Transportation_ Assignment and Transshipment problems

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					              Chapter 7
    Transportation, Assignment and
       Transshipment Problems

                 Applications of Network
                        Physical analog    Physical analog
      Applications                                                 Flow
                           of nodes            of arcs
                   phone exchanges,
                                          Cables, fiber optic Voice messages,
     Communication     computers,
                                          links, microwave          Data,
        systems       transmission
                                             relay links     Video transmissions
                   facilities, satellites
                      Pumping stations                        Water, Gas, Oil,
    Hydraulic systems                         Pipelines
                      Reservoirs, Lakes                       Hydraulic fluids
       Integrated      Gates, registers,
                                                Wires        Electrical current
    computer circuits    processors
                                            Rods, Beams,
Mechanical systems           Joints                            Heat, Energy
                         Intersections,      Highways,
     Transportation                                                freight,
                           Airports,        Airline routes
        systems                                                   vehicles,
                           Rail yards         Railbeds
2                                                                operators
    A transportation problem basically deals with the
    problem, which aims to find the best way to fulfill
    the demand of n demand points using the
    capacities of m supply points. While trying to find
    the best way, generally a variable cost of shipping
    the product from one supply point to a demand
    point or a similar constraint should be taken into

    7.1 Formulating Transportation
      Example 1: Powerco has three electric
      power plants that supply the electric needs
      of four cities.
      •The associated supply of each plant and
      demand of each city is given in the table 1.
      •The cost of sending 1 million kwh of
      electricity from a plant to a city depends on
      the distance the electricity must travel.
            Transportation tableau

    A transportation problem is specified by
    the supply, the demand, and the shipping
    costs. So the relevant data can be
    summarized in a transportation tableau.
    The transportation tableau implicitly
    expresses the supply and demand
    constraints and the shipping cost between
    each demand and supply point.

    Table 1. Shipping costs, Supply, and Demand
                for Powerco Example

       From                                  To
                    City 1 City 2     City 3 City 4       Supply
                                                       (Million kwh)
       Plant 1       $8         $6     $10        $9         35
       Plant 2        $9        $12    $13        $7        50
       Plant 3       $14         $9    $16        $5        40
      Demand         45         20     30         30
    (Million kwh)
                       Transportation Tableau
    1. Decision Variable:
       Since we have to determine how much electricity
       is sent from each plant to each city;

       Xij = Amount of electricity produced at plant i
       and sent to city j

       X14 = Amount of electricity produced at plant 1
       and sent to city 4
                  2. Objective function

    Since we want to minimize the total cost of shipping
    from plants to cities;

    Minimize Z = 8X11+6X12+10X13+9X14

                  3. Supply Constraints
    Since each supply point has a limited production

    X11+X12+X13+X14 <= 35
    X21+X22+X23+X24 <= 50
    X31+X32+X33+X34 <= 40

                  4. Demand Constraints
     Since each supply point has a limited production

     X11+X21+X31 >= 45
     X12+X22+X32 >= 20
     X13+X23+X33 >= 30
     X14+X24+X34 >= 30

                      5. Sign Constraints
     Since a negative amount of electricity can not be
     shipped all Xij’s must be non negative;

     Xij >= 0 (i= 1,2,3; j= 1,2,3,4)

             LP Formulation of Powerco’s Problem
     Min Z = 8X11+6X12+10X13+9X14+9X21+12X22+13X23+7X24

     S.T.:    X11+X12+X13+X14 <= 35           (Supply Constraints)
              X21+X22+X23+X24 <= 50
              X31+X32+X33+X34 <= 40
              X11+X21+X31 >= 45               (Demand Constraints)
              X12+X22+X32 >= 20
              X13+X23+X33 >= 30
              X14+X24+X34 >= 30
              Xij >= 0 (i= 1,2,3; j= 1,2,3,4)
        General Description of a Transportation
     1. A set of m supply points from which a good is
        shipped. Supply point i can supply at most si
     2. A set of n demand points to which the good is
        shipped. Demand point j must receive at least di
        units of the shipped good.
     3. Each unit produced at supply point i and shipped
        to demand point j incurs a variable cost of cij.

     Xij = number of units shipped from supply point i to
     demand point j
                           i m j n
                  min  cijXij
                           i 1 j 1
                         j n
                  s.t. Xij  si (i  1,2,...,m)
                         j 1
                  i m

                  i 1
                           ij    dj ( j  1,2,...,n)

                  Xij  0(i  1,2,...,m; j  1,2,...,n)

       Balanced Transportation Problem
     If Total supply equals to total demand, the
     problem is said to be a balanced
     transportation problem:

                 i m           j n

                s  d
                 i 1
                                j 1

     Balancing a TP if total supply exceeds total

       If total supply exceeds total demand, we
       can balance the problem by adding dummy
       demand point. Since shipments to the
       dummy demand point are not real, they are
       assigned a cost of zero.

     Balancing a transportation problem if total
          supply is less than total demand

       If a transportation problem has a total
       supply that is strictly less than total
       demand the problem has no feasible
       solution. There is no doubt that in such a
       case one or more of the demand will be left
       unmet. Generally in such situations a
       penalty cost is often associated with unmet
       demand and as one can guess this time the
       total penalty cost is desired to be minimum
      7.2 Finding Basic Feasible
            Solution for TP
     Unlike other Linear Programming
     problems, a balanced TP with m supply
     points and n demand points is easier to
     solve, although it has m + n equality
     constraints. The reason for that is, if a set
     of decision variables (xij’s) satisfy all but
     one constraint, the values for xij’s will
     satisfy that remaining constraint

     Methods to find the bfs for a balanced TP

       There are three basic methods:

       1. Northwest Corner Method

       2. Minimum Cost Method

       3. Vogel’s Method

               1. Northwest Corner Method
     To find the bfs by the NWC method:
     Begin in the upper left (northwest) corner of the
         transportation tableau and set x11 as large as
         possible (here the limitations for setting x11 to a
         larger number, will be the demand of demand
         point 1 and the supply of supply point 1. Your
         x11 value can not be greater than minimum of
         this 2 values).

     According to the explanations in the previous slide
     we can set x11=3 (meaning demand of demand
     point 1 is satisfied by supply point 1).



                 3          5     2       3

                 3                              2



                 X          5     2       3
     After we check the east and south cells, we saw that
     we can go east (meaning supply point 1 still has
     capacity to fulfill some demand).
                 3           2                  X



                 X           3    2       3

                 3           2                  X

                             3                  3


                 X           X    2       3
     After applying the same procedure, we saw that we
     can go south this time (meaning demand point 2
     needs more supply by supply point 2).
                 3          2                 X

                            3    2            1


                 X          X    X       3

                 3          2                 X

                            3    2       1    X


23               X          X    X       2
     Finally, we will have the following bfs, which is:
     x11=3, x12=2, x22=3, x23=2, x24=1, x34=2

               3        2                      X

                        3       2        1     X

                                         2     X

               X        X       X        X

                 2. Minimum Cost Method
     The Northwest Corner Method dos not utilize shipping
     costs. It can yield an initial bfs easily but the total
     shipping cost may be very high. The minimum cost
     method uses shipping costs in order come up with a
     bfs that has a lower cost. To begin the minimum cost
     method, first we find the decision variable with the
     smallest shipping cost (Xij). Then assign Xij its largest
     possible value, which is the minimum of si and dj

     After that, as in the Northwest Corner Method we
     should cross out row i and column j and reduce the
     supply or demand of the noncrossed-out row or
     column by the value of Xij. Then we will choose the
     cell with the minimum cost of shipping from the
     cells that do not lie in a crossed-out row or column
     and we will repeat the procedure.

      An example for Minimum Cost Method
     Step 1: Select the cell with minimum cost.

          2          3          5           6

          2          1          3           5

          3          8          4           6

     12          8          4           6

              Step 2: Cross-out column 2

          2            3         5             6

          2            1         3             5

          3            8         4             6

     12            X         4             6

     Step 3: Find the new cell with minimum shipping
                  cost and cross-out row 2

              2           3       5          6

              2           1       3          5
     2            8

              3           8       4          6

         10           X       4          6

     Step 4: Find the new cell with minimum shipping
                  cost and cross-out row 1

             2           3        5          6

             2           1        3          5
     2           8

             3           8        4          6

         5           X        4          6

     Step 5: Find the new cell with minimum shipping
                cost and cross-out column 1

             2           3        5          6

             2           1        3          5
     2           8

             3           8        4          6

         X           X        4          6

     Step 6: Find the new cell with minimum shipping
                cost and cross-out column 3

             2           3           5       6

             2           1           3       5
     2           8

             3           8           4       6
     5                       4

         X           X           X       6

     Step 7: Finally assign 6 to last cell. The bfs is found
      as: X11=5, X21=2, X22=8, X31=5, X33=4 and X34=6

               2           3           5           6

               2           1           3           5
       2           8

               3           8           4           6
       5                       4           6

           X           X           X           X

                     3. Vogel’s Method
     Begin with computing each row and column a penalty.
     The penalty will be equal to the difference between
     the two smallest shipping costs in the row or column.
     Identify the row or column with the largest penalty.
     Find the first basic variable which has the smallest
     shipping cost in that row or column. Then assign the
     highest possible value to that variable, and cross-out
     the row or column as in the previous methods.
     Compute new penalties and use the same procedure.

                      An example for Vogel’s Method
                       Step 1: Compute the penalties.

                                                           Supply   Row Penalty

                              6            7          8
                                                            10         7-6=1

                              15           80         78
                                                            15       78-15=63

     Demand              15            5          5

     Column Penalty     15-6=9     80-7=73      78-8=70

        Step 2: Identify the largest penalty and assign the
              highest possible value to the variable.

                                                         Supply   Row Penalty

                            6            7          8
                                                           5         8-6=2

                            15           80         78
                                                          15       78-15=63

     Demand            15            X          5

     Column Penalty   15-6=9         _        78-8=70

        Step 3: Identify the largest penalty and assign the
              highest possible value to the variable.

                                                           Supply   Row Penalty

                            6            7            8
                                                             0           _
                                 5            5

                            15           80           78
                                                            15           _

     Demand            15            X            X

     Column Penalty   15-6=9         _            _

        Step 4: Identify the largest penalty and assign the
              highest possible value to the variable.

                                                              Supply   Row Penalty

                               6            7            8
                                                                X           _
                      0             5            5

                               15           80           78
                                                               15           _

     Demand               15            X            X

     Column Penalty       _             _            _

         Step 5: Finally the bfs is found as X11=0, X12=5,
                        X13=5, and X21=15

                                                              Supply   Row Penalty

                               6            7            8
                                                                X           _
                      0             5            5

                               15           80           78
                                                                X           _

     Demand                X            X            X

     Column Penalty        _            _            _

       7.3 The Transportation Simplex

     In this section we will explain how the simplex
     algorithm is used to solve a transportation problem.

         How to Pivot a Transportation Problem

     Based on the transportation tableau, the following
     steps should be performed.
     Step 1. Determine (by a criterion to be developed
     shortly, for example northwest corner method) the
     variable that should enter the basis.
     Step 2. Find the loop (it can be shown that there is
     only one loop) involving the entering variable and
     some of the basic variables.
     Step 3. Counting the cells in the loop, label them as
     even cells or odd cells.
     Step 4. Find the odd cells whose variable assumes the
     smallest value. Call this value θ. The variable
     corresponding to this odd cell will leave the basis. To
     perform the pivot, decrease the value of each odd cell
     by θ and increase the value of each even cell by θ. The
     variables that are not in the loop remain unchanged.
     The pivot is now complete. If θ=0, the entering
     variable will equal 0, and an odd variable that has a
     current value of 0 will leave the basis. In this case a
     degenerate bfs existed before and will result after the
     pivot. If more than one odd cell in the loop equals θ,
     you may arbitrarily choose one of these odd cells to
     leave the basis; again a degenerate bfs will result
            7.5. Assignment Problems
     Example: Machineco has four jobs to be completed.
     Each machine must be assigned to complete one job.
     The time required to setup each machine for completing
     each job is shown in the table below. Machinco wants to
     minimize the total setup time needed to complete the
     four jobs.

              Setup times
      (Also called the cost matrix)
                          Time (Hours)
                 Job1     Job2   Job3    Job4
     Machine 1       14     5      8      7
     Machine 2       2     12      6      5
     Machine 3       7      8      3      9
     Machine 4       2      4      6     10

                                   The Model
 According to the setup table Machinco’s problem can be
 formulated as follows (for i,j=1,2,3,4):
      min Z  14 X 11  5 X 12  8 X 13  7 X 14  2 X 21  12 X 22  6 X 23  5 X 24
       7 X 31  8 X 32  3 X 33  9 X 34  2 X 41  X 42  6 X 43  10 X 44
      s.t. X 11  X 12  X 13  X 14  1
      X 21  X 22  X 23  X 24  1
      X 31  X 32  X 33  X 34  1
      X 41  X 42  X 43  X 44  1
      X 11  X 21  X 31  X 41  1
      X 12  X 22  X 32  X 42  1
      X 13  X 23  X 33  X 43  1
      X 14  X 24  X 34  X 44  1
      Xij  0orXij  1
     For the model on the previous page note that:

     Xij=1 if machine i is assigned to meet the demands of
     job j

     Xij=0 if machine i is not assigned to meet the demands
     of job j

     In general an assignment problem is balanced
     transportation problem in which all supplies and
     demands are equal to 1.
           The Assignment Problem
In general the LP formulation is given as
                 n     n

     Minimize    c
                i 1 j 1
                             ij   xij


                j 1
                       ij    1, i  1,   ,n   Each supply is 1


                 xij  1, j  1,
                i 1
                                           ,n   Each demand is 1

                xij  0 or 1, ij
          Comments on the Assignment
     • The Air Force has used this for assigning
       thousands of people to jobs.

     • This is a classical problem. Research on the
       assignment problem predates research on LPs.

     • Very efficient special purpose solution techniques
        – 10 years ago, Yusin Lee and J. Orlin solved a problem
          with 2 million nodes and 40 million arcs in ½ hour.

     Although the transportation simplex appears to be very
     efficient, there is a certain class of transportation
     problems, called assignment problems, for which the
     transportation simplex is often very inefficient. For that
     reason there is an other method called The Hungarian
     Method. The steps of The Hungarian Method are as
     listed below:
     Step1. Find a bfs. Find the minimum element in each row
     of the mxm cost matrix. Construct a new matrix by
     subtracting from each cost the minimum cost in its row.
     For this new matrix, find the minimum cost in each
     column. Construct a new matrix (reduced cost matrix) by
     subtracting from each cost the minimum cost in its
 Step2. Draw the minimum number of lines (horizontal
 and/or vertical) that are needed to cover all zeros in the
 reduced cost matrix. If m lines are required , an optimal
 solution is available among the covered zeros in the
 matrix. If fewer than m lines are required, proceed to step

  Step3. Find the smallest nonzero element (call its value
  k) in the reduced cost matrix that is uncovered by the
  lines drawn in step 2. Now subtract k from each
  uncovered element of the reduced cost matrix and add k
  to each element that is covered by two lines. Return to
          7.6 Transshipment Problems
     A transportation problem allows only shipments that go
     directly from supply points to demand points. In many
     situations, shipments are allowed between supply points
     or between demand points. Sometimes there may also
     be points (called transshipment points) through which
     goods can be transshipped on their journey from a
     supply point to a demand point. Fortunately, the optimal
     solution to a transshipment problem can be found by
     solving a transportation problem.

                   Transshipment Problem
     • An extension of a transportation problem
        – More general than the transportation problem in that in this
          problem there are intermediate “transshipment points”. In
          addition, shipments may be allowed between supply points
          and/or between demand points
     • LP Formulation
        – Supply point: it can send goods to another point but cannot
          receive goods from any other point
        – Demand point It can receive goods from other points but
          cannot send goods to any other point
        – Transshipment point: It can both receive goods from other
          points send goods to other points

 The following steps describe how the optimal solution to
 a transshipment problem can be found by solving a
 transportation problem.
 Step1. If necessary, add a dummy demand point (with a
 supply of 0 and a demand equal to the problem’s excess
 supply) to balance the problem. Shipments to the dummy
 and from a point to itself will be zero. Let s= total
 available supply.
 Step2. Construct a transportation tableau as follows: A
 row in the tableau will be needed for each supply point
 and transshipment point, and a column will be needed for
 each demand point and transshipment point.
  Each supply point will have a supply equal to it’s
 original supply, and each demand point will have a
 demand to its original demand. Let s= total available
 supply. Then each transshipment point will have a supply
 equal to (point’s original supply)+s and a demand equal
 to (point’s original demand)+s. This ensures that any
 transshipment point that is a net supplier will have a net
 outflow equal to point’s original supply and a net
 demander will have a net inflow equal to point’s original
 demand. Although we don’t know how much will be
 shipped through each transshipment point, we can be
 sure that the total amount will not exceed s.
                 Transshipment Example
     • Example 5: Widgetco manufactures widgets at two
       factories, one in Memphis and one in Denver. The
       Memphis factory can produce as 150 widgets, and the
       Denver factory can produce as many as 200 widgets
       per day. Widgets are shipped by air to customers in
       LA and Boston. The customers in each city require
       130 widgets per day. Because of the deregulation of
       airfares, Widgetco believes that it may be cheaper
       first fly some widgets to NY or Chicago and then fly
       them to their final destinations. The cost of flying a
       widget are shown next. Widgetco wants to minimize
       the total cost of shipping the required widgets to

         Transportation Tableau Associated
          with the Transshipment Example
     •             NY Chicago LA        Boston Dummy Supply
     •   Memphis $8       $13    $25 $28          $0      150
     •   Denver $15 $12 $26 $25                   $0      200
     •   NY         $0    $6      $16 $17          $0      350
     •   Chicago $6       $0     $14 $16          $0      350
     •   Demand 350      350     130    130       90
     •   Supply points: Memphis, Denver
     •   Demand Points: LA Boston
     •   Transshipment Points: NY, Chicago
     •   The problem can be solved using the transportation simplex
        Limitations of Transportation Problem
     • One commodity ONLY: any one product supplied
       and demanded at multiple locations
        – Merchandise
        – Electricity, water
     • Invalid for multiple commodities: (UNLESS
       transporting any one of the multiple commodities is
       completely independent of transporting any other
       commodity and hence can be treated by itself alone)
        – Example: transporting product 1 and product 2 from the
          supply points to the demand points where the total amount
          (of the two products) transported on a link is subject to a
          capacity constraint
        – Example: where economy of scale can be achieved by
          transporting the two products on the same link at a larger
          total volume and at a lower unit cost of transportation
     Limitations of Transportation Problem
     – Difficult to generalize the technique to accommodate
       (these are generic difficulty for “mathematical
       programming,” including linear and non-linear
        • Economy of scale the per-unit cost of transportation on a link
          decreasing with the volume (nonlinear and concave; there is a trick
          to convert a “non-linear program with a piecewise linear but
          convex objective function to a linear program; no such tricks exists
          for a piecewise linear but concave objective function)
        • Fixed-cost: transportation usually involves fixed charges. For
          example, the cost of truck rental (or cost of trucking in general)
          consists of a fixed charge that is independent of the mileage and a
          mileage charge that is proportional to the total mileage driven.
          Such fixed charges render the objective function NON-LINEAR
          and CONCAVE and make the problem much more difficult to
       Chapter 8
     Network Models

           Networks are Everywhere
     • Physical Networks
       –   Road Networks
       –   Railway Networks
       –   Airline traffic Networks
       –   Electrical networks, e.g., the power grid
     • Abstract networks
       – organizational charts
       – precedence relationships in projects
     • Others?
     • Networks and graphs are powerful
       modeling tools.
     • Most OR models have networks or graphs
       as a major aspect
     • Each representation has its advantages
       – Major purpose of a representation
          • efficiency in algorithms
          • ease of use

 Many important optimization problems can be analyzed
 by means of graphical or network representation. In this
 chapter the following network models will be discussed:

 1. Shortest path problems
 2. Maximum flow problems
 3. CPM-PERT project scheduling models
 4. Minimum Cost Network Flow Problems
 5. Minimum spanning tree problems

CPM/PERT are fundamental tools of project
management and are used for one of a kind, often
large and expensive, decisions such as building
docks, airports and starting a new factory. Such
decisions can be described via mathematical
models, but this is not essential. Some would
argue that CPM/PERT is not a pure OR topic.
CPM/PERT really falls into gray area that can be
claimed by fields other than OR also.

