Faculty
Professor Kenneth R. Baker
Objectives
This course builds on the optimization coverage in the core and provides the student with advanced modeling and optimization tools that can be useful in a variety of industries and functions. The course emphasizes the use of spreadsheets and expands the student's capabilities in using Solver.
We begin by reviewing the formulation and interpretation of linear programming models using spreadsheets and Solver. The course provides an overview of the major types of linear programs, reviewing the allocation, blending, covering, and network models featured in the core, and proceeding to general network formulations. Next, the course introduces Data Envelopment Analysis (DEA), a sophisticated linear programming approach to evaluating the efficiency of similar businesses or operating units. We look briefly at nonlinear programming for perspective on the other approaches. Then we cover the formulation and solution of integer programs, focusing on the use of binary variables and emphasizing applications in distribution, marketing and logistics. Included in the coverage are location models, traveling salesperson problems, and an optimization approach to cluster analysis. Finally, we examine evolutionary algorithms and their use in finding heuristic solutions to challenging combinatorial problems in scheduling, forecasting, and system design.
Requirements
Homework. The course schedule contains regular written homework assignments. Preparation for virtually every class, including the first, involves building models and running Solver. Strict due dates for the homework assignments will be observed. Homework assignments may be done in pairs with permission of the instructor.
Exams. There is a midterm exam and a final exam. These are open book/open notes exams, each with a time limit.
Software. We rely on Risk Solver Platform. This is an advanced Windows version of the Solver packaged with Excel and is part of the student software template for Tuck students. For more information, visit www.solver.com.
Materials
Readings. The text is Optimization Modeling with Spreadsheets by Kenneth Baker. First edition, 2006. (Duxbury Press).
Supplementary Readings
Ronald Rardin, Optimization in Operations Research, Prentice-Hall (1998).
Linus Schrage, Optimization Modeling with LINGO, Lindo Publishing (2003).
Wayne L. Winston and Munirpallam Venkataramanan, Introduction to Mathematical Programming, Brooks/Cole (2003).
Jeffrey H. Moore, Larry R. Weatherford, et al., Decision Modeling with Microsoft Excel, Prentice-Hall, 6E (2001).
Attendance
The general policies of the Tuck School apply. In part, this means that all students are expected to prepare for and attend class each day. Personal illness or family emergency, but not placement activities, are considered grounds for excused absences. Penalties for unexcused absences will be reflected in the course grade.
Grading
Homework 20%
Midterm 35%
Final 45%
Schedule
September 17
Allocation, Covering, and Blending Models
Chapter 2
Chapter 2, #3, 4 & 6
September 18
Case: Red Brand Canners
Case assignment: Reconcile the four models. What is the profit-maximizing action for Red Brand?
Handout
Chapter 2, #5
September 23
Special Network Models
Chapter 3, pages 65-83
Chapter 2, #7, 13, & 15; M2.3
September 24
Case: Hollingsworth Paper Company
Case Assignment: In retrospect, could Hollingsworth have reduced its cost last year, with a different shipping schedule? (Ignore fixed costs for the purposes of this analysis.)
Chapter 3, pages 100-103
Chapter 3, #1
September 30
General Network Models
Chapter 3, pages 83-93
Chapter 3, #2, 3, 4, & 5; M2.5
October 1
Patterns in linear programming solutions
Chapter 4
Chapter 3, #10 & 11: M2.4c
October 7
Data Envelopment Analysis (DEA)
Chapter 5
Chapter 4, #4, 5, 9 & 14; M2.7
October 8
Case: Nashville National Bank
Case Assignment: Verify the DEA model described in the case. Then develop one or two ways to improve on it, anticipating the criticisms that might be aimed at the original model.
Chapter 5, pages 180-185
Chapter 5, #5, 8 & 10; M2.4a
October 14
Nonlinear Programming
Chapter 7, pages 244-269
Chapter 5, #6; M4.5
October 15
Midterm exam
Exam
Portfolio model
October 28
Linearizations
Chapter 7, pages 269-279
Chapter 7, #11
October 29
Binary Choice Models
Chapter 6, pages 186-203
Chapter 6, #1 & 3
November 4
Integer Programming Formulations
Chapter 6, pages 203-209
Chapter 6, #5, 6 & 10
November 5
Traveling Salesperson Problem
Chapter 6, pages 209-219
Chapter 6, #7, 8, & 9
November 11
Location Models
Chapter 6, pages 220-233
Chapter 6, #14 & 15, SNE
November 12
The Evolutionary Solver
Chapter 8
Chapter 6, #11, 12, & 13: M6.8
November 18
Cluster Analysis
Chapter 8, #1, 2, 5 & 6; M6.21
November 19
Case: Colgate Wave
Case Assignment: If Wave is not introduced, what are Colgate's profit-maximizing prices? What if Wave is introduced? How should Colgate respond if Crest rolls back its price?
Chapter 8, pages 317-320
Chapter 8, #7 & 8; M6.13
November 23
Final Exam
Exam
