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Chapter 4

Chapter 4 . MODELING AND ANALYSIS. Model component. Data component provides input data User interface displays solution It is the model component of a DSS that actually solves the problem – it is the heart of any DSS. Modeling Steps.

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Chapter 4

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  1. Chapter 4 MODELING AND ANALYSIS

  2. Model component • Data component provides input data • User interface displays solution • It is the model component of a DSS that actually solves the problem – it is the heart of any DSS

  3. Modeling Steps • Determine the Principle of Choice (or Result / Dependent variable) Eg. Profit • Perform Environmental Scanning & Analysis to identify all Decision / independent variables • For this, • one can use Influence diagrams (Cognitive modeling) • how did you model the car loan payment ? (assignment #2) • Identify an existing model that relate the dependent and independent variables • If needed, develop a new model from scratch • Eg. Factor analysis • Multiple models: If needed divide the problem into sub- problems and fit a model for each sub-problem • Eg. Factor analysis, followed by Regression

  4. Eg. Economy

  5. Static, Dynamic, Multi-Dimensional Models • Static models Models describing a single interval (Fig 4.2). Parameter values may be considered stable (eg. Interest rate) • Dynamic models Models whose input data are changed over time. E.g., a five-year profit or loss projection; a spreadsheet model may capture inflation, business cycle of economy; see also Fig 4.3. • Multidimensionalmodels A modeling method that involves data analysis in several dimensions

  6. Multi-dimensional modeling in Excel

  7. Multi-dimensional view (ABC Hardware, Laptop, Full warranty)=1000 units Equipment type Warranty type Vendor

  8. Model Categories • Optimization • Algorithms (Simplex in LP) • Decision Analysis • Decision-Table/Tree • Simulation • Uses experimentation, random generator • Predictive • Forecasting using regression, time-series analysis • Heuristics • Logical deduction using if-then rules (eg. Expert Systems) • This is a qualitative model • Other • What if, goal-seeking, multiple goals

  9. Optimization • Every LP problem is composed of: • Decision variables • Objective function • Constraints • Capacities

  10. Optimization • Do Exercise #7

  11. Sensitivity analysis • A study of the effect of a change in an input variable on the overall solution • By studying each variable in turn, one can identify the ‘sensitive’ variables • Helps evaluate robustness of decisions under changing conditions • Revising models to eliminate too-large sensitivities

  12. Matching model & decision environments • Certainty A condition under which it is assumed that only one result is associated with a decision (easier to model) • Uncertainty For a given decision, possible outcomes are unknown; even if known, probabilities cannot be calculated due to lack of data. (most difficult to model) Eg. Testing a new rocket / product • Risk Possible outcomes are known & data is available to calculate probabilities of occurrence of each outcome for a given decision

  13. Decision Tables under Risk/Uncertainty Choose Decision D3 since it has the largest Expected Monetary Value.

  14. DecisionTrees under risk/uncertainty

  15. Decision trees in Excel using Precision-Tree Add-in

  16. Simulation • An imitation of reality(eg. market fluctuations) • Creates random scenarios • Major characteristics • Simulation is a technique for conducting experiments • Simulation is a descriptive rather than a normative/prescriptive method • Simulation is normally used only when a problem is too unstructured to be treated using numerical optimization techniques

  17. Simulation • Advantages • A great amount of time compression can be attained • Simulation can handle an extremely wide variety of problem types (eg. queuing, inventory, market returns, product demand variations) • Simulation produces many important performance measures • Disadvantages • An optimal solution cannot be guaranteed • Simulation model construction can be a slow and costly process • Solutions and inferences from a simulation study are usually not transferable to other problems

  18. Simulation

  19. Simulation Exercise Enter this data as shown. Select cell C20. Type, =RAND(), Enter. Copy C20 all the way down to C34. Select D20. Type, VLOOKUP(C20,$C$7:$D$16,2). Copy cell D20 all the way down to D34. Select F24. Type, =Average(D20:D34). Select F25. Calculate SD.

  20. What-if, Goal-seek, Multiple goals • What-if: Similar to sensitivity analysis, but focus is on generating the revised solution when an input value is changed. • Goal-seek: Calculates the value of an input necessary to achieve a desired level of output (goal). Eg. How many hours to study to get an A? • Multiple goals: Finds a compromise solution. Eg. Group decision environments, usually based on utility analysis (Analytical Hierarchy Process-Chapter 10)

  21. Goal-seek Exercise

  22. Scenarios • A statement of assumptions about the operating environment of a particular system at a given time; a narrative description of the decision-situation setting • Scenarios are especially helpful in simulations and what-if analyses • Possible scenarios • The worst possible scenario • The best possible scenario • The most likely scenario • The average scenario Do Exercise #8

  23. Problem solving search methods • DSS uses these in the Design & Choice phases Eg. LP Eg. Chess (large RAM) Eg. Chess Eg. Med diagnosis

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