     General Comments on CPM/PERT vs. ALB
Assembly Line Balancing (ALB) are naturally not
discussed in this text, but it is important to be aware of the
huge difference between the ALB and CPM/PERT
concepts because the precedence diagrams look so similar.
Activity on node (AON) method of network precedence
diagram drawing and the ALB diagram are identical
looking at first. The ALB deals with small repetitive
items such as TV’s while CPM/PERT deals with large one
of a kind projects.

             Network Analysis and Their LP
     • Several Major Basic Classes of Network Problems
        – How to recognize and formulate them? What are the
          features they can be used to model? What are their
        – All can be formulated as an LP
        – Several important “ad-hoc” algorithms and their rationales
          will be provided
     • Most efficient transportation of goods, information
       etc. through a network
        – Transportation problem (already discussed)
        – Transshipment problem

             Network Analysis and Their LP
     • Most efficient way to go from one point to another in a
       distance network or networks representing non-distance
       phenomenon, e.g., the “cost network” representing
       production, inventory, and other costs
        – Shortest path problem:
           • Find the shortest path between two points in a network
           • Dijkstra algorithm
           • Limitations: Breakdown of the Dijkstra’s algorithm when “side
             constraints” are added
           • LP formulation
           • LP formulation to accommodate some side constraints, e.g.,
             disallowing use of two particular links in the shortest path
        – An example application to non-distance contexts:
          Minimum production and inventory cost in the context of
          dynamic programming.
         Network Analysis and Their LP
     – Maximum amount of flow from one point to
       another in a capacitated network
       • Maximum flow problem
       • The flow-augmenting algorithm
       • Limitations: breakdown of the flow-augmenting
         algorithm when “side-constraints” are added
       • LP formulation
       • LP formulation to accommodate some “side-
         constraints,” e.g., no link carrying more than 30% of the
         whole flow so as to avoid drastic reduction of flow after
         failure of any one flow-carrying link

                 8.1 Basic Definitions
     A graph or network is defined by two sets of symbols:
     • Nodes: A set of points or vertices(call it V) are called
     nodes of a graph or network.
           1                        2

     • Arcs: An arc consists of an ordered pair of vertices and
     represents a possible direction of motion that may occur
     between vertices.                   Arc

           1                        2

     • Chain: A sequence of arcs such that every arc has
     exactly one vertex in common with the previous arc is
     called a chain.
                              Common vertex
                              between two arcs

        1           2

     • Path: A path is a chain in which the terminal node of
     each arc is identical to the initial node of next arc.
     For example in the figure below (1,2)-(2,3)-(4,3) is a
     chain but not a path; (1,2)-(2,3)-(3,4) is a chain and a
     path, which represents a way to travel from node 1 to
     node 4.
          1               4

          2               3
         Essence of Dijkstra’s Shortest- Path
     • Key Points regarding the nature of the
       – In each iteration, the shortest path from the origin
         to one of the rest of the nodes is found. That is, we
         obtain one new “solved” node in each iteration.
         (More than one such path and node may be found
         in one iteration when there is a tie. There may also
         exist multiple shortest paths from the origin to
         some nodes.)
       – The algorithm stops when the shortest path to the
         destination is found

         Essence of Dijkstra’s Shortest- Path
     • General thought process involved in each
       – Let S be the current set of “solved nodes” (the set
         of nodes whose shortest paths from the origin been
         found), N be the set of all nodes, and N – S be the
         set of “unsolved nodes
          • 1. The next “solved” node should be reachable directly
            from one of the solved nodes via one direct link or arc
            (these nodes can be called neighboring nodes of the
            current solved nodes). Therefore, we consider only such
            nodes and all the links providing the access from the
            current solved nodes to these neighboring nodes (but no
            other links).     .
      Essence of Dijkstra’s Shortest- Path
     – 2. For each of these neighboring nodes, find the
       shortest path from the origin via only current
       solved nodes and the corresponding distance from
       the origin
     – 3. In general, there exist multiple such neighboring
       nodes.The shortest path to one of these nodes is
       claimed to have been found. This node is the one
       that has the shortest distance from the origin
       among these neighboring nodes being considered.
       Call this new node “solved node.”

       Algorithm for the Shortest Path Problem
     • Objective of the nth iteration: Find the nth nearest node to the
       origin (to be repeated for n = 1, 2, … until the nth nearest node is
       the destination)
     • Input for the nth Iteration: (n – 1) nearest nodes to the origin
       (solved for at the previous iterations), including their shortest
       path and distance from the origin. (These nodes plus the origin
       will be called solved nodes; the others are unsolved nodes)
     • Candidates for the nth nearest node: Each solved node that is
       directly connected by a link to one or more unsolved nodes
       provides one candidate  the unsolved node with the shortest
       connecting link (ties provide additional candidates)
     • Calculation of nth nearest node: For each solved node and its
       candidate, add the distance between them and the distance of the
       shortest path from the origin to this solved node. The candidate
       with the smallest such total distance is the nth nearest node (ties
       provide additional solved nodes), and its shortest path is the one
       generating this distance.

            The Road System for Seervada Park
     • Cars are not allowed into the park
     • There is a narrow winding road system for trams and
       for jeeps driven by the park rangers
        –   The road system is shown without curves in the next slide
        –   Location O is the entrance into the park
        –   Other letters designate the locations of the ranger stations
        –   The scenic wonder is at location T
        –   The numbers give the distance of these winding roads in
     • The park management wishes to determine which
       route from the park entrance to station T has the
       smallest total distance for the operation of the trams

     The Road System for Seervada Park

                                                     D       5
                                         4                           T
     O                       B                                   7
             4               1

     Dijkstra’s Algorithm for Shortest Path on a
         Network with Positive Arc Lengths
     • Oth iteration: Shortest distance from node O to
       Node O. S = {O}.
     • Ist iteration:
       – Step 1: Neighboring Nodes = {A, B, C}
       – Step 2: Shortest path from O to neighboring nodes
         that traverse through the current set of solved
         nodes S. Min {2, 5, 4} = 2 (corresponding to node
       – Step 3: The shortest path from O to A has been
         found with a distance of 2. S = {O, A}

     Dijkstra’s Algorithm for Shortest Path on a
         Network with Positive Arc Lengths

Solved Nodes

          O                      B


     Dijkstra’s Algorithm for Shortest Path on a
         Network with Positive Arc Lengths
     • 2nd Iteration:
         – Step 1: Neighboring nodes = {B, C, D}
         – Step 2: Min (Min (2 + 2, 5), 4, (2 + 7)) = 4.
         – Step 3: Shortest path from B and C has been
           found. S = {O, A, B, C}
     •                                                7
     Current Solved Nodes         A                           D
                            (2)               2

