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The Components of An Architecture for DSS

The Components of An Architecture for DSS. ( D,D,M) Paradigm. Dialog (D) The interface between users and the system. Data (D) The data base and database management system that support the required data and information. Models (M)

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The Components of An Architecture for DSS

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  1. The Components of An Architecture for DSS Decision Support Systems

  2. (D,D,M) Paradigm • Dialog (D) • The interface between users and the system. • Data (D) • The data base and database management system that support the required data and information. • Models (M) • The model base and model base management system that provide the analysis capacities. Decision Support Systems

  3. The Components of a DSS Decision Support Systems

  4. The Dialog Component • Knowledge Base • What knowledge the user must bring to the system in order to interact with it in dealing with the problem area or making the necessary decisions. • What the user knows about the decision and about how to use the DSS. • Action Language • The option for directing the system’s actions. • Question-answer, menu-oriented, command language approaches, visual oriented interfaces, voice input (speech recognition), and so on. • Presentation Language • The alternative presentations of the system’s responses. • Text/Numbers, graphics, animation, voice output, and so on. Decision Support Systems

  5. The Data Component • Internal information • Entities: employees, customers, parts, machines, and so on. • Concepts: ideas, thoughts, and opinions. • External Information • Government data, public database, and so on. • DBMS Decision Support Systems

  6. The Model Component • Types of Models • Optimization Model <=> Descriptive Model • Probabilistic Model <=> Deterministic Model • Customer-built Model <=> Ready-built Model • Model Base • Strategic Models: the policies that govern the acquisition, use, and disposition of resources. • Tactical Models: financial planning, worker requirements planning, and so on. • Operational Models: production scheduling, inventory control, and so on. • Model-building Blocks and Subroutines Decision Support Systems

  7. Systems • - A collection of objects such as people, resources, concepts, and procedures intended to perform an identifiable function or to serve a goal. • The Structure of a System • Closed Vs Open Systems • Black Box • A special type of closed system, in which inputs and outputs are well defined but the process itself is not specified. • Effectiveness = doing the “right” thing • The degree to which goals are achieved. • Efficiency = doing the “thing” right • A measure of the use of inputs (or resources) to achieve results. Decision Support System

  8. Decision Support Systems

  9. Models • - Simplified representation or abstraction of reality. • Simplified • Representative • Keep relevant to the specific problem • Types of Models • Iconic (Scale) Models: ex. physical replica • Analog Models: ex. graphics, simulation/animation • Mathematical (Quantitative) Models Decision Support System

  10. Decision Support Systems

  11. Modeling and Model Management • Types of Models • Statistical/Regression Analysis • Financial • Optimization • Some Aspects • Identification of the Problem and Environmental Analysis • Identification of the Variables • Forecasting • Models • Which to include, too many, or not enough? • Model Management Decision Support Systems

  12. Decision Support Systems

  13. Optimization • Mathematical Programming • allocate scarce resources among various activities to optimize a measurable goal • Linear Programming • Decision Variables • Objective Function • Optimization • Coefficients of the Objective Function • Constraints • Input-Output Coefficients • Capacities Decision Support Systems

  14. Decision Support Systems

  15. Simulation • Advantages and Disadvantages • The Process of Simulation • Types of Simulation • Probabilistic Simulation • Discrete • Continuous • Time Dependent Vs. Time Independent • Visual Simulation • Simulation Experimentation Decision Support Systems

  16. Heuristic Programming • The approach of employing heuristics to arrive at feasible and “good enough” solutions to some complex problems. • “Good Enough” : the range of 90-99% of the true optimal solution • Methodology • When to use heuristics • Advantages and Disadvantages Decision Support Systems

  17. Decision Support Systems

  18. Decision Making • - A process of choosing among alternative courses of action for the purpose of attaining a goal or goals. • - Synonymous with the whole process of management (involves a series of decisions, i.e., What, When, How, Where, By Whom, ...) • The Phases of the Decision Process • Intelligence • Design • Choice • Implementation • Decision Making Vs Problem Solving Decision Support System

  19. Decision Support Systems

  20. Decision Support Systems

  21. Decision Analysis of Few Alternatives • Decision Tables • Maximax • Maximin • Equal Likelihood • Hurwicz • Minimax Regret • EOL/EVPI • Decision Tree Decision Support Systems

  22. The Payoff Table • A payoff table is a means of organizing a decision situation, including the payoffs from different decisions given the various states of nature. • A state of nature is an actual event that may occur in the future. Decision Support Systems

  23. Example - the Real Estate Investments • Dominant Decision: a decision has better payoff than another in all possible states of nature. Decision Support Systems

