1 SEGMENT 3 Modeling and Analysis

2 n Major DSS component n Model base and model management n CAUTION - Difficult Topic Ahead –Familiarity with major ideas –Basic concepts and definitions –Tool--influence diagram –Model directly in spreadsheets

3 n Structure of some successful models and methodologies –Decision analysis –Decision trees –Optimization –Heuristic programming –Simulation n New developments in modeling tools / techniques n Important issues in model base management Modeling and Analysis

4 Modeling and Analysis Topics n Modeling for MSS n Static and dynamic models n Treating certainty, uncertainty, and risk n Influence diagrams n MSS modeling in spreadsheets n Decision analysis of a few alternatives (decision tables and trees) n Optimization via mathematical programming n Heuristic programming n Simulation n Multidimensional modeling -OLAP n Visual interactive modeling and visual interactive simulation n Quantitative software packages - OLAP n Model base management

5 Modeling for MSS n Key element in most DSS n Necessity in a model-based DSS n Can lead to massive cost reduction / revenue increases

6 Good Examples of MSS Models n rail system simulation model n optimization supply chain restructuring models n AHP select a supplier model n optimization clay production model

7 Major Modeling Issues n Problem identification n Environmental analysis n Variable identification n Forecasting n Multiple model use n Model categories or selection n Model management n Knowledge-based modeling

8 Static and Dynamic Models n Static Analysis –Single snapshot n Dynamic Analysis –Dynamic models –Evaluate scenarios that change over time –Time dependent –Trends and patterns over time –Extend static models

9 Treating Certainty, Uncertainty, and Risk n Certainty Models n Uncertainty n Risk

10 Influence Diagrams n Graphical representations of a model n Model of a model n Visual communication n Some packages create and solve the mathematical model n 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

11 MSS Modeling in Spreadsheets n Spreadsheet: most popular end-user modeling tool n Powerful functions n Add-in functions and solvers n Important for analysis, planning, modeling n Programmability (macros) (More)

12 n What-if analysis n Goal seeking n Simple database management n Seamless integration n Microsoft Excel n Lotus 1-2-3

13 Decision Analysis of Few Alternatives (Decision Tables and Trees) Single Goal Situations n Decision tables n Decision trees

14 Decision Tables n Investment example n One goal: maximize the yield after one year n Yield depends on the status of the economy (the state of nature) –Solid growth –Stagnation –Inflation

15 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% Possible Situations

16 Treating Risk n Use known probabilities n Risk analysis: compute expected values n Can be dangerous

17 n Decision Trees n Other methods of treating risk –Simulation –Certainty factors –Fuzzy logic n Multiple goals n Yield, safety, and liquidity

18 Multiple Goals AlternativesYieldSafetyLiquidity Bonds8.4%HighHigh Stocks8.0%Low High CDs6.5%Very High High

19 Optimization via Mathematical Programming n Linear programming (LP) Used extensively in DSS n Mathematical Programming Family of tools to solve managerial problems in allocating scarce resources among various activities to optimize a measurable goal

20 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

21 LP Allocation Model n 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 n Optimal solution: the best, found algorithmically

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

23 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

24 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

25 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 n Heuristics successfully applied to vehicle routing

26 Heuristic Types n Construction n Improvement n Mathematical programming n Decomposition n Partitioning

27 Simulation n Technique for conducting experiments with a computer on a model of a management system n Frequently used DSS tool

28 Major Characteristics of Simulation n Imitates reality and capture its richness n Technique for conducting experiments n Descriptive, not normative tool n Often to solve very complex, risky problems

29 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)

30 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

31 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

32 Simulation Methodology 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

33 Simulation Types n Probabilistic Simulation –Discrete distributions –Continuous distributions –Probabilistic simulation via Monte Carlo technique –Time dependent versus time independent simulation –Simulation software –Visual simulation –Object-oriented simulation

34 Visual Spreadsheets n User can visualize models and formulas with influence diagrams n Not cells--symbolic elements

35 Visual Interactive Modeling (VIS) and Visual Interactive Simulation (VIS) n Visual interactive modeling (VIM) Also called –Visual interactive problem solving –Visual interactive modeling –Visual interactive simulation n Use computer graphics to present the impact of different management decisions. n Can integrate with GIS n Users perform sensitivity analysis n Static or a dynamic (animation) systems

36 Generated Image of Traffic at an Intersection from the Orca Visual Simulation Environment (Courtesy Orca Computer, Inc.)

37 Visual Interactive Simulation (VIS) n Decision makers interact with the simulated model and watch the results over time n Visual interactive models and DSS –Queueing

38 SUMMARY n Models play a major role in DSS n Models can be static or dynamic n Analysis is under assumed certainty, risk, or uncertainty –Influence diagrams –Spreadsheets –Decision tables and decision trees n Spreadsheet models and results in influence diagrams n Optimization: mathematical programming (More)

39 n Linear programming: economic-based n Heuristic programming n Simulation - more complex situations n Expert Choice n Multidimensional models - OLAP n Quantitative software packages-OLAP (statistical, etc.) n Visual interactive modeling (VIM) n Visual interactive simulation (VIS)