1) a research on the current approach that is used for route selection and, 2) a research on possible optimization algorithms that can be used. Finally a prototype system was designed, implemented and tested based on the findings from the research areas. An evaluation of the overall success of the project was conducted, stating any failures, achievements and recommendations for future work.

**The document has approximately 12,738 words in length, excluding References and Appendices

Table of Contents

1.
1.1. 1.2. 1.3. 1.4. 1.5.

Chapter 1 – INTRODUCTION .......................................................... 6
Project Overview .................................................................................................. 6 Sponsor background ............................................................................................. 7 Project Aims Objectives ....................................................................................... 7 Limitations ............................................................................................................ 8 Project Report – General ...................................................................................... 8

2.
2.1. 2.2.

Chapter 2- Research on the current route selection approach ....... 9
Why this topic ....................................................................................................... 9 The current approach .......................................................................................... 10 GIS .............................................................................................................. 10

2.1.

2.1.1. Why do we need to know about Geography ........................................ 11 2.1.2. GIS Applications ....................................................................................... 11 2.2. 2.2.1. 2.3. 2.4. 2.5. GPS ............................................................................................................ 12 GPS Applications ............................................................................................ 13 Analysis of GPS and GIS ................................................................................... 14 Differences between GIS and GPS ..................................................................... 16 Other route selection approaches ........................................................................ 17

2.5.1. The LiDAR application ............................................................................. 17 How the LiDAR technology work.......................................................................... 17 2.6. 2.7. 2.8. Implications for the Project ................................................................................ 18 Recommendations .............................................................................................. 19 Conclusion .......................................................................................................... 20

3.
3.1.

Chapter 3 - Research appropriate and possible algorithms .......... 21
The Algorithms ................................................................................................... 22 3.1.1. The Travelling Salesman Problem, TSP............................................... 22 3.1.2. The Genetic Algorithm, GA ..................................................................... 24 3.2. 3.3. Conclusions and implications ............................................................................. 33 Recommendations for the project ....................................................................... 34

4.
4.1. 4.2.

Chapter 4 – ANALYSIS .................................................................... 34
The Current System ............................................................................................ 34 Requirements for the Proposed System .............................................................. 35

4.2.1. Requirements Elicitation .......................................................................... 36 4.2.2. Interface requirements ............................................................................. 37 4.2.3. Functional requirements .......................................................................... 37 4.2.4. Non-functional requirements................................................................... 38 4.2.5. Platform requirements.............................................................................. 39 4.3. 4.4. Constraints and Limitations ................................................................................ 39 Software Tools .................................................................................................... 39

5.
5.1.

Chapter 5 – DESIGN ......................................................................... 40
System Documentation ....................................................................................... 40 Data Flow Diagrams ............................................................................................... 40 Use Cases ............................................................................................................... 40

6.
6.1. 6.2. 6.3. 6.4. 6.5.

Chapter 6 - DEVELOPMENT AND TESTING ............................. 42
Technical Architecture ....................................................................................... 42 Interface Development ....................................................................................... 42 Unit Testing ........................................................................................................ 43 System Integration .............................................................................................. 43 Acceptance Testing............................................................................................. 43

7.
7.1. 7.2. 7.3. 7.3.

Chapter 7 Management and Control ............................................... 44
Introduction ........................................................................................................ 44 Project Planning and Management ..................................................................... 44 Terms of reference and Project Planning ........................................................... 45 Constraints and Limitations ................................................................................ 46

8.
8.1.

Chapter 8 – EVALUATION ............................................................. 46
System Evaluation .............................................................................................. 46 8.1.1. Achievements ............................................................................................ 47 8.1.2. System Weaknesses................................................................................ 47 8.1.3. Further Improvements.............................................................................. 48

8.2. 8.3.

Research Evaluation ........................................................................................... 48 Process Evaluation .............................................................................................. 49

9. 10. 11.

Chapter 9 – CONCLUSION ............................................................. 50 REFERENCES .................................................................................. 52 APPENDICES .................................................................................... 56
TERMS OF REFERENCE .................................................................................... 56 Overview .................................................................................................................. 56 Objectives ................................................................................................................ 56 Research Topics ..................................................................................................... 57 Constraints............................................................................................................... 58 Legal, Ethical and Professional Issues ............................................................... 58 Resources ................................................................................................................ 58 Gantt Chart .............................................................................................................. 60 Project schedule ..................................................................................................... 61 APPENDIX B – Sponsor Letter .................................................................................... 62 APPENDIX C – DESIGN ............................................................................................. 63 Context diagram...................................................................................................... 63 Low-Level 1 DFD .................................................................................................... 63 Low-Level 2 DFD .................................................................................................... 64 Level 3 DFD ............................................................................................................. 65 APPENDIX D – Testing ............................................................................................... 67 Unit Testing.............................................................................................................. 67 Acceptance Testing Plan....................................................................................... 68 APPENDIX E – Questionnaire ..................................................................................... 69 APPENDIX F – User Manual ....................................................................................... 71 Software Requirements ......................................................................................... 71 Using or running the system ................................................................................. 71 APPENDIX G – Acknowledgements ........................................................................... 72 APPENDIX H – Progress Report.................................................................................. 72 APPENDIX I – Glossary of Terms ............................................................................... 72

APPENDIX A – Terms of Reference, Gantt Chart And Schedule ............................... 56

1. Chapter 1 – INTRODUCTION
1.1. Project Overview

This project was carried as a fulfilment to obtain Honours Degree and to produce a Power Line Route Optimization system. The aim of the project was to at the end of it all, produce a working computer system that would provide the power line planning department with a most efficient and cost effective route for electrifying the villages and/or cities. The project offered the author a chance to learn new things and concepts. These were not only for personal importance or benefit but also academic where the author can carry the study further. The project brings new ways or approaches of tackling the optimization problems that are faced in the industry. The most interesting part in this project is the research on the algorithms that can be used in solving optimization problems. The paper is structured as follows; chapter two examines the current power line route selection approach, chapter three is a research on the possible optimization algorithms for the route selection system (basically Genetic Algorithm and TSP). An analysis of the current system and requirements for the proposed system are then discussed in chapter four, followed by the system design in chapter five, the development and testing is covered in chapter six and then the management and control detailed in chapter seven. In chapter eight the paper provides an evaluation on both the product system and the research. In this chapter the paper stresses out what has been accomplished or not in the whole project and states any further improvement. Chapter nine is just a brief conclusion on the whole project. It surmises all aspects that have been covered in the project. The paper ends up with a listing of relevant sources or references that have been used in the preparation of the project and lastly with a list of appendices.

1.2.

Sponsor background

Botswana Power Corporation, BPC, is the main supplier of electricity in Botswana. This is a parastatal organization i.e. is a company that is owned by the Government of Botswana and private entities. As a service organization BPC has a mission that reads thus “As a service organization, we facilitate sustainable and diversified development of our country by rendering quality electrical energy services that are affordable, efficient, safe and environmentally prudent”, (Botswana Power Corporation, 2007). The requirement for amount of the power cables and the number of poles has basically depended on the distance and number of cities to be networked with the power supply. The author has also assumed that there are some other costs associated on the laying out of the poles. In this case it has been noticed that at times the powering projects tend to take longer distances when networking the streets, villages, towns and/or cities rather than devising a way to find a shortest possible route to follow so as to cut the costs. It is with relevant consideration to the uprising costs indicated in the use of cable and poles that BPC has seen it vital to appoint the author to undertake this project, for the provision of a support system that will ensure that these projects are carried out at minimal cost. 1.3. Project Aims Objectives

The section below identifies a list of objectives for undertaking this project. These are, but not limited to; • To research on the current power cabling approaches that are used for electrification by the Botswana Power corporation and make recommendations for the project • To research and critically evaluate appropriate computer algorithms in the area of problem solving and make recommendations for the project. • To design, implement and test a prototype system that; will determine and provide, based on some scientific algorithm, the most effective route/path

that is accurate, reliable and timely. Design a User Interface using Java for interaction with the prototype system for networking villages, towns or cities including; • • • • • • • Requirements Specification System Design Implementation Testing

To prepare critical evaluation of the project. Write a project dissertation. Defend the project in a viva presentation. Limitations

1.4.

Because the product system was constrained only to the available resources it was only designed to compute the route for a power line without the use of effective systems such as GIS and/or GPS. 1.5. Project Report – General

The author has produced researches that meet the requirements of the university. Harvard referencing style has been used throughout the report. All authors used in this report can be found in the references section. Where text has been quoted directly from the source, it is put in quotation marks and an author is named together with the publish date of the source.

2. Chapter 2- Research on the current route selection approac0h
2.1. Why this topic Because the world that we do business in changes with time, there is a need to change even the way we do business to match up with the times and demands that we face nowadays. This requires an understanding of how our current systems work, see if there is need for change and therefore if a necessity for change recommend the changes and thus implement these changes. This research topic was chosen by the author so as to identify the way the current system employ the selection of a route for a power line. This research shall end up with a recommendation with regard to the findings pertaining to the current system. This research topic looks in detail the approaches that are currently in use for calculating the shortest and most cost effective route a power line must take. The author has conducted a comprehensive research using journals and some questionnaires sent to the project sponsor, who gave some in-depth insights about the system that they currently employ in the company, some face-to-face interviews with some of the employees and the management as well as some observations were carried out for coming up with this chapter. The research has thus shown that the systems that are in place are of great value because it is not only about finding the most optimum route but also with the consideration of the physical features that underlie the route being selected. Thus by far the sponsor has clarified that they use a more effective system that uses Geographic Information System and Global Positioning System. This is fully discussed in the following sub sections. Firstly the sections discuss the current approach and the technologies used and lastly analyses the use of these technologies.

