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Modeling and Analysis Topics
 Modeling for MSS (a critical component)
 Static and dynamic models
 Treating certainty, uncertainty, and risk
 Influence diagrams
 MSS modeling in spreadsheets
 Decision analysis of a few alternatives (with decision tables and decision trees)
 Optimization via mathematical programming
 Heuristic programming
 Simulation
 Model base management

Major Modeling Issues
 Problem identification and environmental analysis (information collection)
 Variable identification
 Influence diagrams, cognitive maps
 Forecasting/predicting
 More information leads to better prediction
 Multiple models: A MSS can include several models, each of which represents a different part of the decisionmaking problem
 Model management


Static Analysis
 Single snapshot of the situation
 Single interval
 Steady state

Dynamic Analysis
 Dynamic models
 Evaluate scenarios that change over time
 Time dependent
 Represents trends and patterns over time
 More realistic: Extends static models

Certainty Models
 Assume complete knowledge
 All potential outcomes are known
 May yield optimal solution

Uncertainty
 Several outcomes for each decision
 Probability of each outcome is unknown
 Knowledge would lead to less uncertainty

Risk analysis (probabilistic decision making)
 Probability of each of several outcomes occurring
 Level of uncertainty => Risk (expected value)

Influence Diagrams
 Graphical representations of a model “Model of a model”
 A tool for visual communication
 Some influence diagram packages create and solve the mathematical model
 Framework for expressing MSS model relationships
 Rectangle = a decision variable
 Circle = uncontrollable or intermediate variable
 Oval = result (outcome) variable: intermediate or final
 Variables are connected with arrows indicates the direction of influence (relationship)

Decision tables
 Multiple criteria decision analysis
 Features include decision variables (alternatives), uncontrollable variables, result variables
 One goal: maximize the yield after one year

Decision trees
 Graphical representation of relationships
 Multiple criteria approach
 Demonstrates complex relationships
 Cumbersome, if many alternatives exists

Mathematical Programming
A family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal

Optimal solution: The best possible solution to a modeled problem
Linear programming (LP): A mathematical model for the optimal solution of resource allocation problems. All the relationships are linear

Linear Programming Steps
 1. Identify the …
 Decision variables
 Objective function
 Objective function coefficients
 Constraints
 Capacities / Demands
 2. Represent the model
 LINDO: Write mathematical formulation
 EXCEL: Input data into specific cells in Excel
 3. Run the model and observe the results

Sensitivity
 Assesses impact of change in inputs on outputs
 Eliminates or reduces variables
 Can be automatic or trial and error

Whatif
Assesses solutions based on changes in variables or assumptions (scenario analysis)

Goal seeking
 Backwards approach, starts with goal
 Determines values of inputs needed to achieve goal
 Example is breakeven point determination

Heuristic Programming
 Cuts the search space
 Gets satisfactory solutions more quickly and less expensively
 Finds good enough feasible solutions to very complex problems
 Heuristics can be
 Quantitative
 Qualitative (in ES)

Traveling Salesman Problem
A traveling salesman must visit customers in several cities, visiting each city only once, across the country. Goal: Find the shortest possible route

When to Use Heuristics
 Inexact or limited input data
 Complex reality
 Reliable, exact algorithm not available
 Computation time excessive
 For making quick decisions

Limitations of Heuristics
Cannot guarantee an optimal solution

Modern Heuristic Methods
 Tabu search
 Intelligent search algorithm
 Genetic algorithms
 Survival of the fittest
 Simulated annealing
 Analogy to Thermodynamics

Simulation
 Technique for conducting experiments with a computer on a comprehensive model of the behavior of a system
 Frequently used in DSS tools

Major Characteristics of Simulation
 Imitates reality and capture its richness
 Technique for conducting experiments
 Descriptive, not normative tool
 Often to “solve” very complex problems
 Simulation is normally used only when a problem is too complex to be treated using numerical optimization techniques

Advantages of Simulation
 The theory is fairly straightforward
 Great deal of time compression
 Experiment with different alternatives
 The model reflects manager’s perspective
 Can handle wide variety of problem types
 Can include the real complexities of problems
 Produces important performance measures
 Often it is the only DSS modeling tool for nonstructured problems

Limitations of Simulation
 Cannot guarantee an optimal solution
 Slow and costly construction process
 Cannot transfer solutions and inferences to solve other problems (problem specific)
 So easy to explain/sell to managers, may lead overlooking analytical solutions
 Software may require special skills

Simulation Methodology Steps:
 Model real system and conduct repetitive experiments.
 1. Define problem
 2. Construct simulation model
 3. Test and validate model
 4. Design experiments
 5. Conduct experiments
 6. Evaluate results
 7. Implement solution

Stochastic vs. Deterministic Simulation
In stochastic simulations: We use distributions (Discrete or Continuous probability distributions)

Timedependent vs. Timeindependent Simulation
Time independent stochastic simulation via Monte Carlo technique (X = A + B)

Discrete event vs. Continuous simulation

Steady State vs. Transient Simulation

Simulation Implementation
 Visual simulation
 Objectoriented simulation

Visual interactive modeling (VIM) Also called
 Visual interactive problem solving
 Visual interactive modeling
 Visual interactive simulation
 Uses computer graphics to present the impact of different management decisions
 Often integrated with GIS
 Users perform sensitivity analysis
 Static or a dynamic (animation) systems

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
 Relations MBMS
 Objectoriented MBMS

