875098125
90-772 Operations Research for the Public Sector
H. John Heinz III School of Public Policy and Management
Carnegie Mellon University
Fall 2006 Course Syllabus
I.
Course Administration
Course Meetings:
Lectures: M, W 1:30 PM – 2:50 PM 1003 Hamburg Hall
Discussion: F 1:30 PM – 2:50 PM 1003 Hamburg Hall
Professor:
Michael P. Johnson
2107C Hamburg Hall
412-268-4270
[email protected]
http://www.heinz.cmu.edu/bio/faculty/johnson2.html
Office Hours: T 11 AM - Noon, F 3 – 4 PM
Teaching Assistant:
Changmi Jung
242 Hamburg Hall
412-268-1846
[email protected]
N/A
TBA
Texts:
Required:

Winston, W.L. and M. Venkataraman. 2003. Introduction to Mathematical
Programming. Operations Research: Volume One, Fourth Edition. Pacific Grove, CA:
Thompson-Brooks/Cole. Available at CMU bookstore: $120.50 (new), $90.50 (used).
Amazon.com: $121.95 (new), $35.00 (used). Companion website:
http://www.brookscole.com/cgiwadsworth/course_products_wp.pl?fid=M2b&product_isbn_issn=0534359647&disciplin
e_number=17# (you can order the book direct from the publisher for $109.76)

Course binder of readings, available from CMU bookstore, $30.
Recommended (available at CMU’s Hunt Library unless otherwise indicated):

Albright, S.C. 2001. VBA for Modelers: Developing Decision Support Systems with
Microsoft Excel. Pacific Grove, CA: Duxbury-Thompson Learning [Not available at
CMU or Pitt].

Cohon, J.L. 1978. Multiobjective Programming and Planning. New York: Academic
Press.

Daskin, M.S. 1995. Network and Discrete Location: Models, Algorithms and
Applications. New York: Wiley-Interscience.
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
Fourer, R., Gay, D.M. and B.W. Kernighan. 2003. AMPL: A Modeling Language for
Mathematical Programming, Second Edition. Pacific Grove, CA: ThompsonBrooks/Cole [Previous edition available at Hunt Library].

Hillier, F.S. and G.J. Lieberman. 1995. Introduction to Operations Research, Sixth
Edition. New York: McGraw-Hill.

Keeney, R.L. 1992. Value-Focused Thinking: A Path to Creative Decisionmaking.
Cambridge, MA: Harvard University Press.

Pollock, S.M., Rothkopf, M.H. and A. Barnett (eds.) 1994. Handbooks in Operations
Research and Management Science, Vol. 6: Operations Research and the Public Sector.
Amsterdam: North-Holland.

Taha, H.A. 2003. Operations Research: An Introduction, 7th Edition. Upper Saddle River,
NJ: Prentice-Hall [Previous edition available at Hunt Library].
Software/Internet Resources:

AMPL. 2006. AMPL: A Modeling Language for Mathematical Programming [free
student version; fee-based professional version]. World Wide Web:
http://www.ampl.com/DOWNLOADS/index.html.

Argonne National Laboratory. 2006. NEOS Server for Optimization [free suite of Webbased math programming solvers]. World Wide Web: http://www-neos.mcs.anl.gov/.

Beasley, J.L. 2006. OR-Notes. World Wide Web:
http://people.brunel.ac.uk/~mastjjb/jeb/or/contents.html.

Creative Decisions Foundation. 2006. Super Decisions [free download of Analytic
Hierarchy Process multi-criteria decision model; registration required]. World Wide Web:
http://www.superdecisions.com/.

Frontline Systems. 2006. Frontline Systems Inc.: Developers of Your Spreadsheet's
Solver [fee-based and free software downloads; registration required]. World Wide Web:
http://www.solver.com/.

Greenberg, H.J. 2006. Mathematical Programming Glossary. World Wide Web:
http://glossary.computing.society.informs.org/.

sci.op-research. 2006. Usenet newsgroups. World Wide Web: http://groupsbeta.google.com/group/sci.op-research?hl=en [OR community discussion group; access
via Google.com].

Optimization Technology Center of Northwestern University and Argonne National
Laboratory. 2005. Linear Programming Frequently Asked Questions. World Wide Web:
http://www-unix.mcs.anl.gov/otc/Guide/faq/linear-programming-faq.html.

Visual Decision, Inc. 2004. Decision Lab 2000 [free download of PROMETHEE multicriteria decision model]. World Wide Web:
http://www.visualdecision.com/download.htm.
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Course Purpose
Operations research/management science (OR/MS) is an interdisciplinary field that is:
(1) "[A] scientific approach to decision making;
(2) [C]oncerned with scientifically deciding how best to design and operate systems,
usually under conditions requiring the allocation of scarce resources" (ORSA 1990, p.
8).
OR/MS (hereafter referred to as OR) uses techniques from organizational management,
economics, finance, operations management, mathematics and computer science and other fields
to enable firms to deliver service efficiently (using as few resources as possible) and effectively
(achieving policy goals and objectives). Whereas economics may provide insight regarding
properties of certain given policies, and management information systems may assist micro-level
implementation of given policies, OR can help determine exactly what policy to pursue and the
estimated impacts associated with alternative policies.
In the public sector, OR can also enable government and nonprofit organizations deliver
service equitably (ensuring fairness between groups affected by policies). Public sector OR is
often used to solve complex problems with multiple stakeholders, multiple objectives and policy,
political or administrative restrictions on allowable actions.
This course will focus on the development, solution and interpretation of deterministic
mathematical programming models for public sector problems. A lesser emphasis will be placed
on solution algorithms for math programming models, including optimization methods and
heuristics. Very little emphasis will be placed on mathematical proofs of model or algorithm
properties.
In addition, because computer-based decision support systems (DSSs) are a primary
means by which OR solutions are delivered to end-users, we will devote a small portion of the
course to presenting the fundamentals of DSS analysis, design and implementation. We will also
discuss methods to solve problems with a relatively small number of pre-defined alternatives
incorporating complex decisionmaker preferences, and mathematical programs with multiple
objectives, referred to as multi-criteria decision models (MCDMs) and multi-objective decision
models (MODMs), respectively.
Course Goals

Develop expertise in modeling and interpreting the solutions to novel, complex problems,
especially those in the public sector;

Use and understand commercially available algorithms and implement heuristic solution
methods;

