Dss

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MIS-745 Decision Support Technologies

School of Management, Syracuse University

Fall 00, Wed. 700 - 45

ProfessorOffice Hours

e-mail

Telephone

URL

Michel Benaroch

Wed. & Thur. 40-50 (or by appointment) room 46

mbenaroc@syr.edu

(15) 44-4/18

http//sominfo.syr.edu/facstaff/mbenaroc/courses/mis-745/syl-f0.htm

Course_Objectives Readings Grading Tentative Schedule

Team Project Group-Case Assignments Downloadable Software Links to Useful Resources

Last updated 8/5/00

• NEW …

Whats new until 8/15/0?

Course Objectives

This course studies the use of advanced technologies in managerial decision-making and business data mining. The

technologies covered include Genetic Algorithms, Neural Networks, Decision Tree Induction, Expert Systems, and

Fuzzy Logic. The course looks at how the theoretical foundations of these technologies apply to business decision

problems involving quantitative models, heuristic (symbolic) models and data-driven models, and focuses on

providing hands-on experience with commercial Excel add-ons and PC-based software tools that employ these

technologies. Attention is also given to key issues organizations face in developing and deploying DSS applications

using these technologies.

Prerequisites only basic spreadsheet skills; no programming skills are required.

By the end of this course, students will

1. understand the process of developing a DSS application;

. know the theoretical foundations of the target decision-support technologies;

. understand the practical basis for applying these technologies to business decision-making problems that

involve reasoning with quantitative models, data-driven models, and heuristic symbolic models. (Sample

problem areas include risk analysis, investment analysis, trading, economic prediction, target/direct

marketing, marketing research, learning consumer choice models, product design, production planning,

scheduling, distribution under constraints, human resource evaluation, engineering, and so on.);

4. gain hands-on experience with several commercial software tools implementing the target technologies

o Excel -- advanced spreadsheet capabilities,

o Generator genetic algorithms,

o ThinkPro, MATLAB neural networks,

o See5 decision trees induction,

o VP-Expert rule-based expert systems,

o QubiQuick, MATLAB fuzzy logic;

5. build a full-scale DSS prototype using one (or more) of the above tools.

The course achieves these objectives through class lectures, computer demonstrations, student presentation and class

discussion of case applications, homework assignments, and a group term project entailing the development of an

actual DSS prototype.

Readings

• Benaroch M., Advanced Decision Support Technologies From Spreadsheet Models to Web Mining, selected

draft chapters will be distributed online.

• Additional lecture notes, cases and articles will be distributed online.

Grading (tentative)

Assignments (individual)

Case presentation (team)

Final Project (team)

Bonus

40%

0%

40%

10%

• Case Presentation. Each group consisting of - students will present 1- cases. The presentation should be

about 0-0 minutes long. The presenting group is not required to submit a written analysis.

General suggestive case questions (try to address question 5, if possible)

1. What is the "business" problem being addressed and why is it challenging?

. Why was it necessary to use the solution approach (or advanced technology) discussed in the case?

. What are the strengths and weaknesses of the solution approach presented in the case?

4. If relevant, how "scalable" is the solution approach presented in the case to real-world setting?

5. How "costly" (time, effort, money) is the solution approach to the particular context discussed in the

case?

• Final Project. The final team project is described in the last part of the syllabus. Students are to work on the

project in their pre-assigned teams. Except for extremely unusual circumstances, no extensions will be given

past the 1/5/00 submission deadline.

• Bonus. A 10% bonus will be given to every individual who finds an "interesting" web-based DSS

application that is described at a sufficient level of detail for everybody to understand its "workings".

Academic Integrity Policy

Students are expected to abide by principles of academic honesty.

The faculty of the School of Management has adapted an Academic Integrity Policy emphasizing that

honesty, integrity, and respect for others are fundamental expectations in our School. The Policy

requires all students who take SOM courses to certify in writing that they have read, understand, and

agree to comply with the Academic Integrity Policy. SOM students will soon receive information

regarding this procedure. All non-SOM students enrolled in this course are required to complete a

certification statement available in the Office of Graduate Programs (Suite ). Completed

statements will be kept on file in the Office of Graduate Programs.

Tentative Schedule

Class Topic & Readings

1 8/7 Introduction to Decision Support Systems (DSSs)

• notes-1

• Benaroch M., Chapter 1 Decision Support Technologies, Draft version

• Benaroch M., Chapter Decision Support Framework, Draft version

• (optional) Dutta S., Wierenga B, and Dalebout A., Designing Management Support Systems Using an Integrative Perspective,

Communications of the ACM, Vol. 40, No. 6, pp. 70-7, 17.

/ Traditional decision support models and how advanced technologies fit HW #1-a Due /10

• notes-

• Benaroch M., Chapter 4 Formal Models in Decision Support, Draft version

• (case) Moynihan G.P, Purushothaman P., McLeod R.W., and Nichols W.G., DSSALM A decision support system for asset and

liability management, Decision Support Systems, Vol. , pp. -8, 00.