2.2. The current approach From the discussion that was carried with the sponsor, it was noticed that the current approach, geographic information is received from the GPS and/or GIS system. This information is used to look at and analyse the terrain that lie the route that is selected for a power line. This information also includes the vegetation, landscape, and any useful information (e.g. if there are any rivers, hills/mountains, sewerage lines and other service providers’ infrastructure) to be considered when selecting the path for a power line. The system has shown during observations that it provides good positioning results for power line designing which has shown great appreciation by the sponsor. The design and selection of the power line is done by the personnel. There is no technology that is used to provide the designers with a more optimum solution to designing the route for a power line. This shows that there is still a need for a good technology that will provide with a more optimum and effective measure for the power line. The research has shown that selection of a power line lies with the designers but the GIS and GPS will provide with the geographical information and accurate coordinates. It will be of great importance to provide a brief discussion of both GIS and GPS to illustrate a history of the systems, the type of information they provide to control servers, and how they are used in the business world and lastly any difference between the two. GIS – A GIS is a system of computer software, hardware, and data used

2.3.

to manipulate, analyze, and graphically present a potentially wide array of information associated with geographic locations (Koontz, 2003). GIS is a system that uses the satellites that are placed on space to provide geographical information to control servers and GIS enable devices. The information that the system sends to the receivers is specified by the user because the GIS can be used in different industries for different purposes. The author has learnt that the

parameters for information differ from application to application depending on usage of GIS. For example the agriculture industry the information required may be for analysis to see how a number of cattle in a farm affect the vegetation in a selected area or identification of a fertile soil in a specific region. Whilst considering that, GIS can be used in the tourism sector to identify the wetlands in the country or to identify the green lands in Africa. Geographic Information System has also been used in archaeological services, scientific investigation. The list can go on until day end and these applications show how different the information that GIS provides differ from sector to sector.

2.3.1. Why do we need to know about Geography Geography plays a role in nearly every decision we make in the business world. We use geographical information when choosing our business sites, farming, planning distribution networks, responding to emergencies, or redrawing country boundaries, planning for power lines, mining etc. All of these situations require an understanding of the geographical locations and parameter that we are working on. 2.3.2. GIS Applications As stated in the above section, GIS has been used in a variety of fields. These include; • The Agriculture sector – when identifying the type of vegetation for farming, choosing appropriate soil and terrain for ploughing. • Electrical Engineering – companies like BPC are not an exception to this notion. As identified by the author earlier, geographical information is useful for illustration of the terrain that underlies the power line route. • The mining sector cannot be excluded from this area because they require knowledge of the earth surfaces and features that lie on the area of consideration for mining. The geographical information is used before (during explorations) and/or during the mining process.

•

The recent crisis of Global warming, the development of government policies for conservation of natural resources, these require the use of GIS to manage the environment. We need to be able to identify the areas that are at risk of human interference, e.g. the wet lands and thus GIS is a good solution to this. GPS – this system’s usage traces back to 1940 (the age of World War ĐĐ).

2.4.

By this time the signals were based on radio frequency and were used in WWĐĐ. The first navigation system was developed in 1940 and by 1957 the US Navy developed the first satellite navigation system and was tested in 1960. This is a system that provides information regarding the position of a device (that is GPS capable), the coordinates, its direction etc. GPS was initially used by the defence departments (US army) but was augmented to incorporate some signals that can be used by the civil society. This augmentation means that the satellite signals are in different categories. A signal was assigned to be used by the US military and the authorised users and the other signal to be used by the civil society. The signal that is used by the US military and other authorized users is basically encrypted to ensure maximum security. By 2005 a new civil signal was created which was to be used for safety-of-life services (Shaw M, 2004). Global Positioning Systems are normally referred to by the acronym GPS. The Global Positioning System is a network of 27 satellites that orbit the Earth. In addition, GPS can provide the coordinates of any location on the planet, accurate timing, and velocity information. This allows for GPS to be a very valuable instrument in a variety of applications (Adam T, et al, 2004). After reading and analysing all the research material the author has realised the only one common aspect of GPS. There is only one common agenda in all the definition found and these illustrate that GPS is an accurate measure to locating and positioning devices using the satellites that orbit the earth. This therefore means that the GPS is a system that deals with coordinates of the receiving

device and the orbiting satellite. The research has also shown that GPS provides highly accurate and precise positions for the devices that navigate the world. When people talk about a GPS, they usually mean a GPS receiver. The Global Positioning System (GPS) is actually a constellation of 27 Earth-orbiting satellites (24 in operation and three extras in case one fails). The U.S. military developed and implemented this satellite network as a military navigation system, but soon opened it up to everybody else. GPS is still being further developed to maximize accuracy and efficiency. More civil signals are being added so as to improve robustness of operations and system accuracy. As GPS units are becoming smaller and less expensive, there are an expanding number of applications for GPS (Corvallis Microtechnology, 1996). 2.4.1. GPS Applications As the research has show GPS can be used in a variable number of applications. This section identifies just a few of applications that have or still use GPS; • Logistics – the author has found that GPS has seen great appreciation in the courier industry. The companies use this system to locate or track their cargo. This is usually seen in the cargo ships, distribution trucks and parcel tracking for companies that do business on the Internet. • It is again used in the agriculture sector. Mostly in the cattle tracking systems. (Barbari M, et al, 2006)The use of GPS-collar receivers to study the position of animals is a common technique for studies on wild animal habitats (such as those of deer, bears, and wolves). Together, the GIS and the GPS facilitate an accurate representation of resources which will allow livestock managers to better estimate stocking rates and address improper livestock distribution. • The Global Positioning System has also been utilized for traffic management (Adam T, et al, 2004). The managers will be able to locate

where there is more traffic congestion and allow most cars to carry on with alternative routes. • Parcel delivery services – an example would be the companies that do business online. As the demand for better services by customers increase, there is also the need for confirmation of receipt of the parcels to avoid situations where the product is lost on the way and the service provider cannot actually know if the product is lost or delivered to the customer or where the customer has received the product and claims not to have received it. GPS has come to the rescue of such problems. 2.5. Analysis of GPS and GIS

GPS and GIS are both used for processing spatial data. They use global satellites that orbit the earth and send information to the control servers depending on the coordinates. The satellites are placed in space and the information this system sends to servers can be used for tracking of parcels for delivery. As the world sees an increase in a number of companies that do business on the Internet, customers do expect good services e.g. assurance of delivery. This requires that the seller also need to have confirmation of delivery. With the advent of GPS and GIS, online sales have been of great success. GPS has also seen great appreciation in modern expert systems like cattle tracking and/or car tracking systems. This shows how GPS has affected the world that we live in today. The incorporation of GPS in recent mobile devices like cell phones and PDAs is a good illustration of how GPS and GIS (discussed earlier) are of great importance to the modern business. The use of GIS and GPS in projects such as in the electric engineering provides a more concise environment to stand with but still the author sees a lot of potential in this project because the methods require some optimization algorithm. It should be noted here that GPS and GIS use satellites that orbit the earth and sends graphical images and/or information to the control serves or PDAs, based on their coordinates. This indicates that still the issue of finding the

shortest route for a power line lies with the designers who take into consideration the physical features that the power line goes over e.g. hills, rivers, sewerage systems, roads etc. One researcher and also author Mr. Zurawski, (Zurawski, 1985), wrote “to find the most appropriate route, a number of aspects must be considered. These aspects can be grouped into four major categories; technical aspects, economical aspects, environmental aspects and socio-political aspects. This statement indicates that the solution as to the most optimum and cost effective power line route lies mostly with the route designer and/or planner because considering the information sent by the GIS and/or GPS systems they can be able to analyse and come up with the best route. The choice on the best route is based on these factors stated by Zurawski (1985). Basing the decision of the costs of following one particular route over another will also be done looking at the geographic information sent by the GIS. The author takes into consideration that the information provided by both GIS and GPS can be used as parameters to the optimization algorithms (discussed in the next chapter) for a better and optimum solution to the problem. The study has shown that GPS represents a US$10 billion industry with 52% of the market residing in the United States (Adam T., et al, 2004). This statistical representation shows how GPS has seen a great appreciation in the business world. Even in most dangerous environments (e.g. pilots and mountain climbers) use GPS and it indicates how important GPS devices are in the world that we live in today. GPS is still being further developed to incorporate maximum accuracy and efficiency to business processes. Though GIS has been proclaimed as the best solution to most applications, many of the early Geographical Information Systems have been relatively technical and only really accessible to people with a high level of computer literacy. This means that the implementation of GIS required greater expertise. But with the

advent of new technologies, GIS is now accessible to the civil population. The developments have enabled everyone to buy a GIS enabled device and enjoy the benefits of GIS. 2.6. Differences between GIS and GPS An in-depth research was carried out by the author to understand how GIS and GPS differ. In undertaking this research the author has depended much on the paper by Mitch J. Duncan and W. Kerry Mummery, (Duncan M. J., et al, 2007). In this paper the duo has carried out a research on both GIS and GPS and has provided a good analysis of how GPS and GIS differ from each other.

•

From the paper it was found that both GPS and GIS provide the same results when applied to some certain procedure. GIS did not give out accurate results for route specification when compared to GPS. Travel routes estimated by GIS are not representative of actual routes measured by GPS, which indicates that GIS may not provide an accurate estimate of barriers encountered.

•

A geographic information system differs from other computerized information systems in two major respects. First, the information in this type of system is geographically referenced (geocoded). Second, a geographic information system has considerable capabilities for data analysis and scientific modelling, in addition to the usual data input, storage, retrieval, and output functions.

From other research papers it was learnt that the main difference between the two systems is the type of information they send to the devices. GIS sends geographical information that can be used to analyse a selected location whilst the GPS sends the geographical location of a device, together with the timely position of the sending satellite. The information sent from both systems is sued in different ways but can be integrated to form well concrete information for better and well-informed business decisions.

