Chapter 4 Modeling and Analysis

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Aronson, and Liang 4-1

Chapter 4 Modeling and Analysis

Turban, Aronson, and Liang

Decision Support Systems and Intelligent Systems,

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Learning Objectives

• Understand basic concepts of MSS

modeling.

• Describe MSS models interaction.

• Understand different model classes.

• Structure decision making of alternatives.

• Learn to use spreadsheets in MSS

modeling.

• Understand the concepts of optimization,

simulation, and heuristics.

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Learning Objectives

• Understand the capabilities of linear

programming.

• _{Examine search methods for MSS models.}

• Determine the differences between

algorithms, blind search, heuristics.

• _{Handle multiple goals.}

• Understand terms sensitivity, automatic,

what-if analysis, goal seeking.

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Dupont Simulates Rail Transportation

System and Avoids Costly Capital

Expense Vignette

• _{Promodel simulation created}

representing entire transport system

• _{Applied what-if analyses}

• _{Visual simulation}

• Identified varying conditions

• Identified bottlenecks

• _{Allowed for downsized fleet without}

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MSS Modeling

• Key element in DSS

• _{Many classes of models}

• _{Specialized techniques for each model}

• _{Allows for rapid examination of alternative}

solutions

• _{Multiple models often included in a DSS}

• _{Trend toward transparency}

–

_{Multidimensional modeling exhibits as}

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Simulations

• _{Explore problem at hand}

• Identify alternative solutions

• Can be object-oriented

• _{Enhances decision making}

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DSS Models

• _{Algorithm-based models}

• Statistic-based models

• Linear programming models

• _{Graphical models}

• _{Quantitative models}

• _{Qualitative models}

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Problem Identification

• Environmental scanning and analysis

• Business intelligence

• Identify variables and relationships

–

Influence diagrams

–

Cognitive maps

• Forecasting

–

_{Fueled by e-commerce}

–

Increased amounts of information

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Static Models

• Single photograph of situation

• Single interval

• Time can be rolled forward, a photo at a

time

• Usually repeatable

• Steady state

–

_{Optimal operating parameters}

–

_Continuous

–

_Unvarying

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Dynamic Model

• _{Represent changing situations}

• Time dependent

• Varying conditions

• _{Generate and use trends}

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Decision-Making

• _Certainty

–

Assume complete knowledge

–

_{All potential outcomes known}

–

_{Easy to develop}

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Decision-Making

• _Uncertainty

–

Several outcomes for each decision

–

_{Probability of occurrence of each}

outcome unknown

–

Insufficient information

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Decision-Making

• _{Probabilistic Decision-Making}

–

Decision under risk

–

_{Probability of each of several possible}

outcomes occurring

–

Risk analysis

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Influence Diagrams

• _{Graphical representation of model}

• Provides relationship framework

• Examines dependencies of variables

• _{Any level of detail}

• _{Shows impact of change}

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Influence Diagrams

Decision

Intermediate

or

uncontrollable

Variables:

Result or outcome

(intermediate or

final)

Certainty

Uncertainty

Arrows indicate type of relationship and direction of influence

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Influence Diagrams

Random (risk)

Place tilde above

variable’s name

~

Demand

Sales

Preference

(double line arrow)

Graduate

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Modeling with Spreadsheets

• Flexible and easy to use

• End-user modeling tool

• Allows linear programming and

regression analysis

• Features what-if analysis, data

management, macros

• Seamless and transparent

• Incorporates both static and dynamic

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Decision Tables

• _{Multiple criteria decision analysis}

• Features include:

–

_{Decision variables (alternatives)}

–

Uncontrollable variables

–

_{Result variables}

• _{Applies principles of certainty,}

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Decision Tree

• _{Graphical representation of}

relationships

• _{Multiple criteria approach}

• _{Demonstrates complex relationships}

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MSS Mathematical Models

• Link decision variables, uncontrollable

variables, parameters, and result variables

together

–

_{Decision variables describe alternative choices.}

–

Uncontrollable variables are outside

decision-maker’s control.

–

Fixed factors are parameters.

–

Intermediate outcomes produce intermediate

result variables.

–

Result variables are dependent on chosen

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MSS Mathematical Models

• _{Nonquantitative models}

–

Symbolic relationship

–

_{Qualitative relationship}

–

_{Results based upon}

• _{Decision selected}

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Mathematical Programming

• Tools for solving managerial problems

• _{Decision-maker must allocate resources}

amongst competing activities

• Optimization of specific goals

• _{Linear programming}

–

_{Consists of decision variables, objective}

function and coefficients, uncontrollable

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Multiple Goals

• Simultaneous, often conflicting goals

sought by management

• _{Determining single measure of}

effectiveness is difficult

• _{Handling methods:}

–

_{Utility theory}

–

_{Goal programming}

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Sensitivity, What-if, and Goal

Seeking Analysis

• _Sensitivity

–

_{Assesses impact of change in inputs or parameters on}

solutions

–

_{Allows for adaptability and flexibility}

–

_{Eliminates or reduces variables}

–

_{Can be automatic or trial and error}

• _What-if

–

_{Assesses solutions based on changes in variables or}

assumptions

• _{Goal seeking}

–

_{Backwards approach, starts with goal}

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Search Approaches

• Analytical techniques (algorithms) for

structured problems

–

_{General, step-by-step search}

–

_{Obtains an optimal solution}

• Blind search

–

_{Complete enumeration}

• _{All alternatives explored}

–

Incomplete

• _{Partial search}

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Search Approaches

• Heurisitic

–

_{Repeated, step-by-step searches}

–

_{Rule-based, so used for specific situations}

–

_{“Good enough” solution, but, eventually, will}

obtain optimal goal

–

_{Examples of heuristics}

• _{Tabu search}

–

_{Remembers and directs toward higher quality choices}

• _{Genetic algorithms}

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Simulations

• _{Imitation of reality}

• _{Allows for experimentation and time compression}

• _{Descriptive, not normative}

• _{Can include complexities, but requires special skills}

• _{Handles unstructured problems}

• _{Optimal solution not guaranteed}

• _Methodology

–

_{Problem definition}

–

_{Construction of model}

–

_{Testing and validation}

–

_{Design of experiment}

–

_{Experimentation}

–

_Evaluation

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Simulations

• Probabilistic independent variables

–

Discrete or continuous distributions

• Time-dependent or time-independent

• Visual interactive modeling

–

Graphical

–

Decision-makers interact with simulated

model

–

may be used with artificial intelligence

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Model-Based Management System

• Software that allows model organization

with transparent data processing

• _Capabilities

–

_{DSS user has control}

–

_{Flexible in design}

–

_{Gives feedback}

–

_{GUI based}

–

_{Reduction of redundancy}

–

_{Increase in consistency}

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Model-Based Management System

• Relational model base management

system

–

_{Virtual file}

–

_{Virtual relationship}

• Object-oriented model base management

system

–

_{Logical independence}

• _{Database and MIS design model systems}