ESTM 60203: Module on Operations Research
Syllabus for Fall, 2009
182 Fitzpatrick Hall
Dept. of Chemical & Biomolecular Engineering
University of Notre Dame
Notre Dame, IN 46556
Office Hours: Whenever available, but best by Appointment
Voice, Text, or Voicemail: 574-699-3525
Email: Kantor [dot] 1 [at] nd [dot] edu
Provide students with a working knowledge of selected concepts and analytical tools of Operations Research with broad application in process operations.
This module consists of a nine lecture overview of selected concepts from Operations Research. The course is organized around a general theme of modeling and optimization for process operations, with the main attention on techniques for modeling and solving problems in process operations, managing complex systems of activities, and understanding the role of uncertainty in capital allocation and planning.
Students completing this module will be able to:
- Formulate, model in a mathematical programming language, and compute solutions for small to medium scale applications of linear programming in process operations, including binary and integer decision variables.
- Formulate and solve network, transportation, and related logistics optimization problems of small to medium scale.
- Prepare a critical path analysis for medium scale projects, identify the critical path, find earliest finish times and latest start times.
- Analyze job shop and flow shop performance for deterministic conditions under common prioritization schedules, including FIFO, LIFO, EDD, and SDT.
- Calculate optimal schedules for job and flow shops under deterministic constraints.
- Formulate and solve capital allocation problem using mean/variance analysis of return and risk.
- Calculate optimal inventories using two-stage stochastic decision models with recourse.
- Prepare decision trees and solve for expected mean value, expected value of perfect information.
- Analyze case studies using selected tools from Operations Research.
Texts and Other Materials
- Operations Management 9/e by Jay Heizer and Barry Render. Published in 2008 by Pearson Education.
- Class presentation slides will be available on Concourse.
- Case Studies (will be provided):
- Merton Truck Co., Harvard Business School Case Study 189163-PDF-ENG
- Australian Motors Ltd., Harvard Business School Case Study OIT23-PDF-ENG
- Optimization Modeling Exercises, Harvard Business School Case Study UV0432-PDF-ENG
Supplementary Materials (available on-line at no cost):
- Case Studies
- Operations Research. INFORMS web site to describe the benefits of OR in business applications.
- P&G's Secret Weapon: "OR Inside". Case study on the application of OR at Proctor and Gamble.
- IBM DeveloperWorks series on linear programming:
- Presentation: http://www.linuxconf.eu/2007/papers/Castro.pdf
- Lecture on Linear Programming
- Gilbert Strang: http://deimos3.apple.com/WebObjects/Core.woa/Browse/mit.edu.1303074306?i=1098364323
- Applications of Optimization with Xpress-MP by Christelle Gueret, Christian Prins, and Marc Sevaux. Translated and revised by Susanne Heipcke. Published in 2002 by Dash Optimization, ISBN 0-9543503-0-8. Available from Amazon.com or by download from the Dash Optimization web site.
- Other Links
- Barr's Course http://faculty.smu.edu/barr/models/html/links.html
- Operations Research Toolkit: http://lyle.smu.edu/~barr/ortoolkit/
A key objective of this course is to provide students with the technical skills necessary to formulate and solve classic applications in operations management. This objective is facilitated if students have individual access to software tools for implementation. Recommended software configurations:
- Matlab with Optimization Toolbox. The University maintains a site license for Matlab and a set of toolboxes for both Macintosh and PC platforms. Matlab provides a interactive computational environment for a wide range of applications.
- GLPK (GNU Linear Programming Kit). The GLPK toolkit provides command-line tools for the solution of linear and mixed integer programming problems. GLPK includes a non-proprietary language MathProg for the specification of MILP programs. Tutorials
- Excel with Solver. The Windows version of Microsoft Excel incorporates a 'solver' for small to medium scale linear and mixed integer linear programming problems. Use on a Macintosh requires installation of a separate, stand-alone Solver application available from Frontline Systems.
A grade will be assigned based on in-class participation and performance on assignments, a short project, and a take-home examination. The take-home examination will be assigned on the last class day.
The Operations Research component of the ESTEEM program will be taught in a series of ten 75 minute lectures scheduled between November 6 and December 9, 2009. In the event of rescheduling, topics will be covered in the order listed below.
|Introduction to Linear Optimization||1. Quantitative Module B of Heizer and Render, pp. 705-732.|
2. Part 1 of IBM Developer Works series on Linear Programming.
|Assignment 1: Download and install Matlab and toolboxes, Excel with Solver, and GLPK. Verify the software is working. Following lecture, prepare an analysis of for the Merton Truck Case Study. Support your analysis with a computational tool of your choice.|
|Blending Problems||Part 2 of IBM DeveloperWorks series on Linear Programming.||Assignment 2: Problem B.29 (p. 730) from Heizer & Render. Use the GMPL mathematical programming language to prepare a solution. (Due Fri., Nov 20th)|
|Network Flow and Transportation||Quantitative Module C, pp. 733-753 from Heizer & Render.||Assignment 3: Case Study "Custom Vans, Inc.", Heizer and Render, pp. 751-752. (Due Tues., Nov. 24th).|
|Project Management||Chapter 3 of Heizer & Render, pp. 55-102.||Assignment 4: Case Study "Managing Hard Rock's Rockfest", Heizer and Render Video Case p. 101.|
|Discrete Optimization||Part 3 of the IBM Developer Series on Linear Programming.||Assignment 5: Find a solution to today's New York Times Sudoku puzzle.|
|Job Shops and Flow Shops||Chapter 15, "Short-Term Scheduling," Heizer and Render.||Assignment 6: Australian Motors, Ltd. Case Study. Prepare GMPL model of the simplified AM Model. Be prepared to discuss the implementation issues raised in the case study.|
|Uncertainty, Risk, and Diversification||Lecture Notes||Assignment 7: Prepare a solution to Problem 1 of the "Optimization Modeling Exercises" Case Study using Matlab.|
|Inventory Management||Chapter 12, "Inventory Management," of Heizer and Render. pp. 481-523.|
|Optimization Under Uncertainty||Quantitative Module 4, "Decision Making Tools," Heizer and Render, pp. 685-704.|
|FINAL EXAM||Take-Home Final Due at Noon, 12/17|