Understand policy and organizational implications of public-sector OR models.
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Course Prerequisites
Students who take this course should have experience in elementary management science
with a spreadsheet emphasis, microeconomics, management information systems, statistics and
public policy.
Administrative Details
There will be nine problem sets, a project and a final exam. Excel’s Solver is always
acceptable for computational exercises in problem sets and the project. However, other packages
such as AMPL, LINDO or LINGO may be used as well to solve mathematical programs. In
particular, mathematical programming languages such as AMPL or LINGO may make
implementation of large or complex math programs significantly easier than with Solver or
LINDO. Software packages such as Super Decisions or Decision Lab 2000 are useful for solving
multi-criteria decision models.
All publicly available PCs in the Heinz School include the Solver add-in as a feature of
Microsoft Excel (but this version is limited to 200 decision variables and 100 constraints). The
student versions of AMPL (limited to 300 decision variables and 300 constraints/objectives),
Super Decisions and Decision Lab 2000 are available on the Web (see URLs listed in
“Software/Internet Resources”, above). More advanced versions of Solver are available (for a
fee) on the Frontline Systems website, www.solver.com. The free NEOS Optimization Server
will accept math programs of arbitrary size for use with a variety of non-commercial solvers.
Sudent versions of LINGO, LINDO and Premium Solver are available on the CD-ROM
included with the Winston and Venkataraman text. The following course software are available
on cluster PCs in Hamburg Hall A103: AMPL (student version), Decision Lab 2000, LINDO
V6.1 (student version), LINGO V7.0 (student version), Premium Solver for Education and Super
Decisions.
All problem sets are due at the start of class unless otherwise specified. The project will
focus on modeling, solution and analysis of a student-defined problem in the public sector as
well as requirements analysis for a decision support system that implements the OR model. The
final exam will be pencil-and-paper only.
Collaboration between students on problem sets is acceptable and encouraged. At most
two students may collaborate on problem sets. Whenever collaboration occurs, please indicate
which student is responsible for most of a given problem.
Professor Johnson’s faculty assistant is Connie Lucas, 8-8756. Her office is 2112 HbH,
directly opposite Prof. Johnson’s office. She will have copies of the course syllabus and the
reading packet. She will also have copies of graded homeworks, project deliverables and exams
that have not been picked up.
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Course grades will be computed based on the following formula:
Homework
Project
Final Exam
35%
40%
25%
Homeworks, exams and the project may include extra credit portions.
Requests for regrades of any course material may be submitted to the professor. When
doing a regrade, the entire homework, exam or case can be examined, and the new grade could
be higher or lower than the original grade. All regrades are final.
Students are requested to make appointments for meetings with the professor outside
normal office hours.
Course Web Page:
http://blackboard.andrew.cmu.edu
After logging in, choose “F06-90772: F06-Operations Research” from the portion of the page titled
“My Courses.”
This Web page is an integrated course management system called Blackboard. All students
who are registered for 90-772 may access this page, and use a number of resources, including:

Course syllabus

Lecture notes

Homework assignments

Data for homework assignments and projects

Frequently Asked Questions

Course updates and announcements

Threaded discussion sections for all course-related concerns

Links to other Internet resources.
The Blackboard site is intended to enable students to access course information more
easily, for the professor and TAs to communicate with students more easily, and for students to
help each other master course material more easily than might be the case with a standard course
Web page. Therefore, I expect that all students will check the course Web page frequently to stay
current with course issues. Please be sure to use the on-line help to familiarize yourself with
Blackboard’s features.
All OR students have been registered for the Blackboard site, with username set to the
Andrew ID and password set to the first eight digits of the student ID. Please change the default
password as soon as possible. Please contact the professor if you have trouble logging in or if you
are dropping the course and wish to delete your OR Blackboard account.
Before sending e-mail to TAs or the professor, check the Web site and other students to see if your
questions have been already answered!
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II. Course Topics and Lecture Plan
Course Topics (27 class lectures, 3 tutorials, 1 discussion-based project presentation)

Introduction to Public-Sector OR: modeling, implementation, policy analysis and public
values (3)

Linear algebra review (1)

Linear Programming: models/applications, solution methods (4 plus two tutorial sessions)

Decision models with multiple criteria and objectives (2 plus one tutorial session)

Linear Programming: sensitivity analysis and duality (3)

Network LP models (5)

Integer programming: models, solution methods (3)

Heuristic and meta-heuristic solution methods (2)

Location models (3)

Special topics: Introduction to decision support systems (1)

Project presentations (1 discussion session)
Lecture Plan
Monday, August 28:


Lecture 1 - Course Introduction
Topics:
-
Student introductions
-
Review of syllabus
-
Introduction to public-sector OR
Reading:
- Pollock, S.M. and M.D. Maltz. 1994. “Operations Research in the Public Sector: An
Introduction and Brief History”, in (S.M. Pollock, M.H. Rothkopf and A. Barnett,
Eds.) Handbooks in Operations Research and Management Science, Vol. 6:
Operations Research and the Public Sector. Amsterdam: North-Holland, pp. 1 22.
- Winston, W.L. and M. Venkataraman. 2003. Introduction to Mathematical
Programming. Operations Research: Volume One, Fourth Edition. Pacific Grove,
CA: Thompson-Brooks/Cole, Chapter 1, pp. 1 – 10.