2.7.

Other route selection approaches

2.7.1. The LiDAR application The Chinese government has seen a big growth of population and thus the demand for electricity grows as well. In this regard the electric engineers had to construct more power lines to supply the customer base. To this notion, looking at the provided statistics, the Chinese electricity power line is regarded the second longest power line in the world running approximately 500 000 kilometres by 2004 (Xu, et al, 2008). To construct and manage these long power lines the Chinese energy departments have embarked on a project called a LIDAR application. The LiDAR technology uses a digital camera, with the use of the GPS technology (discussed earlier). How the LiDAR technology works The LiDAR system has three basic processes that make it all complete. These are; Data capturing This process requires a setup of a digital aerial camera, the laser scanner and the GPS system embedded in the system. Firstly a flight plan is required with an installation of GPS, INS system and the aerial camera. The system used laser ranging and aerial photography theory to acquire big area 3D geographical data. Data Processing In order to get the needed information the system needs to do some process with the acquired data. This data processing stage included sub stages like trajectory assurance, laser data processing, data classification, coordination setting, and photo orientation.

Design Optimization Lastly is the design optimization stage where the LiDAR technology is used to efficiently and effectively create and design the power line. A power line route is designed based on the 3D photos derived from the first stage. An example where this was carried out was the construction and design of Luoping-Baise 500kV second power line project. This project started from Luoping 500kV substation Yunnan province and ended at Baise substation Guangxi province (Xu, et al, 2008). Through the use of the LiDAR technology, route optimization was achieved, quality of service, environment was protected and saved. This has been quite a good invention by the electric engineers in China. The research has shown that this technology also uses GIS as the main source of the geographical information. This still puts more emphasis on the powerfulness of GIS and its usage. Besides its powerfulness, GIS needs a more sophisticated approach like the LiDAR technology to build up a more effective and effecient system. As illustrated previously, GIS and GPS cannot alone, without the use of some approach, provide us with the most optimum route for a power line. An integration of these with another technology or approach will be the answer to this for many businesses, hence the proposal for integration with an appropriate algorithm (Chapter 3). 2.8. Implications for the Project It can never be disputed that GIS and GPS are playing a major role in today’s business world. Both GIS and GPS have seen great usage with the introduction of PDAs and GPS capable mobile phones. This integration of GIS or GPS with PDAs and phones has taken the world by storm to date. From the research undertaken the author has realized that since 2005 the world has seen a vast increase in the use of PDAs and GPS capable phones. Now GIS and GPS are used in the civil engineering industry to enhance decision making, efficiency and cost effectiveness.

This research has been of great importance to the project as identified by the author. The research has resulted in the project being carried out in a well based knowledge of the underlying factors that shall be considered when selecting a route for a power line. Though the system will not be developed to satisfy this requirement the author will design the system with an assumption that the system derives the image maps from a GIS system that sends the digital map to a device. This research has also been a guide to the project in illustrating how important consideration of such issues and/or factors is to systems like these. The system though not bringing these into perspective will be designed in such a manner that the user has a basic knowledge of the underlying factors on the chosen route. 2.9. Recommendations As the author has reiterated, the system seems to be functioning well but the author has chosen this project so as to bring in a new concept of solving such problems like power line routing. This has brought in the integration of optimization algorithms (discussed in the following chapter) with the GIS and GPS systems. This would make our systems more cost effective and more efficient. In this regard it is essential to state that there is a lot of potential in this project and thus should be carried on because it was even proved to be of value to the sponsor. The project is therefore to be carried on and implemented using any of the optimization algorithms discussed in Chapter 3. From this chapter the author will be able to understand which optimization algorithm is appropriate for the product system and thus the project progresses.

2.10. Conclusion The undisputed fact here is that the use of GIS and GPS alone cannot (without the use of a computational system or algorithm) provide the designer with the most optimum route for the power line but they provide useful information to know if there are some terrains underlying the route they have selected. In this regard both GPS and GIS can be integrated with an optimization algorithm to provide a computation of a line. The author has therefore learnt that GPS and GIS are taking the world by storm as they are still being developed to cover all parameters of life. The research used for the production of this paper has shown beyond reasonable doubt that GPS and GIS are very powerful and are seeing great appreciation from the world population. Many systems, ranging from cattle or car tracking, tourism and power line designing have used or still use GIS and GPS. Though there is a considerable difference between GIS and GPS, integration of these together with optimization systems, as proposed by the author, will be of a valuable investment to companies such as Botswana Power Corporation. The systems developed in such a manner will cut the costs of service provision; will be efficient as well as provision of reliable business processes. We consider the LiDAR system which comprised of a GIS system and the LiDAR technology for optimization of power line designing. The author has learnt how important GIS and GPS can be in the implementation of the product system. The integration of these with an optimization methodology will be a great investment. Because of the limited resources such as GIS and GPS enabled devices or computers, this research has resulted in the production of a system that computes only the shortest route to be taken by a power line. The author has assumed that the designer will use the images provided by the GIS and GPS systems, and looking at the features of the terrain plot the lines for a power-line. The product solution will take these as parameters and computes an optimum route.

3. Chapter 3 - Research appropriate and possible algorithms
As many companies or businesses in the commercial industry work hard to become more effective and efficient in their business activities, Botswana Power Corporation is not an exception. BPC does also want to ensure cost effective and efficient processes when delivering products to their customers hence the need to research (by the author) on the possible optimization algorithms. This research area was chosen and carried in order to critically analyse some aspects on the possible algorithms that can be useful in solving the optimization problems of the power lines. Optimization is the most prevailing aspect in the global world as many companies try to ensure that systems provide optimum, efficient and cost effective measures of doing business. It is with this regard that the author has seen it vital to undertake a research on the possible algorithms that can be used for optimization problems. This research chapter has brought in just two (2) algorithms to research on, critically analyse and provide some recommendations. There are a number of algorithms that could also be looked at such as the Ant Algorithm, Djikstra and more others but the author has considered to just looking at the Travelling Salesman Problem and the Genetic Algorithm. For a good and more appetising research, Ant algorithm would be a good essence to also be looked at. The author has looked at it but cannot be stated once more in this paper. The author just wanted to get familiarized with the just a few of the algorithms and thus the other algorithms are out of scope for this dissertation though they could add up some bone on the flesh.

3.1.

The Algorithms 3.1.1. The Travelling Salesman Problem, TSP

From the research that has been carried out, the author has learnt that TSP has been a propelling approach for decades. It has been used for many years in most industries. TSP use traces back to the 19th century and has been studied for quite a long time until to-date. The general form of the TSP appears to have been first studied by mathematicians during the 1930s in Vienna and at Harvard, notably by Karl Menger (tsp.gatech.edu, 2008). This statement illustrates precisely how TSP has been useful and that it is not just a new approach but it is only that the study of TSP continues and becomes incrementally effective with time. Kylie Bryant (2000) considers that the idea of TSP is to find a tour of a given number of cities, visiting each city exactly once and returning to the starting city where the length of this tour is minimized. With this notion it can be easily stated that (with relevance to the project) when we want to electrify 65 villages, and we want to ensure that the power line traverse through all the villages, and thus entering just a village once until to the last village, then what will be shortest, efficient and cost effective route to take. This has now seen the emergence of this project. The TSP is another combinatorial approach to optimization problems as stated by the author earlier and there are other optimization algorithms which belong to the same listing with TSP. Travelling Salesman Problem has been seen as a proper solution to solving simple optimization problems as compared to other algorithms like the Genetic Algorithm. In this section the author identifies the applications that has used or use TSP as the optimization solution. Since TSP has been identified under a list of combinatorial optimization approaches, it can be applied to a wide range of discrete optimization problems to cut business costs and enhance efficiency. It is

with these applications that will guide and direct the project into the future. These applications include; − The TSP has seen its usage in the transportation and logistics applications for example where optimization is required for routing of school buses to pick up children for school in the district. − Another good example where TSP can be used is the messenger who drops off the mail for clients. He/she may need the shortest possible route from the base to customers and back to the base at a minimum cost and/or distance travelled. − In the recent applications, TSP has been used in job scheduling for optimization purposes. − 3.1.1.1. • TSP has also seen its usage in the designing of neural networks. Strengths of TSP The most obvious advantage of TSP is that it is very easy to design and therefore good for simple optimization problems. Having researched much on this algorithm the author has found from relevant sources some sample codes that modelled TSP and has learnt how easy and more interesting TSP is. The author has personally learnt that TSP is an easy and simple approach to solving optimization problems. • TSP uses a minimal number of solutions and derives an optimum solution to the problem. 3.1.1.2. Limitations of TSP

From the above section, the author has indicated that TSP finds a solution from a minimal number of solutions. This can be a disadvantage because it can be disputed that the population being used may have omitted most relevant and optimum solutions to the problem in hand. A set of solutions that TSP use may of lesser relevant to the problem being observed.

3.1.1.3.

Sample Pseudo-code for a TSP

Input: Number of cities n and array of costs c (i, j) i, j=1,..n (*_We begin from city number 1_*) Output: Vector of cities and total cost. (*_starting values_*) c=0; cost = 0; visits = 0; e=1 (*_e=pointer of the visited city_*) (*_determination of round and cost_*) FOR r=1 to n-1 DO choose of pointer j WITH minimum =c(e, j)=min{c(e, k); visits(k)=0 AND k=1,..,n} cost = cost + minimum; e=j; c(r) = j; END r-loop c(n)=1 cost = cost + c(e,1); The above algorithm models the flow of execution for a TSP solution. Firstly a list or cities should be defined and stored in an array or Vector together with the costs and/or distances associated with visiting each city. For illustration purposes the pseudo code does not include the distances between the specified cities. This information can be derived from a GIS system. 3.1.2. The Genetic Algorithm, GA

As much as the TSP, Genetic Algorithms have been used for quite a long time so far. GAs as illustrated by Adam M. (2004), were founded in the 1950s and 60s by evolutionary biologists who programmed them in the computers seeking to model the aspects of natural evolution. The GA is another combinatorial approach to solving optimization problems. It has also seen its usage over the past decades.

Research has shown that by the early 60s researchers such as John Holland had started developing evolution-inspired algorithms for function optimization and machine learning (Adam M., 2004). GAs as defined by Holland (1962) is the abstraction and formalization of naturaladaptation mechanisms for general purpose computations. Genetic Algorithm does not actually ensure optimal solution to a problem; however it usually gives good approximations in a reasonable amount of time. This means that the user may not be able to know if the solution given is the most optimum or not. Genetic algorithms are evolutionary techniques that use crossover and mutation operators to solve optimization problems using a survival of the fittest idea. Genetic algorithms constitute a class of search algorithms especially suited to solving complex optimization problems. Genetic algorithms transpose the notions of evolution in Nature to computers and imitate natural evolution. Basically, they find solution(s) to a problem by maintaining a population of possible solutions according to the ‘survival of the fittest’ principle (Renner G, et al, 2002). The issue that Genetic Algorithm uses an evolutionary approach to problem solving means that a population of solutions is created and using a “survival of the fittest” approach, weaker solutions are erased or eliminated and new generation of solutions is derived until the final solution is found. This final solution is therefore viewed as the most optimum solution to the problem given but the user may not be in a state to justify the issue. The GA uses the concepts like encoding, mutation, cross-over and evaluation where a population of solutions is derived and the concepts (mutation, encoding, cross-over etc.) are applied on them to come up with a more reasonable solution. The concept is applied to a problem by randomly finding solutions and then

combining the fittest solutions to create a new generation of solutions which would be better than the previous generation.

3.1.2.1.

How GAs work

For clarification purposes the author has provided a brief methodology of how GAs work. Genetic Algorithms start by initializing individual solutions (called chromosomes), selection of fittest solution (through an iterative approach), reproduction (mutation and/or crossover) and termination. Below the author describes the methodology in detail. t := 0; Compute initial population B0; WHILE stopping condition not fulfilled DO BEGIN select individuals for reproduction; create offsprings by crossing individuals; eventually mutate some individuals; compute new generation END As obvious from the above algorithm, the transition from one generation to the next consists of four basic components:

Initialization A random approach is used to generate individual solutions to a problem. These randomly generated solutions are formed to create a population of solutions which will be the basis for the next step (selection).

Selection This is an iterative step where, from the initial population, a ‘survival of the fittest’ approach is used to select the best set of solutions. This new set of solution forms a new set of solution. The process continues to the next step.

Reproduction (also called cross-over) The following step is to create a second generation of solutions through biological means (crossover also called mutation). This step requires that a two parent solutions are selected to create a new child solution. The newly created offspring would normally have features from both of its parents. The process goes back to the previous step until a specific number of generations have been created. A best-fit approach will still be used in this process to select the most optimum solution. Basically, crossover is the exchange of genes between the chromosomes of the two parents (Bodenhofer U., 2004). This implicitly means therefore that the two parents are taken together at an intersection to form a new solution. This combination resultant can now be change accordingly and tested against the specified condition. Though it is beyond the scope of this research, David Beasley, David R. Bull, Martin Ralph R., 1993, had stated the two types of cross-over. These are the 2Point crossover and the Uniform crossover. A brief clarification of how the two methods differ is basically that; Under uniform crossover, schemata of a particular order are equally likely to be disrupted, irrespective of their defining length. With 2-point crossover, it is the defining length of the schemata which determines its likelihood of disruption, not its order. This means that under uniform crossover, although short defining length schemata are more likely to be disrupted, longer defining length schemata are comparatively less likely to be disrupted. Uniform crossover has the advantage that the ordering of genes is entirely irrelevant. This means that reordering operators such as inversion are unnecessary, and we do not have to worry about positioning genes so as to promote building locks (David Beasley, et al, 1993).

Termination The last step that this process comes in is the termination stage. This stage is only reached when a specified termination condition has been met. There are different conditions that can be specified by the designer which when reached the process terminates. These include, but not limited to; • • • The specified number of generations have been reached The most optimum solution is reached that satisfies the specified criteria Or a combination of different conditions.

Genetic algorithms constitute a class of search algorithms especially suited to solving complex optimization problems (Renner G, et al, 2003). This notion illustrates that GAs are commonly used to solve complex optimization problems. This, in the authors view, shows that it requires concrete expertise to use Genetic Algorithms to solve optimization problems. It should be noted also here that this notion will impact the decision for implementation of the final solution considering the knowledge and expertise the author has in the field of optimization problem solving. Considering the statement made by Renner (2003), it is justifiable that Genetic Algorithms are a very useful approach that has been used for many years for complex optimization problem. In the journal they state that when using GAs a proper representation and fitness measure must be designed, the construction of a chromosome comes to be the first step the designer must take. To add more to its complexity, GAs are very hard to analyze especially when applied into a more difficult problem.

3.1.2.2. The Algorithm Pseudo code Following is a Pseudo code that illustrates the complete flow of a Genetic Algorithm applied to a problem area. This part was derived to show how the implementation of GA can be done. t := 0; Create initial population B0 = (b1,0, . . . , bm,0); WHILE stopping condition not fulfilled DO BEGIN (*_ proportional selection _*) FOR i := 1 TO m DO BEGIN x := Random[0, 1]; k := 1; WHILE k < m & x < ∑k j=1 f(bj,t)/ ∑m j=1 f(bj,t) DO k := k + 1; bi,t+1 := bk,t END (*_ one-point crossover _*) FOR i := 1 TO m − 1 STEP 2 DO BEGIN IF Random[0, 1] ≤ pC THEN BEGIN pos := Random{1, . . . , n − 1}; FOR k := pos + 1 TO n DO BEGIN aux := bi,t+1[k]; bi,t+1[k] := bi+1,t+1[k]; bi+1,t+1[k] := aux END END

END (*_ mutation _*) FOR i := 1 TO m DO FOR k := 1 TO n DO IF Random[0, 1] < pM THEN invert bi,t+1[k]; t := t + 1 END

3.1.2.3. Difference of GAs from traditional methods Compared with traditional continuous optimization methods, such as Newton or gradient descent methods, we can state the following significant differences (Bodenhofer U., 2004): • GA manipulates coded versions of the problem parameters instead of the parameters themselves. • While almost all conventional methods search from a single point, GAs always operates on a whole population of points (strings). This contributes much to the robustness of genetic algorithms. It improves the chance of reaching the global optimum and, vice versa, reduces the risk of becoming trapped in a local stationary point. • Normal genetic algorithms do not use any auxiliary information about the objective function value such as derivatives. Therefore, they can be applied to any kind of continuous or discrete optimization problem. The only thing to be done is to specify a meaningful decoding function. • GAs use probabilistic transition operators while conventional methods for continuous optimization apply deterministic transition operators. More specifically, the way a new generation is computed from the actual one has some random components (we will see later by the help of some examples what these random components are like).

3.1.2.4.

Applications of GAs

From the research done by the author it is clear that genetic algorithms have proven to be a powerful and successful problem-solving approach, basically demonstrating the power of evolutionary principles. Genetic algorithms have been used in a wide variety of fields to provide with optimum solutions to problems as difficult as or more difficult than those faced by human designers. This meaning that the GA is mostly used in complex problems that require much expertise and more computation is a factor to consider when making recommendations for its usage. A number of applications have arisen that have seen the use of Genetic Algorithms. These applications are, but not limited to; • Travelling salesman, TSP, It has been learnt by the author that GA can be used to solve the Travelling Salesman Problem. • The most recent application where GAs is used is in Time-tabling problems, e.g. designing a non-conflicting timetable for a college. • Scheduling applications, a clear example being job-shop scheduling. In this type of application, the main objective is to schedule jobs in a sequence dependent or non-sequence dependent approach in order to maximize the volume of production. • The Resource-Constrained Project Scheduling Problem, (Hartmann S., 2002). 3.1.2.5. Strengths of GAs

Adam M. (2004) suggests that the first and most important point is that genetic algorithms are intrinsically parallel. Most other algorithms are serial and can only explore the solution space to a problem in one direction at a time, and if the solution they discover turns out to be suboptimal, there is nothing to do but abandon all work previously completed and start over. It is also vital to note that the strength with GA lies with the fact that GAs has multiple offspring; this illustrates that they can find solutions in more complicated and different ways as

compared to other approaches. Given a population of solution, GA can find the most optimum solution other than most optimization problems because they seem to deal much with just a lesser number of solutions. This gives the Genetic Algorithm an upper hand over other optimization approaches. The main advantage of the genetic algorithms is that they can find a feasible solution for a very short time (Karova M, et al, 2005). From the very beginning the author has illustrated the importance of efficiency when doing business, the finding of a solution in the shortest time by GA comes to be an answer to business managers and system designers. The implementation of effective and efficient systems is of great importance to every company in today’s world. The most common agenda here is that Genetic Algorithms are a powerful strategy for solving the optimization problems faced in the industry or our daily lives. The biggest advantage of GAs is that they find solutions from an evolutionary approach. Thus from a specified problem the GA can create a population of solution, the generation being manipulated (i.e. through mutation, mating, crossover and/or evolution) to define new generation of solutions until the most fit and optimum solution is realized.

3.1.2.6.

