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May 27, 2024
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MG 712 - Decision Support for Operations Management3 lecture hours 0 lab hours 3 credits
Course Description
This course provides the student with the fundamentals of mathematical decision-making tools as they are used in operations management. Mathematical programming models including linear and integer programming for resource allocation and transportation models are covered. Mathematical forecasting techniques are reviewed. The student is introduced to the basics of simulation. Students need to have access to a recent version of a spreadsheet program which includes these models. (prereq: MG 610 , MG 633 , MG 645 )
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
• Understand the major model used in management
• Be able to apply these models to aid in management solutions
• Understand the limitations of the use of these models
Prerequisites by Topic
Statistics and operations
Course Topics
• Fundamental concepts and methodology for using mathematical modeling to support decision-making in operations management. The role and use of spreadsheet technology Introduction to mathematical programming and constrained optimization models. The basics of building and solving LP models. Graphical representation of solutions
• Using spreadsheets to solve LP problems. Production blending, scheduling and planning examples of using spreadsheets to solve models
• Sensitivity analysis and the interpretation of model solutions for decision-making purposes
• Network and transportation models Warehousing, transportation, and distribution problems and examples
• Integer and binary variables in LP models
• Casual forecasting: linear bivariate and multivariate techniques non-linear models
• Overview of forecasting and quantitative forecasting techniques. Time Series forecasting: averaging and exponential smoothing, trend analysis, seasonality
• Elements of queuing models: customers, servers and waiting lines. Their use in measuring and analyzing production and inventory facilities
• Simulation models random variables and random number generators functions to make decisions kinds of simulations: single event, time-oriented simulation models, event-oriented simulations models
• Replicating the simulations generating statistics generating tables optimizing values
• Decision analysis: uncertainty, risk and probability, decision trees, conditional probabilities, utility theory
Laboratory Topics
None appended
Coordinator
David Schmitz
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