1 (chapter 5 con’t) modeling and analysis. 2 heuristic programming cuts the search gets...

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(CHAPTER 5 Con’t)

Modeling and Analysis

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Heuristic Programming

Cuts the search Gets satisfactory solutions more quickly and less

expensively Finds rules to solve complex problems Finds good enough feasible solutions to complex problems Heuristics can be

Quantitative Qualitative (in ES)

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ

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When to Use Heuristics

1. Inexact or limited input data

2. Complex reality

3. Reliable, exact algorithm not available

4. Computation time excessive

5. To improve the efficiency of optimization

6. To solve complex problems

7. For symbolic processing

8. For making quick decisions


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Advantages of Heuristics

1. Simple to understand: easier to implement and explain

2. Help train people to be creative

3. Save formulation time

4. Save programming and storage on computers

5. Save computational time

6. Frequently produce multiple acceptable solutions

7. Possible to develop a solution quality measure

8. Can incorporate intelligent search

9. Can solve very complex models


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Limitations of Heuristics

1. Cannot guarantee an optimal solution

2. There may be too many exceptions

3. Sequential decisions might not anticipate future consequences

4. Interdependencies of subsystems can influence the whole system

Heuristics successfully applied to vehicle routing


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Simulation

Technique for conducting experiments with a computer on a model of a management system

Frequently used DSS tool


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Major Characteristics of Simulation

Imitates reality and capture its richness

Technique for conducting experiments

Descriptive, not normative tool

Often to solve very complex, risky problems


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Advantages of Simulation

1. Theory is straightforward

2. Time compression

3. Descriptive, not normative

4. MSS builder interfaces with manager to gain intimate knowledge of the problem

5. Model is built from the manager's perspective

6. Manager needs no generalized understanding. Each component represents a real problem component

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7. Wide variation in problem types

8. Can experiment with different variables

9. Allows for real-life problem complexities

10. Easy to obtain many performance measures directly

11. Frequently the only DSS modeling tool for nonstructured problems

12. Monte Carlo add-in spreadsheet packages (@Risk)


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Limitations of Simulation

1. Cannot guarantee an optimal solution

2. Slow and costly construction process

3. Cannot transfer solutions and inferences to solve other problems

4. So easy to sell to managers, may miss analytical solutions

5. Software is not so user friendly


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Simulation Methodology

Model real system and conduct repetitive experiments1. Define problem

2. Construct simulation model

3. Test and validate model

4. Design experiments

5. Conduct experiments

6. Evaluate results

7. Implement solution


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Simulation Types

Probabilistic Simulation Discrete distributions Continuous distributions Probabilistic simulation via Monte Carlo technique Time dependent versus time independent simulation Simulation software Visual simulation Object-oriented simulation


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Multidimensional Modeling

Performed in online analytical processing (OLAP) From a spreadsheet and analysis perspective 2-D to 3-D to multiple-D Multidimensional modeling tools: 16-D + Multidimensional modeling - OLAP (Figure 5.6) Tool can compare, rotate, and slice and dice

corporate data across different management viewpoints


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Entire Data Cube from a Query in PowerPlay (Figure 5.6a)

(Courtesy Cognos Inc.)


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Graphical Display of the Screen in Figure 5.6a (Figure 5.6b)



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Environmental Line of Products by Drilling Down (Figure 5.6c)



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Visual Spreadsheets

User can visualize models and formulas with influence diagrams

Not cells--symbolic elements


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Visual Interactive Modeling (VIM) and Visual Interactive Simulation (VIS)

Visual interactive modeling (VIM) (DSS In Action 5.8)Also called

Visual interactive problem solving Visual interactive modeling Visual interactive simulation

Use computer graphics to present the impact of different management decisions.

Can integrate with GIS Users perform sensitivity analysis Static or a dynamic (animation) systems (Figure 5.7)


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Generated Image of Traffic at an Intersection from the Orca Visual

Simulation Environment (Figure 5.7)(Courtesy Orca Computer, Inc.)


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Visual Interactive Simulation (VIS)

Decision makers interact with the simulated model and watch the results over time

Visual interactive models and DSS VIM (Case Application W5.1 on book’s Web site) Queueing


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Quantitative Software Packages-OLAP

Preprogrammed models can expedite DSS programming time

Some models are building blocks of other models Statistical packages Management science packages Revenue (yield) management Other specific DSS applications

including spreadsheet add-ins


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Model Base Management

MBMS: capabilities similar to that of DBMS But, there are no comprehensive model base management

packages Each organization uses models somewhat differently There are many model classes Within each class there are different solution approaches Some MBMS capabilities require expertise and reasoning


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Desirable Capabilities of MBMS

Control Flexibility Feedback Interface Redundancy reduction Increased consistency


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MBMS Design Must Allow the DSS User to:

1. Access and retrieve existing models.

2. Exercise and manipulate existing models

3. Store existing models

4. Maintain existing models

5. Construct new models with reasonable effort


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Modeling languages Relational MBMS Object-oriented model base and its

management Models for database and MIS design and their

management


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SUMMARY

Models play a major role in DSS Models can be static or dynamic Analysis is under assumed certainty, risk, or

uncertainty Influence diagrams Spreadsheets Decision tables and decision trees

Spreadsheet models and results in influence diagrams Optimization: mathematical programming

(More)Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition

Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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Linear programming: economic-based Heuristic programming Simulation - more complex situations Expert Choice Multidimensional models - OLAP

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Quantitative software packages-OLAP (statistical, etc.) Visual interactive modeling (VIM) Visual interactive simulation (VIS) MBMS are like DBMS AI techniques in MBMS


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