Limitations of GAs

Although genetic algorithms have proven to be an efficient and powerful problemsolving strategy, they are not a panacea. GAs does have certain limitations and these are; • GAs is complex and requires an in-depth understanding of their concepts. As illustrated by the author from the previous section about TSP, the author has also searched for some sample GA codes and learnt how much complicated GA is. It requires more expertise and understanding of its concepts (i.e. mutation, crossover etc).

•

The author has learnt that GA cannot justifiably be said to have produced the most optimum solution to a problem because the computation is performed in a way that is out of reach for human nature i.e. the designer or user cannot be sure if the solution provided is the fittest one.

•

Because GA finds solutions in an evolutionary manner, the author considers that nature can evolve in disastrous way and thus the advantage of evolutionary approach of GA can pose threats to problem solving. The solution that is produced may be of critical harm to the system being developed or maintained.

3.2. Conclusions and implications It is of great importance to note here that both TSP and GA are useful for solving optimization problems. The research indicates that there is much potential in the project and the product software solution. The implementation of the solution and selection of the algorithm to use will depend on the knowledge the author has gained from the research in regard to the algorithms and his/her programming skills. Having research much on these approaches to an extent that the author searched for sample codes, it has been learnt how different these two methodologies are and which algorithm is suitable for implementation in the system being developed. During the research it was found how the two methodologies have been doing in the world. The two algorithms have seen much usage in the world systems and it is notably relevant to state that both approaches are important and good for recommendation. This research has resulted in the implementation of a system that computes, using the Travelling Salesman Problem algorithm, and from a set of villages, the shortest route for a power line.

3.3.

Recommendations for the project

From the previous chapter the author has stated that there is a need to consider and recommend an appropriate optimization algorithm for this project. Because the project has been seen as a great innovation, and that an extensive research on the possible algorithms has been carried out, the author has seen it vital to choose TSP as a good tool for our problem. This recommendation is based on the research that the author has carried on both algorithms, and with a clear understanding of how they both work, the author (being a student with no much expertise) has considered the use of TSP vital because it requires no much expertise and is useful for simple optimization problems like ours here.

4. Chapter 4 – ANALYSIS
4.1. The Current System

The following section analyses and describes the approach that currently BPC employs for power line route selection. This section can be regarded as a feasibility study to see if the system being proposed is of great importance or not and to also illustrate how it fits into the organization’s complete system. Firstly, as stated by Mr. Mosojane, a way-leave (permission to contract power infrastructure) is applied for from the land authorities (Land Board, Councils, etc). Then, after the permission has been granted, the infrastructure is laid out. The route is captured using a GIS (Geographical Information System) that uses GPS (Global Positioning System) which captures and stores coordinates. The information can now be displayed on the screen or printed out as maps. The approach seems to be effective in finding the route, the use of GIS and GPS has grown in today’s world. But with this regard the author has taken the project to bring in the TSP (Travelling Salesman) approach which is useful in optimization problems like the route selection that BPC carries. The author has taken into consideration the factors stated by Mr. Mosojane, these being the

physical features that lie in the route selected (Environmental, other service providers e.g. Water Utilities, Sewerage System). The production of data flow diagrams (DFDs) the author has been able to illustrate how the current system operates. These are depicted in Appendix CData Flow Diagrams.

4.2.

Requirements for the Proposed System

This chapter provides a detailed description of what the Power-line Route Optimizer is required to do. These requirements are fully based on what the author has found out from the research chapters and mostly reliant on the the current system pitfalls (if any). The research (Topic 1) has shown that the current system functions well and comes to be effective. The author should state here that the product solution comes in with a new concept, thus the use of the Travelling Salesman Problem algorithm to solve the underlying problem. Though the current system is functioning well, we bring in a different approach of solving the route optimization problem. When the world changes, the way we do business also need to change. The author has brought this brilliant idea to illustrate that an innovative society can bring new solutions to the problems that we face in today’s world, hence the proposal for this project. The author has learnt that new ways of finding solutions to problems are of great importance to OUR growing business challenges. It has been stated in the research that the current system use geographic information system (GIS) with the use of global positioning system (GPS) which are useful for considering the features underlying the selected line, the proposed solution narrows the scope to focus only in the provision of a shortest route. The author in this regard has assumed that the product solution can be integrated with the use of GIS and/or GPS to find a more powerful system. The author when setting the requirements for this system has assumed that the image map that the product system uses is derived from the GIS or GPS system.

4.2.1. Requirements Elicitation Though requirements gathering is not as simple as some would view it, in understanding and gathering the requirements for the proposed system the author has undertaken concrete measures to ensure that the system that is delivered meets the sponsor’s business needs and fits well in the business. The following methodologies were used to gather and understand the system requirements; • Through the design and issuance of Questionnaires the author was able to get the required information regarding the system. This methodology has been helpful in making sure that the author has access to some useful information that is beyond reach. • In supporting the findings from the questionnaires, the author has undertaken face-to-face (or direct) conversations with the project sponsor. This involved asking relevant questions and getting detailed answers. This type of requirement gathering was so helpful because what the author couldn’t understand was clarified right on the spot and if questions arose they were answered immediately. • The author has also embarked on designing a prototype which was sent for review to the project sponsor, who in turn provided some comments regarding the system. In this way the author was able to get the impression from the sponsor about the system. This approach was able to illustrate the boundaries of the system. It is quite useful in clearing any misunderstandings between the users and the designers. • Another useful approach was getting it right by Observations; the author has taken some visits to the sponsor’s premises in purpose of observing how the system really works.

4.2.2. Interface requirements User interfaces Below is a sketch of the planned user interface. This illustration was the initial prototype that was sent to the sponsor and discussed. This was a good step to understanding the requirements for the system. Having discussed it with the sponsor, changes were made accordingly. Figure 1 – The prototype screen BPC

Select region:

-- select --

FIND PRINT

B P C

4.2.3. Functional requirements The functional requirements are those that can be measured and that the system can be easily assessed based on. In this case the proposed system should; • Provide a graphical user interface that illustrates a map for the villages or cities that are to be electrified.

•

Provide a facility for the user to select the area to which electrification will be done.

•

From the selected area, highlight the areas with a mark as points. The points will be used for calculating the distance and the shortest route for the power line.

•

Calculate, from the selected points, the shortest possible route to be used for electrification of the selected villages and/or cities.

•

Provide an option for the user to print the output screen for future reference or filing.

4.2.4. Non-functional requirements These requirements are not measurable but a system may be required to show it conforms to them. These are; • It should not take long to run. This means that the application should not take or lock all the system resources and thus making the user to wait for long to get the results. • It should be platform independent. The software system should run on any machine regardless or the platform the machine runs. • For reliability purpose, the system should have a low MTTR (Mean Time To Repair) in cases of failure. It should also provide for appropriate results to the user. • The user should be able to carry around with them the system either in Compact Disks, DVD or USB Flash Disks. This would enhance portability of the system. • Usability – the system should be user5 friendly in terms of its user interface. • Scalability – the system should be able to accommodate any changes without failure i.e. accepting changes (if any) in its operational mode without malfunctioning.

4.2.5. Platform requirements The system shall run on a Pentium 4, DDR2 RAM, Windows XP Service Pack 2 or later, 4.3. Constraints and Limitations

It should be note here that the author would not be able to implement the system so as to consider the physical feature that underlie the route for a power line (i.e. the physical features like hills, sewerage system or other service providers). The college does not provide for such systems or hardware that is GPS enabled and thus this would limit the scope of implementation for the product system. The author will for prototyping and illustration purposes, use a map image assuming it was derived from a GPS or GIS system. This would show how much this system is of great potential to businesses that need to find good optimization solutions. 4.4. Software Tools

The software chosen for the implementation of the whole system and that are available are; • Java 2 JDK 5 for the design of the User Interface and the logical computation of the TSP algorithm. Java has been installed in the author’s personal computer at home and in the college computers. • The author has found the use of Eclipse, JGrasp or Textpad, which are all installed in the college computers and the author’s personal computer, a great deal for implementing the product system. • • Microsoft word will be used for documentation of the product system. Because this system does not require any database, the author does not consider any database tool essential to mention in this section.

5. Chapter 5 – DESIGN
5.1. System Documentation

From the start documentation of the system was a key concept to adhere to and was seen as an integral part of this project. Design documents are used to communicate with three groups of individuals. These groups of individuals are; • • • Those who will be implementing the design Those who will need, in the future, to modify the system and; Those who need to create systems or subsystems that interface with the system being designed In this regard the author has put in some UML notions of modelling the product system. This concept has been dependent on the illustrations from the previous chapter. The purpose of modelling the system was to clarify the logical mapping of the product system with its requirements. These UML diagrams include the context diagram, use case diagram, sequence diagram and they are all shown in APPENDIX C and they try to show a discrete model of the system that is to be designed. Data Flow Diagrams They illustrate how the product system will function indicating the input data, flow of data and the processes within the system and the outputs (printings or filing). The author under this section illustrates the sequence of events when the system is running. These are called the sequence diagram, which are also shown in APPENDIX C. Use Cases The general purpose of a use case diagram is to illustrate and show an organization of how what the system should do. Whilst it is based on what the user does it also expresses the tasks from the user in a natural language. Use cases also help in identifying the scope of the product system. This therefore depicts that the number of use cases in a system indicate the overall size of the system. See Appendix C – Design for these.

5.2. Design Concept From the prototyping and analysis of the prototype with the sponsor, the author has chosen to use the layout design and program logic provided by G. Darby, 2002. The system was designed and implemented using Pascal. The system was more of a game where the user selects the route they guess is the shortest and the computer shows an optimum route. This meaning the user plays against the computer. Though the author had no previous knowledge of Pascal, the author had to just grasp the logic and program flow as well as use the layout design which has an option for the user to select the number of cities. Figure 2 – The Travelling Salesman Problem, G. Darby (2002)

5.3. Modify Layout From the initial layout the author has modified the layout such that the user does not select a route but is only computed by the computer. The author has decided

to use the map of Botswana instead of the one provide with the system. An elimination of the time limit for an exhaustive search for a route was essential since it’s not a requirement for the product system.