Problem Set #1 (due Friday, September 8):
-
Public-sector OR problem identification and description
-
Value-focused thinking
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Wednesday, August 30:


Lecture 2 - Public-Sector Implementation of OR and Modeling
Principles
Topics:
-
Modeling and policy analysis in public-sector OR
-
Use and misuse; understanding and misunderstanding of models
-
Links between operations research, economics, policy analysis and planning
Readings:
-
Gass, S.I. 1994. “Public Sector Analysis and Operations Research/Management
Science”, in (S.M. Pollock, M.H. Rothkopf and A. Barnett, Eds.) Handbooks in
Operations Research and Management Science, Vol. 6: Operations Research and the
Public Sector. Amsterdam: North-Holland, pp. 23 - 46.
-
Barnett, A. 1994. “Models Fail”, in (S.M. Pollock, M.H. Rothkopf and A. Barnett,
Eds.) Handbooks in Operations Research and Management Science, Vol. 6:
Operations Research and the Public Sector. Amsterdam: North-Holland, pp. 47 - 66.
-
Blumstein, A. 2002. Crime Modeling. Operations Research 50(1): 16 – 24.
-
Larson, R.C. 2002. Public Sector Operations Research: A Personal Journey.
Operations Research 50(1): 135 – 145.
Friday, September 1:
Discussion Session
Monday, November 4:
No Lecture (Labor Day)
Wednesday, September 6:
Lecture 3 – Value-Focused Thinking


Topics:
-
Value-focused thinking principles
-
Extensions to OR modeling
-
Case study: energy planning
Readings:
-

Keeney, R.L. 1996. Value-Focused Thinking: Identifying Decision Opportunities and
Creating Alternatives. European Journal of Operational Research 92: 537 – 549.
Problem Set #2 (due Friday, September 15):
-
Matrix manipulation
-
LP formulations
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Friday, September 8:

Problem Set #1 due
Monday, September 11:


-
Introduction to matrices
-
Formulating and solving systems of linear equations
-
Linear independence and dependence
-
Matrix inversion
Reading:
Winston and Venkataraman, Chapter 2, p. 11 – 41, 44 – 46.
Wednesday, September 13:
Formulations

Giapetto problem

Diet problem
-
Value-based thinking as an aid to formulation of LPs
-
LP Characteristics

Canonical form

Basis/basic feasible solution

Slack/surplus/artificial variables

Infeasible/optimal/unbounded solutions to LPs
Reading:
-

Winston and Venkataraman, p. 49 - 71.
Problem Set #3 (due Monday, September 25):
-
LP formulations
-
LP solutions
Friday, September 15:

Lecture 5 - Introduction to Linear Programming
Topics:
-

Lecture 4 - Linear Algebra Review
Topics:
-

Discussion Session
Tutorial – Solving Linear Programs using Software
Topics:
-
Spreadsheet solvers: Excel's Solver
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

Math programming languages: AMPL, LINDO, LINGO
Readings:
-
Winston and Venkataraman, Section 4.17, p. 202 – 210, Sections 4.9 – 4.10, p. 158 –
167.
-
Fourer, R., Gay, D.M. and B.W. Kernighan. 2003. AMPL: A Modeling Language for
Mathematical Programming, Second Edition. Pacific Grove, CA: ThompsonBrooks/Cole, p. 1 - 20.
Problem Set #2 due
Monday, September 18:


Topics:
-
Min-max/max-min formulations
-
Absolute value formulations
-
Multiple time periods
-
Piecewise-linear formulations
Reading:
-
Winston and Venkataraman, p. 72 - 113.
Wednesday, September 20:

Lecture 6 - LP Formulation Techniques
Lecture 7 - LP Applications
Topics:
-
Work Scheduling
-
Capital budgeting
-
Land use planning
Friday, September 22:
Tutorial – Solving Linear Programs using Software (continued)
Monday, September 25:
Lecture 8 – Decision Models with Multiple Criteria

Topics:
-
-
Introduction to decision rules

Multi-attribute decision models

Multi-objective decision models
Multi-Attribute Decision Models:

Additive weighting methods
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

Value/utility functions

Analytic Hierarchy process
Readings:

Cohon, J.L. 1978. Multiobjective Programming and Planning. New York: Academic
Press, p. 13 - 26, 85 - 97.