• (case) Recio B., Rubio F., Criado J.A., A decision support system for farm planning using AgriSupport II, Decision Support System, 6

(00) 180

/10 ----------------- ------------------------- HW #1-b Due /17

4 /17 Genetic Algorithms (GA) & (Generator) HW #-a Due /4

• notes-

• Benaroch M., Chapter 5 Evolutionary Algorithms and Applications, Draft Chapter

• (case) Vergara E.F. et al., An evolutionary algorithm for optimizing material flow in supply chains, Computers & Industrial

Engineering, Vol. 4, pp. 407-41, 00.

5 /4 --------- " -------------- & Genetic Programming (GP) HW #-b&#c Due 10/1

Project abstract due /4

• (case) Sarker R. and Newton C., A genetic algorithm for solving economic lot size scheduling problem, Computers & Industrial

Engineering, Vol. 00, pp. 000-000, 00.

6 10/1 Data Mining Overview

• notes-4-0

• (case) Daskalaki S., Kopanas I., Goudara M., Avouris N., Data mining for decision support on customer insolvency in

telecommunications business, European Journal of Operational Research, Vol. 145, pp. �55, 00.

• (case) Cooper L.G. and Giuffrida G., Turning Data Mining into a Management Science Tool New Algorithms and Empirical Results,

Management Science, 000.

7 10/8 Supervised Neural networks (NNs) & (ThinkPro, MATLAB) HW #-a Due 10/15

Data for HW #

• notes-5

• Benaroch M., Chapter 11 Supervised Neural Networks, Draft Chapter

• (case) Leigh W. et al, Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic

algorithm a case study in romantic decision support, Decision Support Systems, Vol. , pp. 6177, 00.

8 10/15 ----------------- ------------------------- HW #-b Due 10/

10/ Supervised & Unsupervised Neural networks (NNs) & (ThinkPro, MATLAB)

• notes-5-1

10 10/ Decision Tree Induction & (See5, CART) HW #4-a Due 11/5

Full project proposal due

• notes-6

• Benaroch M., Chapter 10 Decision Tree Induction, Draft Chapter

11 11/5 ----------------- ------------------------- HW #4-b Due 11/1

• (case) Kim J.W. et al., Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts,

International Journal of Electronic Commerce, Vol. 5, No. , pp. 45�6, Spring 001.

1 11/1 Expert systems (ESs) & (VP-Expert) HW #5 Due 11/1

• Notes-7

• (case) Lazarov A. and Shoval P., A rule-based system for automatic assignment of technicians to service faults, Decision Support

Systems, Vol. , pp. 460, 00.

1 11/1 Fuzzy Logic & (CubiQuick, MATLAB)

• notes-8

14 11/6 Thanksgiving no class

15 1/ Student project presentations and course summary Complete project due 1/5

Team Project

The term project is to be done in groups of -4 students. Students are free to choose the topic for the project based on their interests,

experience, majors, etc. Important mile-stones are

1. One-page project abstract due on /4/00

The abstract should include a short description of the decision problem to be addressed, explanation of why this problem

is suitable for a DSS application, in what ways is the proposed DSS supposed to be used and by who, and what decisionsupport

technologies and software package are likely to be suitable for implementation.

After the project abstract is approved, each group will work closely with the instructor to expand and refine the abstract

into a full proposal (i.e., conceptual formulation of the problem, data requirements, data collection plan, implementation

strategy, testing and validation, etc.).

. Full project proposal due on 10//00

The structure and content of the full proposal usually depends on the type of problem solved, the problem-solving

approach, and technologies used. Therefore, each group must work closely with the instructor to develop the full

proposal. However, you can get an idea of what is the full proposal might include by looking at the following outline.

. Complete project due on 1/5/00

Each group has to hand in the disk(s) containing the entire working DSS prototype, and an accompanying 10-15 pages

paper (proposal enhanced by implementation details, users' manual, etc.). For the same reason explained above, each

group will have to work with the instructor to decide on the exact structure and content of the accompanying paper. Note

since the instructor reserves the right to keep both the disk(s) and paper, each group must make sure that they keep a copy

of these.

Each group will give a 15 minutes presentation of their project, so that the other students can benefit from their experience

and insights.

Downloadable Software

Software Tools

• Generator genetic algorithms (download, unzip, and run file install.exe)

• ThinkPro neural networks (download, unzip, and run file install.exe)

• See5 decision trees induction

• VP-Expert rule-based expert systems (download, and unzip into a directory named VPX)

• QubiQuick, MATLAB fuzzy logic

Spreadsheet Files Class Examples & Assignments

• Local-Optimum demo illustrating how Excel's Solver gets stuck in a local optimum

• Optimization Examples all the optimization examples provided in class

• Worker-Task assignment assignment-type problem to run with GAs.

• Portfolio Balancing for problem in the GA homework assignment.

• Trading-Rules demo illustrating how GAs are used to find optimal trading rules

Links to Optional Readings and Useful Resources

Traditional Decision Support Systems

• (case) Forgionne G.A., Selecting rail grade crossing investments with a decision support system, Information Sciences, Vol. 144, pp. 75�

0, 00.

• (article) Plane D.R., How to Build Spreadsheet Models, MSOR Today, February, 17.

• (article) Bhargava H.K., Sridhar S., and Herrick C, Beyond Spreadsheets Tools for Building Decision Support Systems, Computer, Vol.

, No. , March, 1.

Genetic Algorithms and Genetic Programming

• (case) Suer G.A., Vazquez R., Santos J., Evolutionary programming for minimizing the average flow time in the presence of non-zero

ready times, Computers & Industrial Engineering, Vol. 45, pp. 1-44, 00

• (case) Balasubramanian R. and Bui T., GPR A Data Mining Tool Using Genetic Programming, Communications of the Association for

Information Systems, 5(6), 001.

• (case) Karuga G.G. et al., AdPalette an algorithm for customizing online advertisements on the fly, Decision Support Systems, Vol. ,

pp. 85�106, 001.

• (case) Oliver J.R., A Machine Learning Approach to Automated Negotiation and Prospects for Electronic Commerce, Working paper,

Wharton, U. of Penn, 000.

• (case) Allen F., and Karjalainen R., Using Genetic Algorithms to Find Technical Trading Rules, Journal of Financial Economics, Vol.

51, pp. 45-71, 1.

• (tutorial) Introduction to Genetic Algorithms (general overview & Java applets with good visuals)

• (tutorial) Experimenting with the parameters of Genetic Algorithms (Java Applet -- very effective)

• (tutorial) The Genetic Programming Notebook (useful introduction and links to rich sources)

Data Mining

• (case) Chakrabarti S. et al., Mining the Web's Link Structure, IEEE Computer, pp. 60-67, August 1.

• (case) Gerritsen R., Assessing Loan Risks A Data Mining Case Study, IEEE IT Pro, pp. 16-1, December, 1.

• (article) Spiliopoulou M., Web Usage Mining for Web Site Evaluation, Comm. Of the ACM, 4(8), 000.

Neural Networks Supervised Learning

• (case) An-Sing Chen, Mark T. Leung, Hazem Daouk, Application of neural networks to an emerging Financial market forecasting and

trading the Taiwan Stock Index, Computers & Operations Research, 0 (00) 01�

• (case) Montagno R. et al., Using neural networks for identifying organizational improvement strategies, European Journal of Operational

Research, Vo. 14, pp. 8-5, 00.

Neural Networks Unsupervised Learning (self-organizing maps)

• (case) Restaurant Location in Hoteling Business

• (case) Voice Recognition

Knowledge Induction (and Machine Learning)

• (case) Sorensen et al., The Decision Tree Approach to Stock Selection, Journal of Portfolio Management, 000.

• (case) Theusinger C. and Huber K.P., Analyzing the footsteps of your customers, WEBKDD'000, 000.

• (case) Fawcett T. and Provost F., Adaptive Fraud Detection, Data Mining and Knowledge Discovery, pp. 1-8, 17.

Expert and Knowledge-Based Systems

• (case) Mustafa Ozbayrak, Robert Bell, A knowledge-based decision support system for the management of parts and tools in FMS,

Decision Support Systems, Vol. 5, pp. 487-515, 00.

• (case) Lazarov A. and Shoval P., A rule-based system for automatic assignment of technicians to service faults, Decision Support

Systems, Vol. , pp. 460, 00.

Fuzzy Logic

• (case) L. Rizzi, F. Bazzana, N. Kasabov, M. Fedrizzi, L. Erzegovesi, Simulation of ECB decisions and forecast of short term Euro rate

with an adaptive fuzzy expert system, European Journal of Operational Research, Vol. 145, pp. 6-81, 00

• (case) Yuan Y. et al., The development and evaluation of a fuzzy logic expert system for renal transplantation assignment Is this a useful

tool?, European Journal of Operational Research, Vo. 14, pp. 15�17, 00.

• (case) Deinnichenko V., Bikeshera G., and Borisov A., Fuzzy Approach in Economic Modeling of Economics of Growth, in Artificial

Intelligence in Economics and Management, Ein-Dor P. (Ed.), Springer-Verlag, 16.

• Machacha L.L. and Bhattacharya P., A Fuzzy-Logic-Based Approach to Project Selection, IEEE Transactions on Engineering

Management, Vol. 47, No. 1, pp. 65-7, FEBRUARY, 000.

• Kunsch P.L. and Fortemps P., A fuzzy decision support system for the economic calculus in radioactive waste management, Information

Sciences, Vol. Xxx, pp. xxx-xxx, 00.

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