# IS425Exam1ch4

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1. Modeling and Analysis Topics
• -Modeling for MSS (a critical component)
• -Static and dynamic models
• -Treating certainty, uncertainty, and risk
• -Influence diagrams
• -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
• -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
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
• -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
 Author: tttran1 ID: 204876 Card Set: IS425Exam1ch4 Updated: 2013-03-04 16:22:01 Tags: IS425Exam1ch4 Folders: Description: IS425Exam1ch4 Show Answers: