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MSS Modeling

MSS Modeling. Key element in DSS Many classes of models Specialized techniques for each model Allows for rapid examination of alternative solutions Multiple models often included in a DSS Trend toward transparency Multidimensional modeling exhibits as spreadsheet. Major Modeling Issues.

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MSS Modeling

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  1. MSS Modeling • Key element in DSS • Many classes of models • Specialized techniques for each model • Allows for rapid examination of alternative solutions • Multiple models often included in a DSS • Trend toward transparency • Multidimensional modeling exhibits as spreadsheet

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

  3. Simulations • Explore problem at hand • Identify alternative solutions • Can be object-oriented • Enhances decision making • View impacts of decision alternatives

  4. DSS Models • Algorithm-based models • Statistic-based models • Linear programming models • Graphical models • Quantitative models • Qualitative models • Simulation models © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  5. Problem Identification • Environmental scanning and analysis • Business intelligence • Identify variables and relationships • Influence diagrams • Cognitive maps • Forecasting • Fueled by e-commerce • Increased amounts of information available through technology © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  6. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  7. Static Models • Single photograph of situation • Single interval • Time can be rolled forward, a photo at a time • Usually repeatable • Steady state • Optimal operating parameters • Continuous • Unvarying • Primary tool for process design

  8. Dynamic Model • Represent changing situations • Time dependent • Varying conditions • Generate and use trends • Occurrence may not repeat

  9. Decision-Making • Certainty • Assume complete knowledge • All potential outcomes known • Easy to develop • Resolution determined easily • Can be very complex © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  10. Decision-Making • Uncertainty • Several outcomes for each decision • Probability of occurrence of each outcome unknown • Insufficient information • Assess risk and willingness to take it • Pessimistic/optimistic approaches © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  11. Decision-Making • Probabilistic Decision-Making • Decision under risk • Probability of each of several possible outcomes occurring • Risk analysis • Calculate value of each alternative • Select best expected value © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  12. Influence Diagrams • Graphical representation of model • Provides relationship framework • Examines dependencies of variables • Any level of detail • Shows impact of change • Shows what-if analysis © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  13. Influence Diagrams Variables: Intermediate or uncontrollable Result or outcome (intermediate or final) Decision Arrows indicate type of relationship and direction of influence Certainty Amount in CDs Interest earned Sales Uncertainty Price © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  14. Influence Diagrams ~ Demand Random (risk) Place tilde above variable’s name Sales Sleep all day Graduate University Preference (double line arrow) Get job Ski all day Arrows can be one-way or bidirectional, based upon the direction of influence © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  15. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  16. Modeling with Spreadsheets • Flexible and easy to use • End-user modeling tool • Allows linear programming and regression analysis • Features what-if analysis, data management, macros • Seamless and transparent • Incorporates both static and dynamic models © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  17. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  18. Decision Tables • Multiple criteria decision analysis • Features include: • Decision variables (alternatives) • Uncontrollable variables • Result variables • Applies principles of certainty, uncertainty, and risk © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  19. Decision Tree • Graphical representation of relationships • Multiple criteria approach • Demonstrates complex relationships • Cumbersome, if many alternatives © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  20. MSS Mathematical Models • Link decision variables, uncontrollable variables, parameters, and result variables together • Decision variables describe alternative choices. • Uncontrollable variables are outside decision-maker’s control. • Fixed factors are parameters. • Intermediate outcomes produce intermediate result variables. • Result variables are dependent on chosen solution and uncontrollable variables. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  21. MSS Mathematical Models • Nonquantitative models • Symbolic relationship • Qualitative relationship • Results based upon • Decision selected • Factors beyond control of decision maker • Relationships amongst variables © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  22. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  23. Mathematical Programming • Tools for solving managerial problems • Decision-maker must allocate resources amongst competing activities • Optimization of specific goals • Linear programming • Consists of decision variables, objective function and coefficients, uncontrollable variables (constraints), capacities, input and output coefficients © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  24. Multiple Goals • Simultaneous, often conflicting goals sought by management • Determining single measure of effectiveness is difficult • Handling methods: • Utility theory • Goal programming • Linear programming with goals as constraints • Point system © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  25. Sensitivity, What-if, and Goal Seeking Analysis • Sensitivity • Assesses impact of change in inputs or parameters on solutions • Allows for adaptability and flexibility • Eliminates or reduces variables • Can be automatic or trial and error • What-if • Assesses solutions based on changes in variables or assumptions • Goal seeking • Backwards approach, starts with goal • Determines values of inputs needed to achieve goal • Example is break-even point determination © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  26. Search Approaches • Analytical techniques (algorithms) for structured problems • General, step-by-step search • Obtains an optimal solution • Blind search • Complete enumeration • All alternatives explored • Incomplete • Partial search • Achieves particular goal • May obtain optimal goal © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  27. Search Approaches • Heurisitic • Repeated, step-by-step searches • Rule-based, so used for specific situations • “Good enough” solution, but, eventually, will obtain optimal goal • Examples of heuristics • Tabu search • Remembers and directs toward higher quality choices • Genetic algorithms • Randomly examines pairs of solutions and mutations © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  28. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  29. Simulations • Imitation of reality • Allows for experimentation and time compression • Descriptive, not normative • Can include complexities, but requires special skills • Handles unstructured problems • Optimal solution not guaranteed • Methodology • Problem definition • Construction of model • Testing and validation • Design of experiment • Experimentation • Evaluation • Implementation © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  30. Simulations • Probabilistic independent variables • Discrete or continuous distributions • Time-dependent or time-independent • Visual interactive modeling • Graphical • Decision-makers interact with simulated model • may be used with artificial intelligence • Can be objected oriented © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  31. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

  32. 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

  33. 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

  34. 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

  35. Linear programming components • LP is composed of the following: 1- decision variables- vars whose values are unknown and / or searched for. 2- objective functions: math functions that do the following: a- relates decision vars to goals. b- measure goal attainment to be optimized.

  36. 3- objective function coefficient: unit profit or cost coefficient indicating the contribution to the objective of one unit of decision variable. 4- constraints: expressed in the form of linear inequalities or equalities that limit resources and/or requirements. 5-capacities describe upper and lower limits on constraint variables.

  37. 6- input-output coefficient-technology which indicate resource utilization for decision variables. Do the homework handed in class for linear programming implentation.

  38. Multiple Goals • Analysis of management decision aims at evaluating how far each alternative brings management toward achieving its goals. • Most management decisions have multiple goals. • Different management have different goals • To achieve multiple goals, we need to analyze each alternative in light of achievement of the proposed goal.

  39. Goals may complement each other or conflict each other. • Difficulties of analyzing multiple goals: 1- it is hard for organizations to clearly state their goals. 2-DM may change the importance assigned to goals overtime or for different situation – situation change.

  40. 3- goals and sub goals are viewed differently at various levels of organization and within different departments. • 4-if organization changes or the environment changes goals also change. • 5-difficult to quantify relations between alternatives and their role in goal determination.

  41. 6-Each DM has different goals regarding a complex problem and he participate to solve it. 7-particpant in problem solving assess the priorities of various goals differently.

  42. Method to handle Multiple Goals 1- utility theory 2- goal programming 3- expression of goals and constraints using LP. 4- a point system

  43. Sensitivity Analysis • Attempts to assess the impact of change in the input data or parameters on the proposed solution- the result variables. • Allows flexibility and adaptation to changing conditions and to the requirements of different decision-making situation. • Provide better understanding of the model and the decision making situation it attempts to describe.

  44. Permits managers to input data so that confidence in the model increases. • Tests relationships such as: 1- impact of change in external variables (uncontrolled) and on outcome variables. 2-Impact of changes in decision variables on outcome vars. 3- effect of uncertainly in estimating external vars.

  45. 4-Effects of different dependent interactions among vars. 5-Robustness of decision under changing conditions

  46. Uses of sensitivity analysis Sensitivity analysis can be used for: 1- revising models to eliminate large sensitivities. 2-adding details about sensitive vars or scenarios. 3-obtaining better estimates of sensitive external vars. 4-altering the real-world system to reduce actual sensitivity.

  47. 5- accepting and using the sensitive real world, leading to continuous and close monitoring of actual results.

  48. Types of sensitivity A- automatic sensitivity analysis • Reports the range within which a certain input var (unit cost) can vary without affecting the proposed solution. • Usually limited to one change at a time, only for certain vars.

  49. B- Trail and Error: • Impact of change in any var or several vars, can be determined through trail and error approach. • You can change some input and solve problem again. The more vars change the more solutions you discover. This can be done through either what-if or goal seeking.

  50. What-if : structured as what will happen to the solution if an input var or an assumption or value is changed? • Goal-seeking: calculates values of the input necessary to achieve a desired level of an output (goal). It represent a backward solution approach. Ex: how many tellers needed to reduce waiting time in a bank?

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