The Components of An Architecture for DSS 2018/12/2 Decision Support Systems

Decision Support Systems (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. 2018/12/2 Decision Support Systems

Decision Support Systems The Components of a DSS 2018/12/2 Decision Support Systems

Decision Support Systems 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. 2018/12/2 Decision Support Systems

Decision Support Systems 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 2018/12/2 Decision Support Systems

Decision Support Systems 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 2018/12/2 Decision Support Systems

Decision Support System 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. 2018/12/2 Decision Support System

Decision Support Systems 2018/12/2 Decision Support Systems

Decision Support System 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 2018/12/2 Decision Support System

Decision Support Systems 2018/12/2 Decision Support Systems

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 2018/12/2 Decision Support Systems

Decision Support Systems 2018/12/2 Decision Support Systems

Decision Support Systems 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 2018/12/2 Decision Support Systems

Decision Support Systems 2018/12/2 Decision Support Systems

Decision Support Systems Simulation Advantages and Disadvantages The Process of Simulation Types of Simulation Probabilistic Simulation Discrete Continuous Time Dependent Vs. Time Independent Visual Simulation Simulation Experimentation 2018/12/2 Decision Support Systems

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 2018/12/2 Decision Support Systems

Decision Support Systems 2018/12/2 Decision Support Systems

Decision Support System 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 2018/12/2 Decision Support System

Decision Support Systems 2018/12/2 Decision Support Systems

Decision Support Systems 2018/12/2 Decision Support Systems

Decision Analysis of Few Alternatives Decision Tables Maximax Maximin Equal Likelihood Hurwicz Minimax Regret EOL/EVPI Decision Tree 2018/12/2 Decision Support Systems

Decision Support Systems 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. 2018/12/2 Decision Support Systems

Example - the Real Estate Investments Dominant Decision: a decision has better payoff than another in all possible states of nature. 2018/12/2 Decision Support Systems

Decision Support Systems Maximax Criterion The maximum of the maximum payoffs 2018/12/2 Decision Support Systems

Decision Support Systems Maximin Criterion The maximum of the minimum payoffs 2018/12/2 Decision Support Systems

Decision Support Systems The Regret Table The difference between the payoff from the best decision and all other decision payoffs. 2018/12/2 Decision Support Systems

Minimax Regret Criterion The minimum of the maximum regret. 2018/12/2 Decision Support Systems

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 2018/12/2 Decision Support Systems

Decision Support Systems 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. 2018/12/2 Decision Support Systems

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 2018/12/2 Decision Support Systems

The Regret Table with Probabilities Calculate the opportunity loss and choose the minimum. EOL(office) = $0(0.6)+70,000(0.4) = $28,000 2018/12/2 Decision Support Systems

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) 2018/12/2 Decision Support Systems

Decision Tree for the Example A decision tree is a diagram consisting of square decision nodes, circle probability nodes, and branches representing decision alternatives. 2018/12/2 Decision Support Systems

Decision Support Systems Decision Tree for the Example with Expected Values 2018/12/2 Decision Support Systems

Sequential Decision Tree $2,000,000 $3,000,000 $700,000 $2,300,000 $1,000,000 2018/12/2 Decision Support Systems

Decision Support Systems Sequential Decision Tree with Nodal Expected Values $2,000,000 $3,000,000 $700,000 $2,300,000 $1,000,000 2018/12/2 Decision Support Systems

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. 2018/12/2 Decision Support Systems

Decision Support Systems 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 2018/12/2 Decision Support Systems

Decision Support Systems 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” 2018/12/2 Decision Support Systems

Decision Support Systems 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. 2018/12/2 Decision Support Systems

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 2018/12/2 Decision Support Systems

Applications of the AHP (2/2) Measuring Performance Designing a System Ensuring System Stability Optimizing Planning Conflict Resolution 2018/12/2 Decision Support Systems

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. 2018/12/2 Decision Support Systems

Decision Support Systems The Weighting Matrix Eigenvalue and Eigenvector problem AX = lX, where l = n. Wi = 1 2018/12/2 Decision Support Systems

Decision Support Systems Rating Scale for Comparison 2018/12/2 Decision Support Systems

Decision Support Systems Example - Buying a Car 2018/12/2 Decision Support Systems

Decision Support Systems Pair Comparison (1/2) 2018/12/2 Decision Support Systems

Decision Support Systems Pair Comparison (2/2) 2018/12/2 Decision Support Systems

Decision Support Systems Composite Priority 2018/12/2 Decision Support Systems

Criteria User Interface 2018/12/2 Decision Support Systems