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Geospatial

Diversity <br>Federated Information Management via the Geospatial Server<br>Modeling Reality - 3D <br>Model the real world<br>Engineering users <br>Unified environment for planning, analysis, design, management<br>Openness <br>Environment based on Oracle 10g Spatial and OGC Standards<br>Multi-disciplinary projects <br>Use of best of breed tools with collaboration<br>Workflow<br>Focus on the whole enterprise<br>

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Geospatial

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  1. CHAPTER 5 Modeling and Analysis

  2. Modeling and Analysis • Major DSS component • Model base and model management • CAUTION - Difficult Topic Ahead • Familiarity with major ideas • Basic concepts and definitions • Tool--influence diagram • Model directly in spreadsheets Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  3. Modeling and Analysis • Structure of some successful models and methodologies • Decision analysis • Decision trees • Optimization • Heuristic programming • Simulation • New developments in modeling tools / techniques • Important issues in model base management Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  4. Modeling and Analysis Topics • Modeling for MSS • Static and dynamic models • Treating certainty, uncertainty, and risk • Influence diagrams • MSS modeling in spreadsheets • Decision analysis of a few alternatives (decision tables and trees) • Optimization via mathematical programming • Heuristic programming • Simulation • Multidimensional modeling -OLAP • Visual interactive modeling and visual interactive simulation • Quantitative software packages - OLAP • Model base management Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  5. Modeling for MSS • Key element in most DSS • Necessity in a model-based DSS • Can lead to massive cost reduction / revenue increases Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  6. Good Examples of MSS Models • DuPont rail system simulation model (opening vignette) • Procter & Gamble optimization supply chain restructuring models (case application 5.1) • Scott Homes AHP select a supplier model (case application 5.2) • IMERYS optimization clay production model (case application 5.3) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  7. Major Modeling Issues • Problem identification • Environmental analysis • Variable identification • Forecasting • Multiple model use • Model categories or selection (Table 5.1) • Model management • Knowledge-based modeling Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  8. Static and Dynamic Models • Static Analysis • Single snapshot • Dynamic Analysis • Dynamic models • Evaluate scenarios that change over time • Time dependent • Trends and patterns over time • Extend static models Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  9. Treating Certainty, Uncertainty, and Risk • Certainty Models • Uncertainty • Risk Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  10. Influence Diagrams • Graphical representations of a model • Model of a model • Visual communication • Some 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 connected with arrows Example (Figure 5.1) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  11. Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  12. Analytica Influence Diagram of a Marketing Problem: The Marketing Model (Figure 5.2a)(Courtesy of Lumina Decision Systems, Los Altos, CA) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  13. Analytica: Price Submodel (Figure 5.2b)(Courtesy of Lumina Decision Systems, Los Altos, CA) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  14. Analytica: Sales Submodel (Figure 5.2c)(Courtesy of Lumina Decision Systems, Los Altos, CA) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  15. MSS Modeling in Spreadsheets • Spreadsheet: most popular end-user modeling tool • Powerful functions • Add-in functions and solvers • Important for analysis, planning, modeling • Programmability (macros) (More) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  16. What-if analysis • Goal seeking • Simple database management • Seamless integration • Microsoft Excel • Lotus 1-2-3 • Excel spreadsheet static model example of a simple loan calculation of monthly payments (Figure 5.3) • Excel spreadsheet dynamic model example of a simple loan calculation of monthly payments and effects of prepayment (Figure 5.4) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  17. Decision Analysis of Few Alternatives(Decision Tables and Trees) Single Goal Situations • Decision tables • Decision trees Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  18. Decision Tables • Investment example • One goal: maximize the