Render Stair Hanna Quantitative Analysis for Management by nja90133


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									MGNT 4640 SYLLABUS, FALL 2006, PAGE 1

                MGNT 4640A (Management Science) Syllabus
                              Fall Semester 2006

Instructor: Dr. W. Kent Moore              Office: Thaxton Hall 203

Phone: 333-5991                         e-mail:

Office Hours: Mondays, 9:00 - 12:15, 1:30 - 5:30
              Tuesdays & Thursdays, 9:00 - 10:45, 3:15 - 5:30
              Wednesdays & Fridays, 9:00 - 12:15, 1:30 - 4:30

Text: Render, Stair, and Hanna, Quantitative               Analysis   for
Management, 9th edition, Prentice-Hall, 2006.

Course Description: Applications of quantitative techniques to
managerial   decisions.   Topics  include linear  programming,
decision theory, applications of probability, forecasting,
transportation problems, and simulation.

Prerequisite:     BUSA 2100 or MATH 2620, and MGNT 3250.

Course Objectives:

1. To apply major quantitative techniques in order to improve
    managerial decisions.

2. To develop analytical, critical thinking, and problem-solving
    skills in a business context.

Course Outline:

  I.   Introduction to Management Science (Sections 1.1 – 1.5)

       A.   Decision making; discipline of management science
       B.   Model building; breakeven analysis

 II.   Probability (Sections 2.1 – 2.6)

       A.   Basic probability concepts
       B.   Addition and multiplication rules
       C.   Conditional probability
       D.   Bayes Theorem

Course Outline (continued)

 III. Transportation Problems (Supplementary notes)

       A.   Steppingstone Method: improvement indexes, costs
       B.   Balanced and unbalanced problems

  IV. Linear Programming (Sections 2.1-2.3, 2.5, 8.2, 8.5, 8.8)

       A.   Characteristics and examples
       B.   Graphical method (maximization & minimization)
       C.   Slack and surplus variables; special cases
       D.   Formulation

 IV.   Forecasting (Sections 5.1 - 5.5, 4.1 - 4.4, Supplementary

       A.   Qualitative approaches
       B.   Averaging methods
       C.   Exponential smoothing
       D.   Regression and correlation
       E.   Time series with seasonal adjustments

  VI. Simulation (Sections 15.1 - 15.3, 15.7, Supplementary

       A.   Characteristics, advantages, and disadvantages
       B.   Use of random numbers
       C.   Simulation work sheets
       D.   Cost-benefit analysis

 VII. Probability Distributions and Decision Analysis
          (Sections 2.8, 2.9, 2.11, 3.1 - 3.5, Supplementary

       A.   Alternatives, states of nature, payoff tables
       B.   Decision making under uncertainty: optimistic,
            conservative, regret, & equally likely criteria
       C.   Expected value, decision making under risk
       D.   Conditional profits tables, expected profits under
       E.   Expected profit under certainty, EVPI
       F.   Exp. net marginal profit; cutoff probabilities
       G.   Normal distribution
       H.   Application of normal distribution to inventory levels

VIII. Management Science Usage (Supplementary Notes)

Testing and Grading: Each student's grade will be determined by
the number of points that he/she accumulated during the
semester. There will be a total of 450 - 500 possible points
derived from the following sources: four tests, homework, and a
final exam.   The final exam will cover topics discussed after
the previous test and other important selected topics from the
course.   At the end of the semester (and during the semester,
after each test), all point totals will be ranked from highest
to lowest. Cutoff points very close to 90%, 80%, 70%, and 60%
will be used to determine grades.

Attendance: You are expected to be present each class period
except when special hardships occur, e.g. illness.      I reserve
the right to lower your course grade by half a letter grade (5%)
if you have more than 3 absences.     More than 6 absences will
mean an automatic F. (See page 79 of the 2006-2007 VSU catalog.)

Policy Concerning Make-up Tests: Make-up tests will be given
only for very good reasons, such as illness or a death in the
family. In any case, you are expected to call me prior to the
time of the test if you must be absent on a test day. If I am
not in my office when you call, leave your name and number with
the dean's secretary (Mrs. Carolyn Shaw) and I will return your
call. Both the secretary and I can be reached at 333-5991.

Homework:   Homework will be assigned at the end of each class
period.   Homework is considered an essential learning tool and
provides excellent preparation for tests. Some assignments will
include use of the computer and will be handed in for a grade.

Withdrawals: After Friday, October 6, a student can withdraw
only for non-academic hardships, e.g. hospitalization or a death
in the family.

Students   with   Disabilities:   Students    requiring  classroom
accommodations   or   modifications   because   of   a  documented
disability should discuss this need with me at the beginning of
the semester. These students also must contact the Access Office
for Students with Disabilities and register as a student with
special needs (1115 Nevins Hall, 245-2498 or 219-1348).

                           APPROXIMATE SCHEDULE

Aug.      15   Course policies, introduction, breakeven analysis
          17   Basic probability concepts, addition rules
          22   Independent and dependent events, conditional probability
          24   Multiplication rules
          29   Bayes Theorem
          31   Bayes Theorem (continued)

Sept.      5   Transportation problems
           7   TEST 1
          12   Transportation problems (continued)
          14   Transportation problems (continued), introduction to
               linear programming
          19   Graphical linear programming
          21   Graphical linear programming (continued)
          26   Linear programming formulation
          28   TEST 2

Oct.       3 Forecasting: qualitative techniques, averaging methods
           5 Forecast error, exponential smoothing
          10 Exponential smoothing (continued), correlation
          12 Correlation (continued), regression
          19 Regression (continued)
          24 TEST 3
          26 Regression (continued), time series analysis with seasonal
          31 Simulation

Nov.       2   Simulation (continued)
           7   Decision making under uncertainty
           9   Expected value, conditional profits tables
          14   TEST 4
          16   Expected profits under risk, expected profits under
               certainty, EVPI
          21   Expected net marginal profits, cutoff probabilities
          28   Normal distribution, application to inventories
          30   Management science usage, review
Dec.       8   FINAL EXAM, 10:15 – 12:15 (Friday)

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