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MGMT 295 - Management Science
Course Code:
MGMT 295


Calendar Description:
This is an introductory course in the techniques of management science. It explains with a minimum of mathematics how to formulate decision problems, how to solve them, and how to apply the solutions obtained. Fundamental management problems such as determining an optimal allocation of an organization's limited resources among competing demands are examined. The application of computers to solve management science problems is demonstrated.

Date First Offered:

Total Hours: 45
Lecture Hours: 2
Laboratory Hours: 1

Total Weeks:

This course is offered online:

MGMT 290 or MATH 104

Non-Course Pre-Requisites:


Rearticulation Submission:

Course Content:
Week Topic
1 Introduction to Management Science & Linear Programming
2 LP and Sensitivity Analysis
3 LP and Simplex Method
4 Maximization and Minimization
5 Postoptimality Analysis
6 Transportation and Assignment Problems
7 Integer Programming
8 Midterm Exam, Goal Programming
9 Decision Theory
10 Forecasting
11 Inventory Models
12 Networks, CPM and PERT
13 Queuing Models and Simulation
14 Markov Analysis and Information Systems
15 Final Exam

Learning Outcomes:
Upon successful completion of this course, the student should be able to:

1. Define management science and outline its characteristics;
2. Identify the uses and limitations of management science techniques;
3. List the steps of the scientific method, and the six-step management science process;
4. Define a system and describe its structure, differentiate between its efficiency and effectiveness;
5. Describe the components of a mathematical model, outline various types of models;
6. Explain what linear programming (LP) is, and list its components;
7. Solve LP problems graphically and interpret the;
8. Explain how sensitivity analysis can be useful to a decision maker, use this technique to evaluate a change in the value of an objective function coefficient and in the RHS of a constraint;
9. Discuss the role computers play in solving simplex problems and the relevance of manual solutions;
10. Solve maximization and minimization problems and interpret those solutions;
11. Read and interpret the solution to a dual problem, and relate the dual solution to the primal solution;
12. Use the assignment and transportation methods to solve integer problems;
13. Use graphical, and branch and bound methods to solve integer problems;
14. Explain what a goal is and how it is expressed in a goal programming model;
15. Formulate goal programming models and solve them using a graphical approach and with computer software;
16. Describe and give examples of decisions under certainty, risk, and complete uncertainty
17. Construct a payoff table, use decision trees to lay out decision alternatives;
18. Determine the expected value of perfect information;
19. Describe and use a variety of forecasting techniques to make forecasts;
20. List and briefly describe the information requirements of inventory management, and material requirements planning (MRP);
21. Use appropriate formulas to compute order quantities, when to re-order, safety stocks, service levels, and expected amounts of shortages;
22. Describe the kinds of problems that can be solved using the shortest route algorithm and use it to solve typical "network" problems;
23. Construct simple network diagrams, analyze various networks;
24. List the kinds of information that a PERT or CPM analysis can provide, use these analyses in project management problems;
25. Identify the appropriate queuing model needed to solve a problem;
26. Differentiate between analytical techniques and simulations, conduct manual and computer simulations;
27. Describe the kinds of system behaviors that Markov analysis pertains to;
28. Use a tree diagram and matrix multiplication (by computer also) to analyze system behavior;
29. Contrast the terms data and information;
30. Explain what a management information system (MIS) is and the various kinds of reports that such systems can supply to decision makers, differentiate an MIS system from a decision support system (DSS) an an expert system.

Grading System:

Grading Weight:
Final Exam: 40 %
Midterm Exam: 30 %
Assignments: 15 %
Lab Work: 15 %

Number of Assignments:

Course Offered in Other Programs:

Other Programs:
Business Management Diploma
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