6. Chapter 6 - DEVELOPMENT AND TESTING
6.1. Technical Architecture

The product system consists only of the Java objects and classes, the interface and files loaded from folders and text files. The system does not require any database system used; it is a standalone application that does not fetch any information from any database system. The product system computes logically the data the user inputs and process the data and then displays the results on the screen which the user can print for future reference.

6.2.

Interface Development

The underlying User Interface (UI) was developed using Java. The author has found it appropriate to use this language for development of the UI because; • Java is a platform independent language; this means that the application will be able to run regardless of the platform. • Because this project has been defined to develop a working prototype of the solution, the author has considered the use of a database system inappropriate. Therefore the system was just developed using object oriented programming (no database required). • Considering the level of understanding of languages, the author has been working mostly with Java programming. The testing strategy adopted was to use a three phase approach towards testing with the aims of ensuring that the system met the expected functional requirements and that as many defects as possible were identified and eliminated prior to project completion. A good test is the one that exposes defects

well in time to avoid defects that will be costly when found after the system has been integrated. 6.3. Unit Testing

The purpose of unit testing was to ensure that any defects were identified and rectified prior to integration of the system modules. This was conducted informally by the author during development of the UI of the system. A test log was created which is listed as Appendix D – Testing. Samples were used to test the UI so that any defect is identified and rectified; the example data used was the selection of the region of places to see if the application could highlight the selected areas in the region. Each test was assigned a number to make a statistical representation of results for better analysis of the system defects. The author has included in the test log an option to list any amendments that were required after every test performed. 6.4. System Integration

This type of testing is the bringing up together of all the system modules. This test was conducted immediately after the unit tests were completed and done by the author (designer). The designer has opted to use the incremental approach of this testing methodology. This type of testing (incremental) is useful over the big-bang approach because the designer can be able to identify where a defect lies in the system. Whilst using the incremental approach of integration, the author has opted to use the top-down testing. This meaning that the designer has started by testing the User Interface with just the underlying functionality without other modules. From this point the designer now added another layer of integration by adding other modules one-by-one into the system. 6.5. Acceptance Testing

This type of testing is preferably done by the end users. The latter can during this testing show an appreciation to the product system or not. Though it was not done with the project sponsor this type of testing is useful to understand if the

product system suits the needs of the sponsors or the business. Because of the time limits this testing was just done not by the sponsor but by some fellow students who have shown a lot of gratitude to the power line route optimizer. Acceptance testing tries to realise if the system, not only meets the specified requirements, but also satisfies all aspect of the user. These may include user friendliness, visibility covering issues of ergonomics etc. A system may meet its requirements but still not satisfying the end user. This type of testing was carried in order to see if the Power Line route Optimizer satisfies the user. See Appendix D – Testing.

7. Chapter 7 Management and Control
7.1. Introduction

This section describes and outlines how the author has managed and controlled the project from the onset (start of proposal), implementation and to the handingin of the final dissertation and product solution. This chapter details the way the project was carried from the start up until its implementation and handover. It start by looking at the how the whole project was managed and the tactics the author used when the project was found to have deviated from the planning methodologies used. The author also illustrates the problems that have been encountered during the project flow. 7.2. Project Planning and Management

The author of the project has from the onset believed in documentation approach to the project. The project documentation was delivered to the project sponsor and supervisor. The author has also defined the Terms or reference that saw the project supervisor and sponsor sign it off. Initially the author has appointed for a meeting with the sponsor to brief them about the project proposal. The sponsor required a documented terms of reference that will be the base for the project. From this conversation a good relationship was built and thus on every note

where the author required some guidance and information regarding the project, the sponsor was always there.

7.3.

Terms of reference and Project Planning

The author proposed a project and thus had to develop a TOR, with the provision of control to the project from the initial stages down to hand over. The author had to find a real problem in any organization i.e. project sponsor who was willing to help in making the project a reality. The author has thus identified BPC as the main project sponsor. The planning tools which for this project were the Schedule and the Gantt chart were produced by the author to indicate how the project phases flow and span. These planning tools are much helpful in keeping track of the project progress and ensure that the author makes concrete decisions when the project sees any deviations from the planned schedule. Appendix A and Appendix B show Terms of Reference and Schedule and Gantt chart respectively. The execution of the project was also a success following the initial plan detailed in the Gantt chart and schedule. The project, though having seen some deviations from the schedule, was properly controlled to be got back into track. The Project Progress Report that was designed for the second review has indicated previously that the project has seen a bit of deviation from the schedule because of the absence of the project sponsor’s personnel. The author has thus taken considerate measures to bring back the project in track so as to meet the project deadline, as detailed in the progress report (Appendix G). This was prepared for the supervisor during the second review of the project and submitted also to the project sponsor to illustrate to them how the project progressed.

7.3.

Constraints and Limitations

Due to the underlying factor that the college could not provide with necessary and relevant materials like the GPS enabled control system, this system could not be implemented with the provision of geographic maps from the GPS or GIS system. This resulted in an implementation of a mere system, using Java, that reads a map image from a folder and displays it and assumes it has been sent from a GPS satellite.

8. Chapter 8 – CRITICAL EVALUATION
In overall, the project is judged to have been a success. The project objectives have been met and the project has been delivered within agreed timescales, taking approximately four hundred and twenty six (426) hours in total to complete. Even though delivered on time the author has met some underpinning factors that if not of the schedule, the project could be a failure. These include the time when the planning engineer from the sponsoring company BPC, Mr. Mosojane, was out of the country for two weeks. This affected the progress of the project because the schedule indicated that some project stages depended on the information to be gathered from the sponsor. As a good planning tool, the Gantt chart and Schedule helped in getting the project back to track and thus seeing the project delivered on the set date. 8.1. System Evaluation The system has by far managed to meets the requirements that have been specified in the Requirements Analysis section. The system has been tested and has passed all the tests that were designed but there is still more room for improvement to this system because the research carried out has exposed a lot more of the potential software and/or architectures that can be used to enhance the functionality of the product system.

The project sponsor has been quite impressed by the prototype system despite some minor failures that have been encountered due to some constraints listed in previous chapters. The sponsor has also reiterated how this project can be done out of an academic perspective and better improved further where it would be integrated with GIS and GPS systems. 8.1.1. Achievements Despite the failures, this prototype system meets most of the requirements. The author has considered most how the system design and implementation was approached. It was stated earlier in previous chapters that the system would only be for route optimization purpose. Thus the prototype system is able to; • Allow the user to specify a list of cities which will be the base for the TSP algorithm. • From a list of cities provided by the user, compute the shortest route for a power line. • • Draw or displays the route on the screen for the user to view it Allow the user to print the screen for filing and/or future reference. 8.1.2. System Weaknesses In this section the author identifies the key issues that the system could not meet. In carrying out this identification, the author has looked at the test cases that were used to test the system. These are also dependent on the system requirements that were specified in the requirements section. Below is a list of these issues; • The system cannot actually provide a set of routes that were considered until the final solution was met. • The product system does not actually use the information provided by a GIS system. • Considering visual aspect, the system does not communicate well with the user what it does. An explanatory guide shall be provided to guide the user on how to do certain operations not just a simple user interface.

8.1.3. Further Improvements Because the system has not achieved some of its requirements, there is still room for improvement and thus the author in this section identifies some key issues that can be further brought into the scope of the system so as to make a more interesting and fully functional system that meet not only its requirements but also the business needs for the project sponsor. • The system as was the case is not using the GIS and/or GPS systems to base the route selection on. As stated the system can be further developed to incorporate the use of GIS and/or GPS information so that the decision for a route is based on this information. 8.2. Research Evaluation The research conducted into the selected areas (by the author and also approved by the supervisor) provided some useful insights into areas that have never been met and explored by the author. The author feels that this research has been applied to the project but recognises the fact that there is a significant amount more to be learned to better the product system especially in the area of incorporation of GIS and GPS in the system. The first research chapter was more of a simple term when we looked at its entirety but the author has learnt that there is still more to learn from the research area. This means that the author didn’t go into detail of how the GIS and GPS communicate with the control servers and PDAs. A more research into this area is therefore required so as to understand the not only the basics of the systems but also to have an in-depth knowledge of how they can be integrated with the product Power line optimization System. The second research chapter on the algorithms that can be used for optimization, the author has learnt that both GA and TSP are useful and the most strong algorithm is the Genetic algorithm but the underlying factor to be noted is (as stated in the chapter) that evolution can come in a disastrous manner and thus

GA can pose threat to the system being designed. The author has learnt the strengths and limitations of both algorithms discussed and are now in a position to recommend an algorithm for a particular process depending on its complexity and requirement. These are the main things that can be identified in the research as great achievement for the dissertation, though not much outlined they have helped the author to design the system accordingly. It is therefore important to state here that the author is aware that the research can be furthered afar for a better, strong and equivocal dissertation. 8.3. Process Evaluation From the onset, communication between the author, the project sponsor and the supervisor has been maintained through direct communication or emailing (especially the supervisor). Though initially the author had had problems in identifying the most appropriate personnel to talk to at the sponsor’s premises, there has been a good relationship between the author and the sponsor who has by far stated that they are interested in having a copy of the product system. Another factor to consider is how the project was managed because it is quite usual to have projects deviating from the initial plans. This was through the world accepted methodologies (i.e. the use of planning tools), which are the Gantt chart and the Schedule. These were so much helpful in identifying any project deviations and keeping it back to track. As specified in the progress report that was submitted during the second review, the project realised some deviations from the schedule and thus a review on the schedule was undertaken. This was a very hard task because there was already a few weeks before project handover and thus a double effort measure was brought in. The author had to work under a limited time to meet the deadline.