              (0)                                 B
79                                        4
     Dijkstra’s Algorithm for Shortest Path on a
         Network with Positive Arc Lengths
     • 3rd Iteration:
        – Step 1: Neighboring nodes = {D, E}. Only AD, BD, BE,
           and CE
        – Step 2: Min(Min(2 + 7, 4+4), Min(4 + 3, 4+4)) = 7
        – Step 3: The shortest path to E has been found S = {O, A, B,
           C, E}

     Current Solved                                              D
                                  (2)            4

           O                     (4)                             E
80                                           C
   Dijkstra’s Algorithm for Shortest Path on a
       Network with Positive Arc Lengths
  • Iteration 4
      – Step 1: Include (only) Nodes D and T. Include
        only arcs AD, BD, ED, & ET
      – Step 2: Min((min(2+7, 4+4, 7+1), (7+7))) = 8
      – Step 3: Shortest path from node O to Node D has
        been found. S = {O, A, B, C, D, E}
                   (2)                              D
     O                                          1
                    (4)   B
                                          E             T
   Current solved nodes                             7
81                  (4)                   (7)
                          C   .
     Dijkstra’s Algorithm for Shortest Path on a
         Network with Positive Arc Lengths
     • Iteration 5
        – Step 1: Include only node T and include arcs DT and ET
        – Step 2: Min(8+5, 7+7) = 13 (no other competing nodes)
        – Step 3: The shortest path from the origin to T, the
           destination, has been found, with a distance of 13

                            A   (2)
            Current solved nodes           D           5
                             B   (4)                           T
82                                           E
                                 (4)             (7)
                            C          .
     Dijkstra’s Algorithm for Shortest Path on a
         Network with Positive Arc Lengths
     • Final Solution
     • Incidentally, we have also found the nth
       nearest node from the origin sequentially
                       A       (2)
                        2                                          5
                           B                             1
         (0)       4                         3                         (13)
83                           C
          Shortest-Path Algorithm Applied to
                Seervada Park Problem
 n        Solved Nodes       Closest      Total     Nth      Minimum       Last
       Directly Connected   Connected   Distance   Nearest   Distance   Connection
       to Unsolved Nodes    Unsolved    Involved    Node
 1            O                A           2         A          2          OA

2, 3          O                C          4          C          4          OC
              A                B        2+2=4        B          4          AB
 4            A                D        2+7=9
              B                E        4+3=7        E          7          BE
              C                E        4+4=8
 5            A                D        2+7=9
              B                D        4+4=8        D          8          BD
              E                D        7+1=8        D          8          ED
 6            D                T        8+5=13       T         13          DT
              E                T        7+7=14
   LP Formulation of the Shortest Path
 • Consider the following shortest path problem
   from node 1 to node 6
 • : denotes a link
                   2                   4           2
               1                   2                       6
           3                                       2

       2           3                       5           7


   • Send one unit of flow from node 1 to node 6
       LP Formulation of the Shortest Path
     • Use flow conservation constraints
       – (Outflow from any node – inflow to that node) = 0
       – For origin = 1
       – For destination = -1
       – For all other nodes = 0
       – Let xj denote the flow along link j, j = 1, 2, .., 7, xj
         = 0 or 1
       – It turns out that this 0-1 constraints can be replaced
         by 0  xj  1, which can in turn be replaced by xj 

         LP Formulation of the Shortest Path
     • Min 4x1 + 3x2 + 3x3 + 2x4 + 3x5 + 2x6 + 2x7
     • S.t. x1 + x2                          =1
     •     -x1       + x3 + x4               =0
     •         - x2           + x5           =0
     •              - x3           + x6      =0
     •                   - x4 – x5      + x7 = 0
     •                             - x6 – x7 = -1
     • Xj  0, j = 1, 2, …, 7 (xj integers)
                 Maximum Flow Problem
     • Maximum flow problem description
       – All flow through a directed and connected network
         originates at one node (source) and terminates at one
         another node (sink)
       – All the remaining nodes are transshipment nodes
       – Flow through an arc is allowed only in the direction
         indicated by the arrowhead, where the maximum amount of
         flow is given by the capacity of that arc. At the source, all
         arcs point away from the node. At the sink, all arcs point
         into the node
       – The objective is to maximize the total amount of flow from
         the source to the sink (measured as the amount leaving the
         source or the amount entering the sink)
               Maximum Flow Problem
     • Typical applications
       – Maximize the flow through a company’s
         distribution network from its factories to its
       – Maximize the flow through a company’s supply
         network from its vendors to its factories
       – Maximize the flow of oil through a system of
       – Maximize the flow of water through a system of
       – Maximize the flow of vehicles through a
         transportation network
                     Maximum Flow Algorithm
     • Some Terminology
         – The residual network shows the remaining arc
           capacities for assigning additional flows after some
           flows have been assigned to the arcs

     The residual capacity for assigning some flow from node B to node O

            O                                               B

The residual capacity for flow from node O to Node B
                Maximum Flow Algorithm
       – An augmenting path is a directed path from the source to
         the sink in the residual network such that every arc on this
         path has strictly positive residual capacity
       – The residual capacity of the augmenting path is the
         minimum of these residual capacities (the amount of flow
         that can feasibly be added to the entire path)
     • Basic idea
       – Repeatedly select some augmenting path and add a flow
         equal to its residual capacity to that path in the original
         network. This process continues until there are no more
         augmenting paths, so that the flow from the source to the
         sink cannot be increased further

              Maximum Flow Algorithm
 • The Augmenting Path Algorithm
     – Assume that the arc capacities are either integers or rational
   • 1. identify an augmenting path by finding some
     directed path from the source to the sink in the
     residual network such that every arc on this path has
     strictly positive residual capacity. If no such path
     exists, the net flows already assigned constitute an
     optimal flow pattern
   • 2. Identify the residual capacity c* of this augmenting
     path by finding the minimum of the residual
     capacities of the arcs on this path. Increase the flow in
     this path by c*
                Maximum Flow Example
     • During the peak season the park management of the
       Seervada park would like to determine how to route
       the various tram trips from the park entrance (Station
       O) to the scenic (Station T) to maximize the number
       of trips per day. Each tram will return by the same
       route it took on the outgoing trip so the analysis
       focuses on outgoing trips only. To avoid unduly
       disturbing the ecology and wildlife of the region,
       strict upper limits have been imposed on the number
       of outgoing trips allowed per day in the outbound
       direction on each individual road. For each road the
       direction of travel for outgoing trips is indicated by
       an arrow in the next slide. The number at the base of
       the arrow gives the upper limit on the number of
       outgoing trips allowed per day.
                  Maximum Flow Example
     • Consider the problem of sending as many units from node O to
       node T for the following network (current flow, capacity):
                                                              D           (0,9)
                                             (0,4)                                  T
                                 B                                          (0,6)
                  (0,4)          (0,2)
                                                   (0,4)          E