  24. Maximax Criterion • The maximum of the maximum payoffs Decision Support Systems

  25. Maximin Criterion • The maximum of the minimum payoffs Decision Support Systems

  26. The Regret Table • The difference between the payoff from the best decision and all other decision payoffs. Decision Support Systems

  27. Minimax Regret Criterion • The minimum of the maximum regret. Decision Support Systems

  28. Equal Likelihood Criterion • The maximum of the expected payoffs based on the equal probability of the states of nature. • Ex. $50,000(0.5)+30,000(0.5) = $40,000 Decision Support Systems

  29. Hurwicz Criterion • The coefficient of optimism, a, is a measure of the decision maker’s optimism. • Multiplies the best payoff by a and the worst payoff by 1-a for each decision, and the best result is selected. Decision Support Systems

  30. Expected Value Criterion • The maximum of the expected payoffs based on the given probability for the states of nature. • EV(office) = $100,000(0.6)-40,000(0.4) = $44,000 Decision Support Systems

  31. The Regret Table with Probabilities • Calculate the opportunity loss and choose the minimum. • EOL(office) = $0(0.6)+70,000(0.4) = $28,000 Decision Support Systems

  32. EVPI - expected value of the perfect information • Given perfect information, the expected payoffs would be $100,000(0.6)+$30,000(0.4) = $72,000. • Without perfect information, EV(office) = $100,000(0.6)-40,000(0.4) = $44,000 • EVPI(office) = $72,000 - 44,000 = $28,000, the amount of money one would pay for the perfect information. • EVPI(office) = EOL(office) Decision Support Systems

  33. Decision Tree for the Example • A decision tree is a diagram consisting of square decision nodes, circle probability nodes, and branches representing decision alternatives. Decision Support Systems

  34. Decision Tree for the Example with Expected Values Decision Support Systems

  35. Sequential Decision Tree $2,000,000 $3,000,000 $700,000 $2,300,000 $1,000,000 Decision Support Systems

  36. Sequential Decision Tree with Nodal Expected Values $2,000,000 $3,000,000 $700,000 $2,300,000 $1,000,000 Decision Support Systems

  37. Analytic Hierarchy Process • A multiobjective multicriteria decision-making approach which employs a pairwise comparison procedure to arrive at a scale of preferences among sets of alternatives. Decision Support Systems

  38. Why AHP ? • Deriving Weights (Priorities) for a set of activities according to importance. • Multiple Criteria • Multiple Objectives • A single overall priority for all activities • Tradeoffs, Fuzziness, and no unified scale of measurement Decision Support Systems

  39. Assumption of AHP • The methods we use to pursue knowledge, to predict, and to control our world are relative, and that the goal that we seek, i.e., knowledge, is itself relative. • It admits inconsistency (including lack of transitivity) and measures the effect of different levels of consistency on the results we seek. • “Perceived constraints must be examined and not taken for granted - the only hope we have to plan our way out of difficult problems” Decision Support Systems

  40. AHP • Causal Processes • an action is described as an event with particular outcomes. • Cause -> Event (Outcome) • Purposive Action Processes • Action -> Event (outcome) -> Consequences • The actor makes his choice of actions through his perception of the consequences that the outcomes will have for him. • The AHP synthesizes these two approaches by identifying the outcomes that are more beneficial to the actors, and at the same time provides a way of accessing the factors (causes) which may have more to do with certain types of outcomes. Decision Support Systems

  41. Applications of the AHP (1/2) • Setting Priorities • Generating a Set of Alternatives • Choosing a Best Policy Alternative • Determining Requirements • Making Decisions Using Benefits and Costs • Allocating Resources • Predicting Outcomes (Time Dependence) - Risk Assessment Decision Support Systems

  42. Applications of the AHP (2/2) • Measuring Performance • Designing a System • Ensuring System Stability • Optimizing • Planning • Conflict Resolution Decision Support Systems

  43. Procedure for AHP Analysis • Determining the requirements of the system • What do we need to do? • Generating alternatives to satisfy those requirements • What are the possible ways of action? • Setting priorities according to the importance of the requirements in order to implement the alternatives to attain some higher objective • Choosing the best policy alternative, or a mix of the best policy alternatives • Using forward and backward projections to obtain a stable outcome. Decision Support Systems

  44. The Weighting Matrix • Eigenvalue and Eigenvector problem • AX = lX, where l = n. • Wi = 1 Decision Support Systems

  45. Rating Scale for Comparison Decision Support Systems

  46. Example - Buying a Car Decision Support Systems

  47. Pair Comparison (1/2) Decision Support Systems

  48. Pair Comparison (2/2) Decision Support Systems

  49. Composite Priority Decision Support Systems

  50. Criteria User Interface Decision Support Systems

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