Von Winterfeldt, D. and W. Edwards. 1986. Decision Analysis and Behavioral
Research. Cambridge: Cambridge University Press, p. 259 – 313.

Saaty, T.L. 1994. How to Make a Decision: The Analytic Hierarchy Process.
Interfaces 24(6): 19 – 43.

Problem Set #3 due

Problem Set #4 (due Monday, October 2):
-
Multi-objective linear programming
-
Multi-criteria decision models
Wednesday, September 27:

Topics:
-
Multi-Attribute Decision Models (continued):

-

Concordance methods (ELECTRE, PROMETHEE)
Multi-Objective Decision Models:

Generation methods

Compromise programming
Readings:

Roy, B. 1991. The Outranking Approach and the Foundations of Electre Methods.
Theory and Decision 31: 49 – 73.

Brans, J.P. and Ph. Vincke. 1985. A Preference Ranking Organisation Method: (The
PROMETHEE Method for Multiple Criteria Decision-Making) Management
Science 31(6): 674 – 656.

Cohon, p. 98 - 126.
Friday, September 29:

Lecture 9 – Decision Models with Multiple Criteria (continued)
Tutorial – Solving MOLP and MCDM using Software
Topics:
-
Spreadsheet solvers for multiobjective LPs: Excel's Solver
-
MCDM software: Super Decisions, Decision Lab
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Monday, October 2:


Lecture 10 – Linear Programming Extensions
Topics:
-
Fractional programming and Data Envelopment Analysis
-
Motivation and illustration of Simplex Method
-
Revised Simplex Method
Readings:
-
Winston and Venkataraman, Section 6.12, p. 335 - 340.
-
Sherman, H.D. and G. Ladino. 1995. Managing Bank Productivity Using Data
Envelopment Analysis (DEA). Interfaces 25(2): 60 – 73.
-
Taha, H.A. 2003. Operations Research: An Introduction, 7th Edition. Upper Saddle
River, NJ: Prentice-Hall, p. 289 – 303.

Problem Set #4 due

Problem Set #5 (due Monday, October 16):
-
Sensitivity analysis
-
LP Duality
Wednesday, October 4:


Topics:
-
Shadow prices and right-hand side ranges
-
Reduced costs and objective function coefficient ranges
-
Sequences of parameter changes
Reading:
-

Lecture 11 - Sensitivity Analysis
Winston and Venkataraman, Chapter 5, p. 227 - 254.
Course Project (due Friday, December 8):
-
Problem description
-
Model(s)
-
Data
-
Solution method(s)
-
Decision support system requirements
Friday, October 6:
90-772 Syllabus Fall 06
Discussion Session
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Monday, October 9:


Lecture 12 – Sensitivity Analysis, Continued
Topics:
-
Changing the column of a nonbasic variable
-
New activities
-
Changing multiple parameters simultaneously (100% rule)
-
Tradeoff curves
-
Spider plots and Tornado diagrams
Readings:
- Winston and Venkataraman, Sections 6.3 – 6.4, p. 285 - 294.
- Eisenbach, T.G. 1992. Spiderplots versus Tornado Diagrams for Sensitivity Analysis.
Interfaces 22(6): 40-46.
Wednesday, October 11:
•

Lecture 13- LP Duality Theory
Topics:
-
Primal/dual equivalency
-
Economic interpretations of dual
-
Important duality theorems
-
Complementary slackness
Readings:
-
Winston and Venkataraman, Sections 6.5 – 6.7, pp. 295 - 307 (omit proofs), Section
6.10, pp. 325 - 328.
Friday, October 13:
Discussion Session
Monday, October 16:
Lecture 14 - Network Flows Introduction


Topics:
-
Nodes, arcs, flows
-
Complete graphs, bipartite graphs, trees, paths
-
Minimum spanning tree problem
Reading:

Bertsekas, D.P. 1998. Network Optimization: Continuous and Discrete Models.
Belmont, Mass.: Athena Scientific, p. 1 – 20.

Winston and Venkataraman, Section 8.1, p. 413 – 414, Section 8.6, p. 456 – 458.
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
Problem Set #5 due

Problem Set #6 (due Monday, October 30):
-
Network flows
Wednesday, October 18:


Lecture 15 - Network Flows, Continued
Topics:
-
Transportation problem
-
Multicommodity flow problem
-
Assignment problem
Readings:

Winston and Venkataraman, Section 7.1, p. 360 – 371, Section 7.5, p. 393 – 394.
Friday, October 20:
No Discussion Session (mid-semester break)
Monday, October 23:
Lecture 16 - Network Flows, Continued


Topics:
-
Transshipment problem
-
Shortest Path problem
-
Maximum Flow problem
Reading:

Winston and Venkataraman, Sections 7.6, p. 400 - 403, 8.2 - 8.3, p. 414 - 431.
Wednesday, October 25:


Lecture 17 - Network Flows, Continued
Topics:
-
PERT/CPM
-
Minimum Cost Flow problem
-
Maximum Flow problem
Reading:

Winston and Venkataraman, Sections 8.4 - 8.5, pp. 431 - 454.
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Friday, October 27:
Discussion Session
Monday, October 30:
Lecture 18 - Network Flows Applications (Rema Padman, guest
lecturer)

Problem Set #6 due
Wednesday, November 1:


Topics:

Introduction to IP

Formulation methods:
▪
If-then constraints
▪
Fixed-charges
▪
Either-or constraints
▪
Piecewise-linear functions
▪
Functions with N possible values
▪
M out of N constraints must hold
▪
Converting general integer decision variables to binary decision variables
Reading:


Lecture 19 - Integer Programming Introduction and Formulations
Winston and Venkataraman, Sections 9.1 – 9.2, p. 475 – 502, Section 9.6, p. 534 545.
Problem Set #7 (due Wednesday, November 15):

Integer programs

Combinatorial optimization problems
Friday, November 3:
Discussion Session
Monday, November 6:
No Lecture (conference travel)
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Wednesday, November 8:


Lecture 20 - Integer Programming Model Classes
Topics:
-
Pure network problems
-
Location problems
-
Knapsack problem
-
The traveling salesman problem
-
Vehicle routing/scheduling
Readings:
-
Garfinkel, R.S. 1985. “Motivation and Modeling”, in (E.L. Lawler, J.K. Lenstra,
A.H.G. Rinnooy Kan and D.B. Shmoys, Eds.) The Traveling Salesman Problem: A
Guided Tour of Combinatorial Optimization. Chichester: John Wiley and Sons, p. 17
- 36.

Christofides, N. 1985. “Vehicle Routing”, in (E.L. Lawler, J.K. Lenstra, A.H.G.
Rinnooy Kan and D.B. Shmoys, Eds.) The Traveling Salesman Problem: A Guided
Tour of Combinatorial Optimization. Chichester: John Wiley and Sons, p. 431 - 448.
Friday, November 10:
Discussion Session
Monday, November 13:
Lecture 21 – Integer Programming Model Classes (continued) and
Solving General IPs

Topics:

Model classes
▪



Machine Scheduling
Solution Methods
▪
Reformulations
▪
Branch-and-bound method
Other optimization-based strategies:

Lagrangean relaxation

Cutting planes

Column generation
Readings:

Winston and Venkataraman, Sections 9.3 – 9.5, p. 512 – 534.
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
Barnhart, C., Johnson, E.L., Nemhauser, G.L., Sigismondi, G. and P. Vance. 1993.
Formulating a Mixed-Integer Programming Problem to Improve Solvability.
Operations Research 41(6): 1013 – 1019.
Wednesday, November 15:
Lecture 22 –Heuristic and Meta-Heuristic Solution Methods (Hakan
Yildiz, Guest Lecturer)
Topics:

-
Introduction to heuristic methods
-
Local heuristics:
-

Greedy heuristics

Local improvement heuristics
Global (Meta) heuristics:

Genetic algorithms and the Evolutionary Solver
Reading:

-
Winston and Venkataraman, Sections 14.2, 14.3, p. 804 – 808; 15.1 – 15.2, pp. 823 –
835

Problem Set #7 due

Problem Set #8 (due Monday, November 27):

Heuristic and meta-heuristic solution methods
Friday, November 17:
Discussion Session
Monday, November 20:
Lecture 23 - Heuristic and Meta-Heuristic Solution Methods
(continued)
Topics:

-

Simulated annealing

Tabu search
Comparison of metaheuristic methods

Global (Meta) heuristics (continued):
Reading:
-
Winston and Venkataraman, Sections 14.3, 14.5 – 14.6, pp. 805 – 808, 815 – 821.
Wednesday, November 22:
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Friday, November 24:
No discussion (Thanksgiving break)
Monday, November 27:
Lecture 24 - Introduction to Location Models; Planar Location
Models


Topics:
-
Introduction

Location on a plane
Readings:

Fitzsimmons, J.A. and M.J. Fitzsimmons. 1998. Service Management: Operations,
Strategy, and Information Technology. Boston: McGraw-Hill, p. 161 - 187.

Problem Set #8 due

Problem Set #9 (due Wednesday, December 6):

Location on a plane

Location on networks
Wednesday, November 29:


Lecture 25 – Network Location Models
Topics:

Set Covering Problem and extensions

Center and Median Problems
Readings:

Current, J., Daskin, M. and D. Schilling. 2002. “Discrete Network Location Models”,
in (Z. Drezner and H.W. Hamacher, Eds.) Facility Location: Applications and
Theory. Berlin: Springer, p. 81 – 117.
Friday, December 1:
Discussion Session
Monday, December 4:
Lecture 26 – Network Location Models (continued)


Topics:

Center and Median Problems

Fixed Charge Facility Location Problems

Obnoxious/Undesirable Facility Location Problems
Readings:
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
Current, Daskin and Schilling, continued.

Erkut, E. and S. Neuman. 1989. Analytical Models for Locating Undesirable
Facilities. European Journal of Operational Research 40: 275 – 291.
Wednesday, December 6:



Topics:
-
Review of information system categories
-
High-level DSS architecture
-
DSS platforms
-
E-Commerce DSS applications
-
Final exam review
Reading:
-
Drudzel, M.J. and R.R. Flynn. 2000. “Decision Support Systems.” Pittsburgh:
University of Pittsburgh, School of Information Sciences and Intelligent Systems
Program.
-
Bhargarva, H.K., Sridhar, S. and C. Herrick. 1999. Beyond Spreadsheets: Tools for
Building Decision Support Systems. Computer, March 1999, pp. 31-39.
-
Zobel, C., Ragsdale, C. and L. Rees. 2000. Hands-On Learning Experience. OR/MS
Today 27(6): 28 – 31.
-
Geoffrion, A. and R. Krishnan. 2001. Prospects for Operations Research in the EBusiness Era. Interfaces, 31(2): 6 – 36.
Problem Set #9 due.
Friday, December 8:

Lecture 27 - Introduction to Decision Support Systems
Discussion Session
Project due (student presentations)
Monday, December 12 – Tuesday, December 13, Thursday, December 15 – Friday, December 16:
Heinz School Final Exams
Reference:
Operations Research Society of America (ORSA). 1990. Careers in Operations Research.
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