In the implementation of the product system, the author has opted to use the Java classes, creation of objects so as to enhance a good application where defects are very easy to identify. Object oriented programming was a key measure to implement the system because it did not require any use of a database system.

9. Chapter 9 – CONCLUSION
Not only on a personal level but also on an academic level, the author has gained much from the project and has therefore gained a great deal of satisfaction from its completion. A greater awareness and appreciation of the importance of the areas covered in the research chapters (the algorithms and both GIS and GPS) has been gained and the author has been granted the opportunity to apply the knowledge gained from research in a practical manner. The author has gained basic knowledge of optimization algorithms (TSP and Genetic Algorithm) and how they can be brought in the field of civil engineering not forgetting the use of GPS and GIS. It should be clearly stated that the research has been an extensive part in this project and it was a more essential component for the greater undertaking of the whole project. Much attention was paid in the second research chapter (on appropriate algorithms for optimization). A lot was looked at where the use of Travelling Salesman Problem was the definite solution over the GA to the problem stated in the terms of reference. This research has opened the mind of the author in understanding the concepts behind the use of both algorithms and how they are or have been used in the business world. Another compromising part was the research o the current power line route selection method. This has brought in the identification of GIS and GPS which have both taken the business world by storm. These have been used in most sectors and/or industries, from the archaeological, scientific, engineering,

agriculture, computer science etc. This research has identified some key factors that need to be considered when selecting a route for a power line. Factors like the physical features, environmental, economic and other services like sewerage systems. The use of GPS and/or GIS would require some optimization processes so as to ensure that the factors have been considered but still producing a more optimum, cost effective and efficient route. With all these aspect considered the project has resulted in the production of a little sophisticated system that only computes from a list of cities a route for a power line. This project has finally assumed that the image map that is used has been received from a GIS system. This project was a success and has been appreciated by most individuals that the author has communicated to, either through face-to-face conversations or emailing. One of my college mates has found it a great achievement considering that without this project the author would never be in a position to understand such demanding and complicated situations like process optimization and how they can be solved using algorithms such as Genetic Algorithm and/or TSP. In designing, implementing and testing the product system the author was guided by the notion that goes; “If a builder build a house for someone, and does not construct it properly, and the house which he built falls in and kills its owner, then that builder shall be put to death”. (Lethbridge C. T. and Laganiere R., 2005) This notion tells us that despite our being as developers and designers we build systems for the end users. Therefore this system was designed with consideration to the needs of the client in this case our project sponsor, BPC. The author personally could not afford to be caught in the hands of shame for not delivering a complete system and on time. It’s quite impressive...huh?

10.

REFERENCES Adam M., 2004, Genetic Algorithms and Evolutionary Computation, accessed 15/10/08 at http://www.talkorigins.org/faqs/genalg/ Anonymous, 1996, Introduction to the Global Positioning System for GIS and TRAVERSE, Corvallis Microtechnology, Inc., 413 S.W. Jefferson Avenue, Corvallis, OR 97333 Applegate D. L., Bixby R. E., Cook W. J. (2006) - The Traveling Salesman Problem, Princeton Series in, Applied Mathematics. Journal of Mathematical Psychology 51 (2007) 401–402. Princeton University Press, Princeton, NJ. Aybars U., 2008, Path planning on a cuboid using genetic algorithms, Department of Computer Engineering, University of Ege, Bornova – _Izmir 35100, Turkey. Adam T, David C. Y. and Cheng Y. K., 2004, Global Positioning Systems: an analysis of applications, current development and future implementations, Computer Standards & Interfaces 27 (2005) 89–100 accessed 11/11/2008 at http://www.sciencedirect.com/science? Barbari M., Conti L., Koostra B.K., Masi G., Sorbetti G. F., Workman S.R. (2006) The Use of Global Positioning and Geographical Information Systems in the Management of Extensive Cattle Grazing, Biosystems Engineering (2006) 95 (2), 271–280 Available Beasley D., Bull D. R., Martin R., 1993, An Overview of Genetic Algorithms: Part 2, Research Topics, University Computing, 1993, 15(4) 170{181. Accessed 21/11/2008 at www.geocities.com/francorbusetti/

Blewitt G., 1997, Basics of the GPS Technique: Observation Equations, Department of Geomatics, University of Newcastle Newcastle upon Tyne, NE1 7RU, United Kingdom Bodenhofer U., 2003, Genetic Algorithms: Theory and Applications, Lecture Notes, Third Edition - Winter 2003/2004, accessed 21/11/2008 at www.flll.uni-linz.ac.at/teaching/Ga/GA-Notes.pdf Bryant K, 2000, Genetic Algorithms and the Traveling Salesman Problem. Chapter 3, pg 20, Accessed: 26/08/2008 at www.math.hmc.edu/seniorthesis/ Cai H. P., Chen Y. W., Hou F., Ke-Wei Y., Xue S. S., Xing L. N., (2008), A hybrid approach combining an improved genetic algorithm and optimization strategies for the asymmetric traveling salesman problem, Engineering Applications of Artificial Intelligence 21 (2008) 1370–1380, Available online at http://www.sciencedirect.com/science? Cook W., (2008), Travelling Salesman Problem, accessed 04/09/2008 at http://www.tsp.gatech.edu/ Botswana Power Corporation, 2007, Botswana Power Corporation Annual report 2007, pg 15 Duncan M. J., Mummery W. K., 2007, GIS or GPS? A Comparison of Two Methods for Assessing Route Taken, American Journal of Preventive Medicine, 2007, 33(1):51–53). Available online at http://www.sciencedirect.com/science? Hartmann S, 2002, A Self-Adapting Genetic Algorithm for Project Scheduling under Resource Constraints, Christian-Albrecht, University of Kiel 24098 Kiel, Germany, Accessed 20/08/2008 at www.bwl.uni-kiel.de/

Helvig C.S., Robins G. and Zelikovsky A., 2003, The moving-target travelling salesman problem, Journal of Algorithms 49 (2003) 153–174, Accessed 05/09/2008 at www.elsevier.com/locate/jalgor Hoon J., Keumwoo L. and Wookwan C., 2006, Integration of GIS, GPS, and optimization technologies for the effective control of parcel delivery service, Computers & Industrial Engineering 51 (2006) 154–162, accessed 11/11/2008 at http://www.sciencedirect.com/science? Horton I., 2005, Ivor Horton’s Beginning: Java 2 JDK 5 edition, JDK 5th Edition, Wiley Publishing Inc, Indianapolis, Indiana Karova M, Smarkov V., Stoyan P., 2005, Genetic operators crossover and mutation in solving the TSP problem, International Conference on Computer Systems and Technologies - CompSysTech’ 2005 Koontz L. D., 2003, GEOGRAPHIC INFORMATION SYSTEMS - Challenges to Effective Data Sharing, Testimony Before the Subcommittee on Technology, Information Policy, Intergovernmental Relations and the Census, Committee on Government Reform, House of Representatives, GAO-03-874T, United States General Accounting Office, Accessed 17/11/2008 at http://www.gao.gov/new.items/d03874t.pdf Lethbridge C. T. and Laganiere R., 2005, Object-Oriented software Engineering – Practical software Development using UML and Java, 2nd Edition, McGraw-Hill, United Kingdom Loverro D., 2004, Global Positioning System Joint Program Office overview, Acta Astronautica 54 (2004) 941 – 942, accesed11/11/2008, Available online at www.sciencedirect.com

McCall J., 2005, Genetic algorithms for modelling and optimisation Journal of Computational and Applied Mathematics 184 (2005) 205–222, accessed 11/11/2008 at http://www.sciencedirect.com/science? Oduguwa V., Roy R., Tiwari A., (2005), Evolutionary computing in manufacturing industry: an overview of recent applications, Applied Soft Computing 5 (2005) 281–299, Available online at http://www.sciencedirect.com/science? Pressman R. S., 2005, Software Engineering: A Practitioner’s Approach, 6th Edition, McGraw-Hill, Singapore Renner G., Aniko E., 2002, Genetic algorithms in computer aided design, Computer-Aided Design 35 (2003) 709–726, accessed 11/11/2008 at http://www.sciencedirect.com/science? Shaw M., 2004, Modernization of the Global Positioning System, Acta Astronautica 54 (2004) 943 – 947, accessed 11/11/2008 at www.elsevier.com/locate/actaastro Saiko D., 2005, Travelling Salesman Problem: Java Genetic Algorithm Solution, accessed 25/07/2008 at http://java-travelingsalesman.googlecode.com/files/ Westminster College, GEOGRAPHIC INFORMATION SYSTEMS, Available online at http://www.westminster.edu/staff/athrock/GIS/GIS.pdf Whitley D., 2006, A Genetic Algorithm Tutorial, Chapter 1, pg 1, accessed 18/10/2008 at http://www.cs.iastate.edu/~honavar/ga_tutorial.pdf

Xu, Yang, Huang, Wang, Liu, 2008, LIDAR Applications In the Electrical Power Industry, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008

11. APPENDICES
APPENDIX A – Terms of Reference, Gantt Chart And Schedule TERMS OF REFERENCE Student Name: Baseki K. Victor Project Title: Power Line Route Optimizer Overview Botswana Power Corporation, BPC, as the main supplier of electricity in Botswana has always been eager to provide electricity to its customers and to ensure that its supply base reaches every corner of Botswana. To do this BPC provides, with the assistance of the government, policies to network every village, town and/or city with enough power. The networking of the places requires a large number of gum poles and power cables. The requirement for amount of the power cables and the number of poles has basically depended on the distance and number of cities to be networked with the power supply. The author has also assumed that there are some other costs associated on the laying out of the poles. In this case it has been noticed that at times the powering projects tend to take longer distances when networking the streets, villages, towns and/or cities rather than devising a way to find a shortest possible route to follow so as to cut the costs. It is with relevant consideration to the uprising costs indicated in the use of cable and poles that BPC has seen it vital to appoint the author to undertake this project, for the provision of a support system that will ensure that these projects are carried out at minimal cost. Objectives The section below identifies a list of objectives for undertaking this project. These are, but not limited to;

•

To research on the current power cabling approaches that are used for electrification by the Botswana Power corporation and make recommendations for the project

•

To research and critically evaluate appropriate computer algorithms in the area of problem solving and make recommendations for the project.

