Saturday, September 19, 2009

Syllabus: ESTM 60203

ESTM 60203: Module on Operations Research

Syllabus for Fall, 2009 

Jeffrey Kantor
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 
Mobile: 574-532-4233
Email: Kantor [dot] 1 [at] nd [dot] edu

Mission Statement

Provide students with a working knowledge of selected concepts and analytical tools of Operations Research with broad application in process operations. 

Learning Outcomes

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:
  1. 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.
  2. Formulate and solve network, transportation, and related logistics optimization problems of small to medium scale.
  3. Prepare a critical path analysis for medium scale projects, identify the critical path, find earliest finish times and latest start times.
  4. Analyze job shop and flow shop performance for deterministic conditions under common prioritization schedules, including FIFO, LIFO, EDD, and SDT.
  5. Calculate optimal schedules for job and flow shops under deterministic constraints.
  6. Formulate and solve capital allocation problem using mean/variance analysis of return and risk.
  7. Calculate optimal inventories using two-stage stochastic decision models with recourse.
  8. Prepare decision trees and solve for expected mean value, expected value of perfect information.
  9. Analyze case studies using selected tools from Operations Research.

Texts and Other Materials

Primary Texts:
  1. Operations Management 9/e by Jay Heizer and Barry Render. Published in 2008 by Pearson Education.
  2. Class presentation slides will be available on Concourse.
  3. 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
  4. IBM DeveloperWorks Series on Linear Programming (available on-line):

Supplementary Materials (available on-line at no cost):


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.

Date TopicReading MaterialsAssignment
Nov 6
(Fri., 9-10:15am)
Introduction to Linear Optimization1. 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.
Nov 16
(Mon., 4:30-6pm)
Blending ProblemsPart 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)
Nov 18
(Wed., 4:30-6pm)
Network Flow and TransportationQuantitative 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).
Nov 20
(Fri., 9-10:15am)
Project ManagementChapter 3 of Heizer & Render, pp. 55-102. Assignment 4: Case Study "Managing Hard Rock's Rockfest", Heizer and Render Video Case p. 101.
Nov 23
(Mon, 4:30-6pm)
Discrete OptimizationPart 3 of the IBM Developer Series on Linear Programming.Assignment 5: Find a solution to today's New York Times Sudoku puzzle.
Nov 30
(Mon., 4:30-6pm)
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.
Dec 2
(Wed., 4:30-6pm)
Uncertainty, Risk, and DiversificationLecture NotesAssignment 7: Prepare a solution to Problem 1 of the "Optimization Modeling Exercises" Case Study using Matlab.
Dec 7
(Mon., 4:30-6pm)
Inventory ManagementChapter 12, "Inventory Management," of Heizer and Render. pp. 481-523.
Dec 9
(Wed., 4:30-6pm)
Optimization Under Uncertainty

Quantitative Module 4, "Decision Making Tools," Heizer and Render, pp. 685-704.

Dec 17
Take-Home Final Due at Noon, 12/17