ECON 4870: Advanced Operations Research

3 Credit Hours

Prerequisite: ECON 2300, 60 credit hours with a minimum GPA of 2.0, and (Admission to the Coles College Undergraduate Program or Coles College Partner Program)
This course focuses on the application of operations research techniques to decision making in business problems from a managerial perspective. A variety of advanced analytical methods is be covered, such as network optimization, nonlinear programming, goal programming, queueing analysis, and simulation. Applications in different business areas are be presented, such as production, planning, finance, scheduling, transportation, resource allocation, and distribution. Excel and Excel add-ins are used extensively to accomplish formulating and solving mathematical models and apply other quantitative techniques.

Notes: This course may be cross-leveled with ECON 7770

Course Learning Outcomes

Network Optimization:
1. Formulate network models for various types of network optimization problems.
2. Identify the characteristics of minimum cost flow problems, maximum flow problems, shortest path problems and transportation, and assignment problems, also develop and solve these problems using Excel.
3. Identify several categories of problems that are essentially special cases of minimum cost network flow problems.
4. Understand the wide application of network optimization and identify several application areas of network optimization.
Nonlinear Programming:
1. Describe the difference between nonlinear and linear programming models.
2. Recognize when a nonlinear programming model is required.
3. Formulate a nonlinear programming model.
4. Understand why nonlinear programming models are generally difficult to solve.
5. Use Excel spreadsheet to develop nonlinear programming models.
6. Use Evolutionary solver to attempt to solve some nonlinear programming models.
7. Apply separable programming technique when applicable.

Queueing Analysis:
1. Describe the elements of a queueing model.
2. Identify the characteristics of the probability distributions that are commonly used in queueing models.
3. Give examples of various types of queueing systems that are commonly encountered.
4. Identify the key measures of performance for queueing systems and the relationships between these measures.
5. Describe the main types of basic queueing models and determine which queueing model is most appropriate from a description of the queueing system being considered.
6. Apply a queueing model to determine the key measures of performance for a queueing system.
7. Describe how differences in the importance of customers can be incorporated into priority queueing models.
8. Describe some key insights that queueing models provide about how queueing systems should be designed.
9. Apply economic analysis to determine how many servers should be provided in a queueing system.
Goal Programming:
1. Identify the kinds of managerial problems that goal programming can address.
2. Describe how a goal programming model differs from other kinds of management science models.
3. Discuss the differences between the weighted goal programming approach and the preemptive goal programming approach.
4. Determine which of these approaches seems more appropriate for a given situation.
5. Formulate and apply a weighted goal programming model from a description of the problem.
6. Formulate and apply a preemptive goal programming model from a description of the problem.
Simulation:
1. Develop enhanced spreadsheet modeling techniques for use in simulation models.
2. Gain an understanding of probability and random variables in computer simulations.