•

To design, implement and test a prototype system that; will determine and provide, based on some scientific algorithm, the most effective route/path that is accurate, reliable and timely. Design a User Interface using Java for interaction with the prototype system for networking villages, towns or cities including; • • • • Requirements Specification System Design Implementation Testing

• • •

To prepare critical evaluation of the project. Write a project dissertation. Defend the project in a viva presentation.

Research Topics Topic 1 A research on the current power cabling approach that is used for electrification looking at their effectiveness.

Topic 2 Research and critically evaluate appropriate algorithms in the area of problem solving and their applicability in the field of computer-aided civil and infrastructure engineering. Constraints The project implementation is constrained to the available software and hardware in the college and thus the author has no choice but to use them. Another constraint is the time span that the project has which in this manner would narrow the scope of the project. Legal, Ethical and Professional Issues - With the provision of the Data Protection Act 1998-The information provided for this project will only be used for the purpose for which it is supplied and will never be provided to a third party without prior permission from relevant stakeholders. - The relationship between the client and the author should be well defined such that it allows a comprehensive environment for both parties. This involves the requirement specification they discuss mostly because the author has college restrictions on the scope and grading of their product system. - With clear indication that the author has an in-depth understanding of programming, especially in Java, it is therefore required that implementation should depend on the knowledge the author have and should not be compared to higher professionals/experts. Resources Hardware − The author will use the Pentium 4, Intel Celeron computer, 40/80Gb Hard Disk, 256 MB RAM available at home and in college campus. Software − Microsoft Project/Excel for project scheduling and controlling will be used,

− Java 2 JDK will be used to implement the User Interface and the computational algorithm. Advice will be provided by Mr. S Steward, lecturer, Botswana Accountancy College, Mr. David Brazier, University of Sunderland – UK and Mrs. T Selato – Botswana Power Corporation. Signed and agreed: Student: …………………………………….. Supervisor: …………………………………… Date: ….. / …... / 2008 Date: ….. / …… / 2008

Gantt Chart

Project schedule

APPENDIX B – Sponsor Letter

APPENDIX C – DESIGN Context diagram

POWER LINE ROUTE SELECTION USER

Low-Level 1 DFD

BPC Power Line Route Designing
2

Global Positioning System Derive Global Position

1

Geographic Information System Derive Geographical Information

3 Power Line Route Selection

Select the Power line Route

Domain of change

The diagram above illustrates the processes that the system employs and how data flows amongst these processes and entities. The area where the proposed

system focuses on is identified with a dotted line. As indicated in the paper, the product system tries to provide an optimum solution to route selection. Low-Level 2 DFD

Land Authorities
Power Line Route Designing
1 Regional Planning Application for way-leave Way-leave approval 2 Way-leave approval Regional Planning

Apply for way-leave

Acquire way-Leave

DESIGNER

3

GPS system Global Position coordinates, (x,y)

Derive the global position information

4

GIS system

Derive the geographic Digital maps information

Domain of change 5 Route selection

Select an appropriate route for power line

Level 3 DFD

Power Line Route Designing

Land Authorities
Apply for way-leave

Acquire way-Leave
D1

Way-leave

DESIGNER

Acquire Geographic Information

Compute shortest route for power line Print Map showing route selected

D2

Routes

M1

Routes

Use Case Diagram Below is a use case diagram depicting the actions and the users of the system.

Select a number of cities View Optimum route USER Print Screen

Class Diagrams

TSP_Solver
City: x, y point_name; distance; getCoordinates(City c); getName(); getDistance() createRandomSolutions() calculateRoute()

City
coordinates: x, y name; getCoordinates(City c); getName();

UInterface
JComponent; UInterface; UInterface(); paintComponent(); repaint(); actionPerformed();

Sequence Diagram A sequence diagram depicts the flow of events and execution of system or user events. The diagram shows the messages or data that is exchanged between system objects. From the above diagram, the author has identified and actor, who in this case is depicted as User, this is the initiator of the interaction. USER Select cities/villages Click on showRoute :clicked(showRoute) Shortest route coordinates Display power line route Click on Print Printed paper : Print() name, coordinates : calculateRoute() UInterface Selected cities/vilages TSP_Solver FILE

:getSelected()

APPENDIX D – Testing Unit Testing Unit Test Plan Project name: Power Line Route Optimizer Task No: Description Sheet no: 1 Date of Test: Expected Output An application showing 1 Start system an image map of Botswana. Select the points Show the areas that are 2 for electrification selected on the display screen Using a TSP algorithm Find shortest route 3 calculate the shortest route for a power line from the selected points Actual Result An application showing an image map of Botswana. Show the areas that are selected on the display screen Using a TSP algorithm calculate the system has calculated the shortest route for a power line from the selected points. Display the route A display of the computed A display of the on the screen for power line route on the 4 illustration to the computer screen. user. Allow the user to A printed paper indicating 5 print the display the output from the product system information computed power line route on the computer screen. A printed paper indicating the output from the product system

Acceptance Testing Plan Unit Test Plan Project name: Power Line Route Optimizer Task No: Description System 1 Visualisation Sheet no: 1 Date of Test: Expected Output The system should not affect the sight of the end user, good use of graphics and colour. Is the end user Indicate if the User is 2 impressed by the really impressed by the layout of the User GUI layout. Interface? Can the system really compute, 3 using the TSP algorithm, the shortest and optimum route for a power line? Does the system A printout of the system allow for printing map showing the 4 the result output? resultant route. The system should be able to provide the shortest and optimum route from a list of cities. Using the TSP algorithm Using a TSP algorithm calculate the system has calculated the shortest route for a power line from the selected points. Actual Result

APPENDIX E – Questionnaire “In accordance to the Data Protection Act 1998-The information provided will be used for the purpose for which it is supplied and will never be provided to a third party without your permission” In a verge to come up with a good project title the following questions have been devised to find a problem area in your organization. Questions: 1. How does your company provide routing of power lines when networking villages, towns, cities or streets? _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ ____________________________________________ Does the approach seem to be efficient and cost effective? YES [ ] NO [ ] (Please tick) Can this approach be modified such that it provides a facility that would calculate, based on a scientific/computational algorithm, the shortest possible route to network villages/cities/streets? The author has thought to bring a combinatorial approach to the system, which will be the basis for the project. YES [ ] NO [ ] (Please tick) Any comments

2.

3.

_____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ ______________________________________________ Do you see any benefits from the product system if this project is to succeed? YES [ ] NO [ ] (Please tick) What, in your view, can be added to the product system so it can meet your business needs? _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ I will be glad if this questionnaire be considered and answered and declare that any information provided will not be given to a third party without your permission. Thank you;

4.

5.

APPENDIX F – User Manual This section provides instruction or guidance on how the system is used and illustrates the required software tools. The author has assumed that the user is well familiar with computer ergonomics. If not please read the manual of the computer. Software Requirements • The system requires that Java is installed in the computer. To install Java, insert the CD into the CD/DVD drive of your computer. Open the folder named Java. To install Java, double click on the file named jdk-1_5_0windows-i586, follow the installation guide. If you already have Java installed in your computer, skip this step. Using or running the system 1. We hope you have not removed that CD from your CD/DVD drive of your computer. If you have, then re-insert it and then open the folder named Power line route optimizer. Double click on the file named startMe (a Batch File, extension is .bat). 2. The screen display that show up when the system start shall look like;

***************************************************** ***************************************************** ** ** ** ** Insert screen shot here ** ** ** ** ** ** ***************************************************** *****************************************************

APPENDIX G – Acknowledgements I would like firstly to thank the Lord for His grace and the strength He gave me in undertaking this project despite all the problems faced. And my family for providing their support when I was down, they stood out there for me. God be with them always. I want to express my sincere gratitude to Him Mr David Brazier for having been a patient supervisor and providing concrete advices in undertaking the project. I have benefited tremendously from his expertise in research and his enormous support. My appreciation for his mentorship goes beyond my words. Without him this project would not have been a success, Buddha shall bless him. Without getting further, I would like to pass great thanks to all my colleagues. The little help that I got from them has revitalised this project. The advices that they got from their own supervisors have been of so helpful in attaining the BSc Degree. Guys, without you, I this project would not have succeeded. To you all, God Bless! APPENDIX H – Progress Report

APPENDIX I – Glossary of Terms Algorithm - A well-defined procedure to solve a problem. It normally takes an input and takes a series of steps to compute a solution to a problem. Almanac - the Almanac is a file which contains positional information for all of the GPS satellites. The Almanac is used by the GPS receiver to determine which satellites to track, and can also be used for mission planning. Genetic algorithm (GA) - is a search technique used in computing to find exact or approximate solutions to optimization and search problems.

Geographic Information System (GIS) - a mapping system which combines positional data with descriptive information to form a layered map. Global Positioning System (GPS) - a system for providing precise location which is based on data transmitted from a constellation of 24 satellites


				
DOCUMENT INFO
Description: This project was undertaken for the fulfillment of the BSc degree in Computer Systems Engineering