1. 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
  2. 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 decision-making problem
    • -Model management
  3. Categories of models
  4. Static Analysis
    • -Single snapshot of the situation
    • -Single interval
    • -Steady state
  5. Dynamic Analysis
    • -Dynamic models
    • -Evaluate scenarios that change over time
    • -Time dependent
    • -Represents trends and patterns over time
    • -More realistic: Extends static models
  6. -Certainty Models
    • -Assume complete knowledge
    • -All potential outcomes are known
    • -May yield optimal solution
  7. Uncertainty
    • -Several outcomes for each decision
    • -Probability of each outcome is unknown
    • -Knowledge would lead to less uncertainty
  8. Risk analysis (probabilistic decision making)
    • -Probability of each of several outcomes occurring
    • -Level of uncertainty => Risk (expected value)
  9. 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)
  10. Decision tables
    • -Multiple criteria decision analysis
    • -Features include decision variables (alternatives), uncontrollable variables, result variables
    • One goal: maximize the yield after one year
  11. Decision trees
    • -Graphical representation of relationships
    • -Multiple criteria approach
    • -Demonstrates complex relationships
    • -Cumbersome, if many alternatives exists
  12. 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
  13. 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
  14. 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
  15. Sensitivity
    • -Assesses impact of change in inputs on outputs
    • -Eliminates or reduces variables
    • -Can be automatic or trial and error
  16. What-if
    -Assesses solutions based on changes in variables or assumptions (scenario analysis)
  17. Goal seeking
    • -Backwards approach, starts with goal
    • -Determines values of inputs needed to achieve goal
    • -Example is break-even point determination
  18. 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)
  19. 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
  20. When to Use Heuristics
    • -Inexact or limited input data
    • -Complex reality
    • -Reliable, exact algorithm not available
    • -Computation time excessive
    • -For making quick decisions
  21. Limitations of Heuristics
    -Cannot guarantee an optimal solution
  22. Modern Heuristic Methods
    • -Tabu search
    • -Intelligent search algorithm
    • -Genetic algorithms
    • -Survival of the fittest
    • -Simulated annealing
    • -Analogy to Thermodynamics
  23. Simulation
    • -Technique for conducting experiments with a computer on a comprehensive model of the behavior of a system
    • -Frequently used in DSS tools
  24. 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
  25. 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 non-structured problems
  26. 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
  27. 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
  28. Stochastic vs. Deterministic Simulation
    -In stochastic simulations: We use distributions (Discrete or Continuous probability distributions)
  29. Time-dependent vs. Time-independent Simulation
    -Time independent stochastic simulation via Monte Carlo technique (X = A + B)
  30. Discrete event vs. Continuous simulation
  31. Steady State vs. Transient Simulation
  32. Simulation Implementation
    • -Visual simulation
    • -Object-oriented simulation
  33. 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
  34. 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
    • -Object-oriented MBMS
Card Set