1. The Components of An Architecture for DSS
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2. 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.
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3. Decision Support Systems
The Components of a DSS
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4. 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.
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5. 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
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6. 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
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7. 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.
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8. Decision Support Systems
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9. 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
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10. Decision Support Systems
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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
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12. Decision Support Systems
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13. 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
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14. Decision Support Systems
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15. 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
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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
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17. Decision Support Systems
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18. 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
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19. Decision Support Systems
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20. Decision Support Systems
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21. Decision Analysis of Few Alternatives
Decision Tables
Maximax
Maximin
Equal Likelihood
Hurwicz
Minimax Regret
EOL/EVPI
Decision Tree
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22. 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.
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23. Example - the Real Estate Investments
Dominant Decision: a decision has better payoff than another in all possible states of nature.
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24. Decision Support Systems
Maximax Criterion
The maximum of the maximum payoffs
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25. Decision Support Systems
Maximin Criterion
The maximum of the minimum payoffs
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26. Decision Support Systems
The Regret Table
The difference between the payoff from the best decision and all other decision payoffs.
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27. Minimax Regret Criterion
The minimum of the maximum regret.
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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
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29. 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.
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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
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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
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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 = $28,000, the amount of money one would pay for the perfect information.
EVPI(office) = EOL(office)
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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.
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34. Decision Support Systems
Decision Tree for the Example with Expected Values
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35. Sequential Decision Tree
$2,000,000
$3,000,000
$700,000
$2,300,000
$1,000,000
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36. Decision Support Systems
Sequential Decision Tree with Nodal Expected Values
$2,000,000
$3,000,000
$700,000
$2,300,000
$1,000,000
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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.
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38. 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
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39. 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”
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40. 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.
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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
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42. Applications of the AHP (2/2)
Measuring Performance
Designing a System
Ensuring System Stability
Optimizing
Planning
Conflict Resolution
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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.
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44. Decision Support Systems
The Weighting Matrix
Eigenvalue and Eigenvector problem
AX = lX, where l = n.
Wi = 1
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45. Decision Support Systems
Rating Scale for Comparison
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46. Decision Support Systems
Example - Buying a Car
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47. Decision Support Systems
Pair Comparison (1/2)
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48. Decision Support Systems
Pair Comparison (2/2)
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49. Decision Support Systems
Composite Priority
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50. Criteria User Interface
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