yield after one year • Yield depends on the status of the economy (the state of nature) • Solid growth • Stagnation • Inflation Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  19. Possible Situations 1. If solid growth in the economy, bonds yield 12%; stocks 15%; time deposits 6.5% 2. If stagnation, bonds yield 6%; stocks 3%; time deposits 6.5% 3. If inflation, bonds yield 3%; stocks lose 2%; time deposits yield 6.5% Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  20. View Problem as a Two-Person Game Payoff Table 5.2 • Decision variables (alternatives) • Uncontrollable variables (states of economy) • Result variables (projected yield) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  21. Table 5.2: Investment Problem Decision Table Model States of Nature Solid Stagnation Inflation Alternatives Growth Bonds 12% 6% 3% Stocks 15% 3% -2% CDs 6.5% 6.5% 6.5% Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  22. Treating Uncertainty • Optimistic approach • Pessimistic approach Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  23. Treating Risk • Use known probabilities (Table 5.3) • Risk analysis: compute expected values • Can be dangerous Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  24. Table 5.3: Decision Under Risk and Its Solution Solid Stagnation Inflation Expected Growth Value Alternatives .5 .3 .2 Bonds 12% 6% 3% 8.4% * Stocks 15% 3% -2% 8.0% CDs 6.5% 6.5% 6.5% 6.5% Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  25. Decision Trees • Other methods of treating risk • Simulation • Certainty factors • Fuzzy logic • Multiple goals • Yield, safety, and liquidity (Table 5.4) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  26. Table 5.4: Multiple Goals Alternatives Yield Safety Liquidity Bonds 8.4% High High Stocks 8.0% Low High CDs 6.5% Very High High Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  27. Table 5.5: Discrete vs. Continuous Probability Distribution Daily Discrete Continuous Demand Probability 5 .1 Normally distributed with 6 .15 a mean of 7 and a 7 .3 standard deviation of 1.2 8 .25 9 .2 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  28. Optimization via Mathematical Programming • Linear programming (LP) Used extensively in DSS • Mathematical Programming Family of tools to solve managerial problems in allocating scarce resources among various activities to optimize a measurable goal Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  29. LP Allocation Problem Characteristics 1. Limited quantity of economic resources 2. Resources are used in the production of products or services 3. Two or more ways (solutions, programs) to use the resources 4. Each activity (product or service) yields a return in terms of the goal 5. Allocation is usually restricted by constraints Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  30. LP Allocation Model • Rational economic assumptions 1. Returns from allocations can be compared in a common unit 2. Independent returns 3. Total return is the sum of different activities’ returns 4. All data are known with certainty 5. The resources are to be used in the most economical manner • Optimal solution: the best, found algorithmically Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  31. Linear Programming Line • Decision variables • Objective function • Objective function coefficients • Constraints • Capacities • Input-output (technology) coefficients Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  32. Lindo LP Product-Mix ModelDSS in Focus 5.4 << The Lindo Model: >> MAX 8000 X1 + 12000 X2 SUBJECT TO LABOR) 300 X1 + 500 X2 <= 200000 BUDGET) 10000 X1 + 15000 X2 <= 8000000 MARKET1) X1 >= 100 MARKET2) X2 >= 200 END Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  33. << Generated Solution Report >> LP OPTIMUM FOUND AT STEP 3 OBJECTIVE FUNCTION VALUE 1) 5066667.00 VARIABLE VALUE REDUCED COST X1 333.333300 .000000 X2 200.000000 .000000 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  34. ROW SLACK OR SURPLUS DUAL PRICES LABOR) .000000 26.666670 BUDGET) 1666667.000000 .000000 MARKET1) 233.333300 .000000 MARKET2) .000000 -1333.333000 NO. ITERATIONS= 3 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  35. RANGES IN WHICH THE BASIS IS UNCHANGED: OBJ COEFFICIENT RANGES VARIABLE CURRENT ALLOWABLE ALLOWABLE COEF INCREASE DECREASE X1 8000.000 INFINITY 799.9998 X2 12000.000 1333.333 INFINITY RIGHTHAND SIDE RANGES ROW CURRENT ALLOWABLE ALLOWABLE RHS INCREASE DECREASE LABOR 200000.000 50000.000 70000.000 BUDGET 8000000.000 INFINITY 1666667.000 MARKET1 100.000 233.333 INFINITY MARKET2 200.000 140.000 200.000 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  36. Lingo LP Product-Mix Model DSS in Focus 5.5 << The Model >>> MODEL: ! The Product-Mix Example; SETS: COMPUTERS /CC7, CC8/ : PROFIT, QUANTITY, MARKETLIM ; RESOURCES /LABOR, BUDGET/ : AVAILABLE ; RESBYCOMP(RESOURCES, COMPUTERS) : UNITCONSUMPTION ; ENDSETS DATA: PROFIT MARKETLIM = 8000, 100, 12000, 200; AVAILABLE = 200000, 8000000 ; Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  37. UNITCONSUMPTION = 300, 500, 10000, 15000 ; ENDDATA MAX = @SUM(COMPUTERS: PROFIT * QUANTITY) ; @FOR( RESOURCES( I): @SUM( COMPUTERS( J): UNITCONSUMPTION( I,J) * QUANTITY(J)) <= AVAILABLE( I)); @FOR( COMPUTERS( J): QUANTITY(J) >= MARKETLIM( J)); ! Alternative @FOR( COMPUTERS( J): @BND(MARKETLIM(J), QUANTITY(J),1000000)); Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  38. << (Partial ) Solution Report >> Global optimal solution found at step: 2 Objective value: 5066667. Variable Value Reduced Cost PROFIT( CC7) 8000.000 0.0000 PROFIT( CC8) 12000.00 0.0000 QUANTITY( CC7) 333.3333 0.0000 QUANTITY( CC8) 200.0000 0.0000 MARKETLIM( CC7) 100.0000 0.0000 MARKETLIM( CC8) 200.0000 0.0000 AVAILABLE( LABOR) 200000.0 0.0000 AVAILABLE( BUDGET) 8000000. 0.0000 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  39. UNITCONSUMPTION( LABOR, CC7) 300.00 0.00 UNITCONSUMPTION( LABOR, CC8) 500.00 0.00 UNITCONSUMPTION( BUDGET, CC7) 10000. 0.00 UNITCONSUMPTION( BUDGET, CC8) 15000. 0.00 Row Slack or Surplus Dual Price 1 5066667. 1.000000 2 0.0000000 26.66667 3 1666667. 0.0000000 4 233.3333 0.0000000 5 0.0000000 -1333.333 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  40. Heuristic Programming • Cuts the search • Gets satisfactory solutions more quickly and less expensively • Finds rules to solve complex problems • Finds good enough feasible solutions to complex problems • Heuristics can be • Quantitative • Qualitative (in ES) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  41. When to Use Heuristics 1. Inexact or limited input data 2. Complex reality 3. Reliable, exact algorithm not available 4. Computation time excessive 5. To improve the efficiency of optimization 6. To solve complex problems 7. For symbolic processing 8. For making quick decisions Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  42. Advantages of Heuristics 1. Simple to understand: easier to implement and explain 2. Help train people to be creative 3. Save formulation time 4. Save programming and storage on computers 5. Save computational time 6. Frequently produce multiple acceptable solutions 7. Possible to develop a solution quality measure 8. Can incorporate intelligent search 9. Can solve very complex models Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  43. Limitations of Heuristics 1. Cannot guarantee an optimal solution 2. There may be too many exceptions 3. Sequential decisions might not anticipate future consequences 4. Interdependencies of subsystems can influence the whole system • Heuristics successfully applied to vehicle routing Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  44. Heuristic Types • Construction • Improvement • Mathematical programming • Decomposition • Partitioning Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  45. Modern Heuristic Methods • Tabu search • Genetic algorithms • Simulated annealing Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  46. Simulation • Technique for conducting experiments with a computer on a model of a management system • Frequently used DSS tool Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  47. Major Characteristics of Simulation • Imitates reality and capture its richness • Technique for conducting experiments • Descriptive, not normative tool • Often to solve very complex, risky problems Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  48. Advantages of Simulation 1. Theory is straightforward 2. Time compression 3. Descriptive, not normative 4. MSS builder interfaces with manager to gain intimate knowledge of the problem 5. Model is built from the manager's perspective 6. Manager needs no generalized understanding. Each component represents a real problem component (More) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  49. 7. Wide variation in problem types 8. Can experiment with different variables 9. Allows for real-life problem complexities 10. Easy to obtain many performance measures directly 11. Frequently the only DSS modeling tool for nonstructured problems 12. Monte Carlo add-in spreadsheet packages (@Risk) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  50. Limitations of Simulation 1. Cannot guarantee an optimal solution 2. Slow and costly construction process 3. Cannot transfer solutions and inferences to solve other problems 4. So easy to sell to managers, may miss analytical solutions 5. Software is not so user friendly Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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