                        Maximum Flow Example
     •   Iteration 1: one of the several augmenting paths is OBET, which has a
         residual capacity of min{7, 5, 6} = 5. By assigning the flow of 5 to this path, the
         resulting network is shown above
                                                                    D           (0,9)
                                                   (0,4)                                       T
                                       B                                          (5,6)
                        (0,4)          (0,2)
                                                         (0,4)          E


                  Maximum Flow Example
     • Iteration 2: Assign a flow of 3 to the augmenting path
       OADT. The resulting residual network is
                                                             D           (3,9)
                                            (0,4)                                  T
                                B                                          (5,6)
                  (0,4)         (0,2)
                                                  (0,4)          E


                  Maximum Flow Example
     • Iteration 3: Assign a flow of 1 to the augmenting path
       OABDT. The resulting residual network is
                                                             D           (4,9)
                                            (1,4)                                  T
                                B                                          (5,6)
                  (0,4)         (0,2)
                                                  (0,4)          E


                   Maximum Flow Example
     • Iteration 4: Assign a flow of 2 to the augmenting path OBDT. The
       resulting residual network is

                                                               D           (6,9)
                                              (3,4)                                  T
                                  B                                          (5,6)
                    (0,4)         (0,2)
                                                    (0,4)          E


                  Maximum Flow Example
     Iteration 5: Assign a flow of 1 to the augmenting path
        OCEDT. The resulting residual network is
                                                             D           (7,9)
                                            (3,4)                                  T
                                B                                          (5,6)
                  (1,4)         (0,2)
                                                  (1,4)          E


               Maximum Flow Example
  Iteration 6: Assign a flow of 1 to the augmenting path
     OCET. The resulting residual network is
                                                          D           (7,9)
                                         (3,4)                                  T
                             B                                          (6,6)
               (2,4)         (0,2)
                                               (2,4)          E


                Maximum Flow Example
  • There are no more flow augmenting paths, so the current
    flow pattern is optimal

                    A                                   7
  13                                        3       1
        O                   B                                       13
            2                               5

                    C                   2
            Maximum Flow Example
  • Recognizing optimality
  • Max-flow min-cut theorem can be useful
  • A cut is defined as any set of directed arcs
    containing at least one arc from every directed
    path from the source to the sink
  • For any particular cut, the cut value is the sum
    of the arc capacities of the arcs of the cut
  • The theorem states that, for any network with a
    single source and sink, the maximum feasible
    flow from the source to the sink equals the
    minimum cut value for all cuts of the network
      8.3 Maximum Flow Problems
 Many situations can be modeled by a network in which
 the arcs may be thought of as having a capacity that
 limits the quantity of a product that may be shipped
 through the arc. In these situations, it is often desired to
 transport the maximum amount of flow from a starting
 point (called the source) to a terminal point (called the
 sink). Such problems are called maximum flow

       An example for maximum flow problem
  Sunco Oil wants to ship the maximum possible amount
  of oil (per hour) via pipeline from node so to node si as
  shown in the figure below.
                                                    Arc     Capacity
                     a0                            (so,1)      2
              (1)3                                 (so,2)      3
  so          1              2                si   (1,2)       3
       (2)2          (1)3            (2)2
                                                   (1,3)       4
                      (1)4   3         (1)1
                                                   (3,si)      1
                                                   (2,si)      2
  The various arcs represent pipelines of different diameters. The
  maximum number of barrels of oil that can be pumped through
  each arc is shown in the table above (also called arc capacity).
      For reasons that will become clear soon, an artificial arc
      called a0 is added from the sink to the source. To
      formulate an LP about this problem first we should
      determine the decision variable.

        Xij = Millions of barrels of oil per hour that will pass
                     through arc(i,j) of pipeline.

      For a flow to be feasible it needs to be in the following
            0 <= flow through each arc <= arc capacity
               Flow into node i = Flow out from node i
      Let X0 be the flow through the artificial arc, the conservation of
      flow implies that X0 = total amount of oil entering the sink. Thus,
      Sunco’s goal is to maximize X0.

      Max Z= X0
      S.t.   Xso,1<=2          (Arc Capacity constraints)
             X0=Xso,1+Xso,2    (Node so flow constraints)
             Xso,1=X12+X13     (Node 1 flow constraints)
             Xso,2+X12=X2,si   (Node 2 flow constraints)
             X13=X3,si         (Node 3 flow constraints)
             X3,si+X2,si=X0    (Node si flow constraints)
 One optimal solution to this LP is Z=3, Xso,1=2, X13=1,
 X12=1, Xso,2=1, X3,si=1, X2,si=2, Xo=3.

       8.6 Minimum Spanning Tree
  Suppose that each arc (i,j) in a network has a length
  associated with it and that arc (i,j) represents a way of
  connecting node i to node j. For example, if each node
  in a network represents a computer in a computer
  network, arc(i,j) might represent an underground cable
  that connects computer i to computer j. In many
  applications, we want to determine the set of arcs in a
  network that connect all nodes such that the sum of the
  length of the arcs is minimized. Clearly, such a group of
  arcs contain no loop.
         Minimum Spanning Tree Problem
  • An undirected and connected network is being
    considered, where the given information includes
    some measure of the positive length (distance, cost,
    time, etc.) associated with each link
  • Both the shortest path and minimum spanning tree
    problems involve choosing a set of links that have the
    shortest total length among all sets of links that
    satisfy a certain property
      – For the shortest-path problem this property is that the
        chosen links must provide a path between the origin and
        the destination
      – For the minimum spanning tree problem, the required
        property is that the chosen links must provide a path
        between each pair of nodes

                 Some Applications
  • Design of telecommunication networks (fiber-optic
    networks, computer networks, leased-line telephone
    networks, cable television networks, etc.)
  • Design of lightly used transportation network to
    minimize the total cost of providing the links (rail
    lines, roads, etc.)
  • Design of a network of high-voltage electrical
    transmission lines
  • Design of a network of wiring on electrical
    equipment (e.g., a digital computer system) to
    minimize the total length of the wire
  • Design of a network of pipelines to connect a number
    of locations

      For a network with n nodes, a spanning tree is a group
      of n-1 arcs that connects all nodes of the network and
      contains no loops.

          1            2

                                (1,2)-(2,3)-(3,1) is a loop

                    (1,3)-(2,3) is the minimum spanning tree

        Minimum Spanning Tree Problem
  • You are given the nodes of the network but not the
    links. Instead you are given the potential links and the
    positive length for each if it is inserted into the
    network (alternative measures for length of a link
    include distance, cost, and time)
  • You wish to design the network by inserting enough
    links to satisfy the requirement that there be a path
    between every pair of nodes
  • The objective is to satisfy this requirement in a way
    that minimizes the total length of links inserted into
    the network
      Minimum Spanning Tree Algorithm
  • Greedy Algorithm
      – 1. Select any node arbitrarily, and then connect (i.e., add a
        link) to the nearest distinct node
      – 2. Identify the unconnected node that is closest to a
        connected node, and then connect these two nodes (i.e., add
        a link between them). Repeat the step until all nodes have
        been connected
      – 3. Tie breaking: Ties for the nearest distinct node (step 1)
        or the closest unconnected node (step 2) may be broken
        arbitrarily, and the algorithm will still yield an optimal
  • Fastest way of executing algorithm manually is the
    graphical approach illustrated next
      Example: The State University campus has five
      computers. The distances between computers are given
      in the figure below. What is the minimum length of
      cable required to interconnect the computers? Note that
      if two computers are not connected this is because of
      underground rock formations.
                              1           1
                      5               4
                                  2           3
                      5                   3
      Solution: We want to find the minimum spanning tree.
      • Iteration 1: Following the MST algorithm discussed
      before, we arbitrarily choose node 1 to begin. The
      closest node is node 2. Now C={1,2}, Ć={3,4,5}, and
      arc(1,2) will be in the minimum spanning tree.

                             1           1
                    5                4
                                 2           3
                     5                   3
      • Iteration 2: Node 5 is closest to C. since node 5 is two
      blocks from node 1 and node 2, we may include either
      arc(2,5) or arc(1,5) in the minimum spanning tree. We
      arbitrarily choose to include arc(2,5). Then C={1,2,5}
      and Ć={3,4}.

                               1           1
                      5                4
                                   2           3
                       5                   3
      • Iteration 3: Since node 3 is two blocks from node 5,
      we may include arc(5,3) in the minimum spanning tree.
      Now C={1,2,5,3} and Ć={4}.

                              1           1
                     5                4
                                  2           3
                      5                   3
      • Iteration 4: Node 5 is the closest node to node 4. Thus,
      we add arc(5,4) to the minimum spanning tree.
      We now have a minimum spanning tree consisting of
      arcs(1,2), (2,5), (5,3), and (5,4). The length of the
      minimum spanning tree is 1+2+2+4=9 blocks.

                              1           1
                      5               4
                                  2           3
                      5                   3
                 8.4 CPM and PERT
  Network models can be used as an aid in the scheduling of large
  complex projects that consist of many activities.

  CPM: If the duration of each activity is known with certainty, the
  Critical Path Method (CPM) can be used to determine the length
  of time required to complete a project.

  PERT: If the duration of activities is not known with certainty,
  the Program Evaluation and Review Technique (PERT) can be
  used to estimate the probability that the project will be completed
  by a given deadline.

       CPM and PERT are used in many applications
                including the following:
  • Scheduling construction projects such as office
  buildings, highways and swimming pools
  • Developing countdown and “hold” procedure for the
  launching of space crafts
  • Installing new computer systems
  • Designing and marketing new products
  • Completing corporate mergers
  • Building ships

      Project Planning, Scheduling and

• Planning: organized approach to accomplish the goal of
  minimizing elapsed time of project
      – defines objectives and tasks; represents tasks interactions on
        a network; estimates time and resources
• Scheduling: a time-phased commitment of resources
      – identifies critical tasks which, if delayed, will delay the
        project’s completion time.
• Control: means of monitoring and revising the progress
  of a project
              Network Representation
• Tasks (or activities) are represented by arcs
      – Each task has a duration denoted by tj
      – Node 0 represents the “start” and node n denotes the “finish” of
        the project
• Precedence relations are shown by “arcs”
      – specify what other tasks must be completed before the task in
        question can begin.
• A path is a sequence of linked tasks going from beginning
  to end
• Critical path is the longest path
      To apply CPM and PERT, we need a list of activities
      that make up the project. The project is considered to be
      completed when all activities have been completed. For
      each activity there is a set of activities (called the
      predecessors of the activity) that must be completed
      before the activity begins. A project network is used to
      represent the precedence relationships between
      activities. In the following discussions the activities will
      be represented by arcs and the nodes will be used to
      represent completion of a set of activities (Activity on
      arc (AOA) type of network).
                        A                       B
             1                        2                     3
           Activity A must be completed before activity B starts
      While constructing an AOA type of project diagram one
      should use the following rules:
      • Node 1 represents the start of the project. An arc should lead
      from node 1 to represent each activity that has no predecessors.
      • A node (called the finish node) representing the completion of
      the project should be included in the network.
      • Number the nodes in the network so that the node representing
      the completion time of an activity always has a larger number than
      the node representing the beginning of an activity.
      • An activity should not be represented by more than one arc in the
      • Two nodes can be connected by at most one arc.
      To avoid violating rules 4 and 5, it can be sometimes necessary to
      utilize a dummy activity that takes zero time.
      Formulating the CPM Problem

 Input Data:
   Precedence relationships and durations

 Decision Variable:
 ESi : Earliest starting times for each of the tasks

 Minimize the elapsed time of the project
 where node n is the last node in the graph
• If tj is the earliest starting time of a task, ESi is
  the earliest starting time of an immediate
  predecessor and ti is the duration of the
  immediate predecessor, then we have
              ESj  ESi + ti for every arc (i, j)

              Critical Path Definitions
      • Earliest Start Time (ES) is the earliest time a task
        can feasibly start
      • Earliest Finish Time (EF) is the earliest time a
        task can feasibly end
      • Latest Start Time (LS) is the latest time a task can
        feasibly start, without delaying the project at all.
      • Latest Finish Time (LF) is the latest time a task
        can feasibly end, without delaying the project at

               Critical Path Method
  • Forward Pass
      – Go through the jobs in order
      – Start each job at the earliest time while satisfying the
        precedence constraints
      – It finds the earliest start and finish times
      – EFi = ESi + ti
      – Earliest start time for an activity leaving a particular
        node is equal to the largest of the earliest finish times
        for all activities entering the node.

            CPM: The Backward Pass
      • Fix the finishing time
      • Look at tasks in reverse order
      • Lay out tasks one at a time on the Gantt chart
        starting at the finish and working backwards to the
      • Start the task at its latest starting time
         – LSi = LFi - ti
         – Latest finish time for an activity entering a particular
           node is equal to the smallest of the latest start times for
           all activities leaving the node.
             CPM and Critical Path
 • Theorem: The minimum length of the schedule is
   the length of the longest path.
   The longest path is called the critical path
 Look for tasks whose earliest start time and latest start time are
   the same. These tasks are critical, and are on a critical path.

           CPM and Critical Path
 • Critical path are found by identifying those tasks where
   ES=LS (equivalently, EF=LF)
 • No flexibility in scheduling tasks on the critical path
 • The makespan of the critical path equals the LF of the
   final task
 • Slack is the difference between LS and ES, or LF and EF.
   An activity with a slack of zero is on the critical path

                      An example for CPM
  Widgetco is about to introduce a new product. A list of
  activities and the precedence relationships are given in
  the table below. Draw a project diagram for this project.

