EM 605 Operations Research
PURPOSE:
This memorandum provides each student the administrative details and guidance necessary
to successfully complete EM 605.
TEXT: “Introduction to Operations Research”
9th edition
Frederick S. Hillier & Gerald J. Lieberman
ISBN: 0073376299
Copyright year: 2010
Publisher: McGraw-Hill
SOFTWARE: QM for Windows & Excel add-in: Solver
COURSE DESCRIPTION:
This course will provide an understanding of history and latest development of Operations
Research (OR) tools and models. The students will be exposed to the process of system
approach to design and development of OR models. The students will also be exposed to the
formulation of requirements to data collection and software for optimization and simulation
of business processes. The course will focus on the use of tools and the development of
models from both presented homework and case studies.
COURSE OBJECTIVES:
This course will give students with the following capabilities:
• Practice an operation research (O.R.) approach to management problems
• Apply modern software packages to conduct analysis of real world data
• Apply analytical techniques and sensitivity analysis to problems and data sets
• Summarize and present the analysis results in a clear and coherent manner
GRADING:
The student is required to do homework every week of the course, and complete a
minimum of 3 case studies during the semester. If the student elects to complete additional
case studies, the highest 3 grades achieved will be used for the case study average potion
of the final grade. If the additional case studies exceed certain minimum grades, additional
points will be added to the case study average (as extra credit).
- Average of all homework assignments is 50% of final grade.
- Average of required case studies + extra credit for additional case studies is 50% of final
grade.
COURSE MAP:
Week
Week
Week
Week
Week
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Week
Week
Week
Week
Week
Week
Week
Week
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2
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4
5
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#01: Introduction to OR Modeling & Optimization, Decision
Variables, Objective Function, Constraints
#02a: Solving Linear Programming Problems – Simplex Method
#02b: Duality Theory, Sensitivity Analysis, other LP Algorithms
#03: Integer, Binary, Mixed Integer , Non-linear Programming
#04: Dynamic Programming
#05: Transportation, Transshipment, Assignment, Network Flow
#06: Meta-heuristics
#07: Game Theory
#08: Decision Analysis
#09: Markov Chains
#10: Queuing Theory
#11: Inventory Theory, Markov Decision Processes
#12: Simulation
Final Homework/Case Study Due