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| Tuesday, January 15, 2013
- Instructor: Dr. David R. Carey
- Email: firstname.lastname@example.org
- Phone: 570-328-1262
- Office: SLC-271
- Office Hours: By Appointment
- Class Times and Location:
- EGM-321: Thursdays 6:30PM-9:15PM Breiseth 107
- Course Description: Quantitative techniques for managerial decision-making are covered. These techniques include linear and integer programming, nonlinear programming, decision analysis, queuing theory and simulation. Problems are modeled and then solved using computer software. This course introduces students to applied principles of optimization and modeling with an emphasis on engineering applications. It is expected that during this course students will gain a number of practical skills, including model development, computer-based solution determination, and deriving implications to engineering management. Software used for this course includes Microsoft excel add-ins (various add-ins are available on accompanying book CD).
- Text: Hillier, Frederick S. and Hillier, Mark S., Introduction to Management Science 4th Ed. McGraw Hill, 2011(ISBN 9760078096600)
- Book Web Site: www.mhhe.com/hillier4e
- Course Objectives: Upon completion of this course a student should be able to:
- Construct an optimization model and compare alternatives under certainty or risk, sensitivity analysis (contrast), discuss the implication (evaluate) of solutions to the management planning process;
- Use Excel and other software to solve applied engineering problems;
- Collect data, simulate performance of a system, validate simulated decisions by way of analysis, and propose cost effective alternatives;
- Research a current trend, analyze how the trend impacts on global cultures; and propose future trends in the discipline that may be of engineering benefit ; and
- Discuss the results of an analysis effectively.
- Lecture Topics:
- Optimization with Spreadsheets using Solver
- Understand the fundamentals of Management Science decision making as they apply to engineering management and project leadership;
- Understand how to define a Problem apply a problem statement to setting up and solving a business decision model;
- How to develop a reasonable model to describe a business problem and to offer potential solutions. This normally applies to business investment and operational decisions.
- Develop ways to understand how to test and validate models.
- Understand the inherent error in the model and its meaning in the business decision process.
- Knowing when you are done; knowing when you and your team have solved the Problem statement with reasonable and verifiable solutions.
- Develop communication skills to facilitate presenting your ideas and results to engineering, business and financial management.
- Use of Case Studies to learn applications.
- Linear Programming
- Transportation and Assignment Problems
- Network Optimization Problems
- Integer, Separable, and NonLinear Programming
- Multi-Objective Optimization
- Applications of Optimization in Operations