           Activity              Predecessors   Duration(days)
  A:train workers                     -               6
  B:purchase raw materials           -                9
  C:produce product 1               A, B              8
  D:produce product 2               A, B              7
  E:test product 2                    D              10
  F:assemble products 1&2            C, E            12
               Project Diagram for Widgetco

                        C8               F 12
                3                   5           6
      1        Dummy
                                  E 10
          B9    2             4

               Node 1 = starting node
               Node 6 = finish node

      Project Diagram for Widgetco Forward Pass (ES,EF)
                           (9,17)                   (26,38)
                            C8                   F 12
          (0,6)    3                      5                   6
      1           Dummy
                                        E 10
          B9              (9,16)
                   2                4
                  Node 1 = starting node
                  Node 6 = finish node

           Project Diagram for Widgetco Backward Pass (LS,LF)
                             (18,26)                   (26,38)
                              C8                    F 12
          (3,9)      3                       5                   6
      1             Dummy
                                           E 10
          B9                (9,16)
                      2                4
                    Node 1 = starting node
                    Node 6 = finish node

      For Widgetco example ES(i)’s and LS(i)’s are as

            Activity         ES(i)       LS(i)
                A             0            3
                B             0            0
                C             9            18
                D             9            9
                E             16           16
                F             26           26
      According to the table on the previous slide the slacks
      are computed as follows:

      Activity B:    0
      Activity A:    3
      Activity D:   0
      Activity C:   9
      Activity E:   0
      Activity F:   0

                           Critical path

      • An activity with a slack of zero is a critical activity
      • A path from node 1 to the finish node that consists
      entirely of critical activities is called a critical path.

      For Widgetco example B-D-E-F is a critical path.

      The Makespan is equal to 38

             Using LP to find a critical path
 Decision variable:

 Xij:the time that the event corresponding to node j occurs

 Since our goal is to minimize the time required to
 complete the project, we use an objective function of:


 Note that for each activity (i,j), before j occurs , i must
 occur and activity (i,j) must be completed.
      Min Z =X6-X1
      S.T. X3>=X1+6            (Arc (1,3) constraint)
           X2>=X1+9            (Arc (1,2) constraint)
           X5>=X3+8            (Arc (3,5) constraint)
           X4>=X3+7            (Arc (3,4) constraint)
           X5>=X4+10           (Arc (4,5) constraint)
           X6>=X5+12           (Arc (5,6) constraint)
           X3>=X2              (Arc (2,3) constraint)

      The optimal solution to this LP is Z=38, X1=0, X2=9,
      X3=9, X4=16, X5=26, X6=38
                On variability of tasks
      • Consider 10 independent tasks
         – each takes 1 unit of time on average
         – the time it takes is uniformly distributed between 0
           and 2.

  CPM (uses expected value)             Modeling Randomness
141        The random schedule takes longer
          On Incorporating Variability

• Program Evaluation and Review Technique
      – Attempts to incorporate variability in the durations
      – Assume mean, m, and variance, s2, of the durations
        can be estimated
• Simulation
      – Model variability using any distribution
      – simulate to see how long a schedule will take

  CPM assumes that the duration of each activity is
  known with certainty. For many projects, this is clearly
  not applicable. PERT is an attempt to correct this
  shortcoming of CPM by modeling the duration of each
  activity as a random variable. For each activity, PERT
  requires that the project manager estimate the following
  three quantities:
  a : estimate of the activity’s duration under the most
  favorable conditions
  b : estimate of the activity’s duration under the least
  favorable conditions
  m : most likely value for the activity’s duration
  Let Tij be the duration of activity (i,j). PERT requires
  the assumption that Tij follows a beta distribution.
  According to this assumption, it can be shown that the
  mean and variance of Tij may be approximated by

                             a  4m  b
                  E (Tij ) 
                             (b  a) 2
                  var Tij 

  PERT requires the assumption that the durations of all
  activities are independent. Thus,

       E (T )
  ( i , j )path
                   ij      : expected duration of activities on any path

       var T
  ( i , j )path
                        ij : variance of duration of activities on any path

  Let CP be the random variable denoting the total
  duration of the activities on a critical path found by
  CPM. PERT assumes that the critical path found by
  CPM contains enough activities to allow us to invoke
  the Central Limit Theorem and conclude that the
  following is normally distributed:

                CP                 T       ij
                            ( i , j )criticalpath

      a, b and m for activities in Widgetco

      Activity     a        b        m
        (1,2)      5        13        9
        (1,3)      2        10        6
        (3,5)      3        13        8
        (3,4)      1        13        7
        (4,5)      8        12       10
        (5,6)      9        15       12
      According to the table on the previous slide:
                 5  13  36                 (13  5) 2
      E (T 12)               9, var T 12              1.78
                      6                          36
                 2  10  24                 (10  2) 2
      E (T 13)               6, var T 13               1.78
                      6                          36
                 3  13  32                 (13  3) 2
      E (T 35)               8, var T 35              2.78
                      6                          36
                 1  13  28                 (13  1) 2
      E (T 34)               7, var T 34             4
                      6                          36
                 8  12  40                   (12  8) 2
      E (T 45)               10, var T 45               0.44
                      6                           36
                 9  15  48                   (15  9) 2
      E (T 56)               12, var T 56              1
148                   6                           36
      Of course, the fact that arc (2,3) is a dummy arc yields

      The critical path was B-D-E-F. Thus,

      Then the standard deviation for CP is (7.22)1/2=2.69
                CP  38 35  38
P(CP  35)  P(                )  P(Z  1.12)  0.13
                 2.69    2.69
  PERT implies that there is a 13% chance that the
  project will be completed within 35 days.

        More on Project Management
      • CPM
        –   Advantage of ease of use
        –   Lays out the Gantt chart (nicely visual)
        –   Identifies the critical path
        –   Used in practice on large projects
             • e.g., used for the big dig

      • Other issues
        – Tasks take resources, which are limited
        – Task times are really random variables
151     – Unpredictable things happen
             Incorporating Resource
      • Each task can have resources that it needs
        – 3 construction workers
        – 1 crane
        – etc
      • In scheduling, do not use more resources
        than are available at any time
        – Makes the problem much more difficult to
          solve exactly. Heuristics are used.
          Dealing with the unknown
      • VERY hard to model
      • How does one model totally unforeseen
        – In the Big Dig, there is a leak in digging a
          tunnel despite assurances it would not happen
        – In the Hubble telescope, a firm assures that the
          mirrors are ground properly. By the time the
          mistake is discovered, the telescope is in outer
        Project Management Software
• Explosive growth in software packages using
  these techniques
• Cost and capabilities vary greatly
• Yearly survey in PM Network
• Microsoft Project is most commonly used
  package today
      – Free 60 day trial versions:
154     trial/info.htm     .
      • Project management
      • Simple model: we use estimates of the time
        for each task, and we use the precedence