SLIDESHARE PPT-DECISION MAKING METHODS.pptx

DECISION MAKING

1. TECHNIQUES FOR DATA AND KNOWLEDGE DRIVEN MULTI-CRITERIA DECISION MODELS PRESENTED BY M.CHAIRMAN AP/ECE KARPAGAM INSTITUTE OF TECHNOLOGY

2. What is multi-criteria decision analysis Multi-criteria decision analysis (MCDA) is a formal, structured and transparent decision making methodology. Its ism is to assist groups or individual decision makers to explore their decisions in the case of complex situations with multiple criteria. MCDA does not provide the ‘right’ answer. MCDA does not provide an objective analysis. MCDA does not relieve decision makers of the responsibility of making difficult judgments.

3. MCDM MCDA assists the decision maker in confidently reaching a decision by: enabling decision makers to gain a better understanding of the problem faced; organising and synthesizing the entire range of information; integrating objective measurements with value judgements; making explicit and managing the decision maker’s subjectivity; and ensuring that all criteria and decision factors have been taken properly into account.

4. Steps of Decision Making Process Step 1. Define the problem “This process must, as a minimum, identify root causes, limiting assumptions, system and organizational boundaries and interfaces, and any stakeholder issues. The goal is to express the issue in a clear, one-sentence problem statement that describes both the initial conditions and the desired conditions.” Step 2. Determine requirements “Requirements are conditions that any acceptable solution to the problem must meet. Requirements spell out what the solution to the problem must do.”

5. Steps of Decision Making Process Step 3. Establish goals Goals are broad statements of intent and desirable programmatic values.... Goals go beyond the minimum essential must have’s (i.e. requirements) to wants and desires .” In mathematical form, the goals are objectives contrary to the requirements that are constraints. The goals may be conflicting but this is a natural concomitant of practical decision situations. Step 4. Identify alternatives “Alternatives offer different approaches for changing the initial condition into the desired condition.” Be it an existing one or only constructed in mind, any alternative must meet the requirements. If the number of the possible alternatives is finite, we can check one by one if it meets the requirements.

6. Steps of Decision Making Process Step 5. Define criteria “Decision criteria, which will discriminate among alternatives, must be based on the goals. It is necessary to define discriminating criteria as objective measures of the goals to measure how well each alternative achieves the goals.” Since the goals will be represented in the form of criteria, every goal must generate at least one criterion but complex goals may be represented only by several criteria. Step 6. Select a decision making tool There are several tools for solving a decision problem. Some of them will be briefly described here, and references of further readings will also be proposed. The selection of an appropriate tool is not an easy task and depends on the concrete decision problem, as well as on the objectives of the decision makers.

7. Steps of Decision Making Process Step 7. Evaluate alternatives against criteria Every correct method for decision making needs, as input data, the evaluation of the alternatives against the criteria. Step 8. Validate solutions against problem statement The alternatives selected by the applied decision making tools have always to be validated against the requirements and goals of the decision problem .

8. CLASSIFICATION OF DECISION MAKING METHODS

9. Optimality under constraints T he choices / decisions of the decision maker differs with his preferences, T he constraints limit the available choices, T he decisions under multiple preferences / goals / objectives are more complex and It may not be possible to attain / achieve many goals at all circumstances.

10. Purchase of Car Consider the cost, performance, comfort and warranty data of different models of car as given in Table I. Table II provides the choice of various buyers under different preferences and no restrictions on other attributes. Under certain restrictions given as conditions, the choices of the buyers differ and are shown in Table III. Table IV provides the solutions for multiple preferences .

11. Table I Data of different models of CAR Car Model Criteria / Attributes Price in Rs Fuel Efficiency in km per liter Comfort Index Max: 100 Warranty period in years A 5,00,000 16 70 6 B 6,00,000 20 75 4 C 4,00,000 13 60 6 D 8,00,000 18 90 5

12. Table II Choice of the buyer for different preferences under no conditions Preference Best choice (Optimal) Minimum Price C Maximum Fuel Efficiency B Maximum comfort level D Maximum Warranty A, C

13. Table III Choice of the buyer for different preferences under conditions Preference Condition(s) Best choice (Optimal) Minimum price Fuel Efficiency 15 A Minimum price Fuel Efficiency 15 Comfort level 75 B Minimum price Fuel Efficiency 15 Comfort level 75 Warranty 5 D

14. Table IV Choice of the buyer for more than one preference Preferences Condition(s) Best choice (Optimal) Minimum Price & Maximum Fuel Efficiency NIL NIL Minimum Price & Maximum Fuel Efficiency Comfort level 5 NIL Minimum Price & Maximum Warranty NIL C Minimum Price & Maximum Warranty Comfort level 5 NIL

15. Single Objective Vs Multi Objective Depending on the form and functional description of the optimization problem, different optimization techniques can be used for the solution, linear programming, nonlinear programming, discrete optimization, etc. The case when we have a finite number of criteria but the number of the feasible alternatives (the ones meeting the requirements) is infinite belongs to the field of multiple criteria optimization. Also, techniques of multiple attribute optimization can be used when the number of feasible alternatives is finite but they are given only in implicit form.

16. General OR model Any OR Problem is defined with: Objective Criterion: Performance Evaluation Parameter Constraints: Restrictions, Limitations, Availability Decision Variables: Influencing Parameters

17. Analytical Hierarchy Process (AHP) AHP was developed in the late 1970s. Today it is the most widely used MCDA method. AHP generates all criteria weighting and alternative preference within each criteria by eliciting these values from the decision maker through a series of pairwise comparisons, as opposed to utilising numerical values directly. Thus, a complex decision is reduced to a series of simpler ones, between pairs of alternative values within criteria or between pairs of criteria. The decision maker’s preference is always explicit. However, the decision maker may be asked to make very many small decisions. Hence, it becomes important to generate an optimised hierarchy of criteria and alternatives, to reduce the number of pairwise decisions .

18. STEPS IN AHP Step 1: Construct the problem hierarchy Model, usually visually, the problem decision identifying relationships between criteria and alternatives. Step 2: Pairwise comparison of criteria Undertake pairwise comparison between criteria, identifying decision maker preference for criteria on which alternatives are evaluated. Step 3: Pairwise comparison of alternatives within each criterion Undertake pairwise comparison between alternatives based on their performance within each criterion. Step 4: Compute the vector of criteria weights From a matrix of pairwise comparison results AHP utilises a variety of matrix transformations to calculate criteria weight vectors representing normalised criteria weightings.

19. STEPS IN AHP Step 5: Compute the matrix of alternative scores From the results of the pairwise comparisons on alternatives within each criterion a n x m (where n is the number of criteria and m is the number of alternatives) matrix is constructed representing the normalised performance (score) of each alternative for each criteria.   Step 6: Ranking the alternatives Utilising the vectors of criteria weights and the matrix of alternative scores a global score and hence ranking for each alternative is calculated using:   where: a is the alternative, c is the criteria, g is the global score of the alternative, w is the criteria weight and s is the alternative score. A function of the ranking equation, aggregating across each criteria means that trade-offs between criteria in fundamental to the final ranking.

20. AHP

21. Decision Making Software (DMS)   Software Supported MCDA method(s) Pair-wise comparison Time analysis Sensitivity analysis Group evaluation Risk management Web- based version AIRM Online AIRM No No Yes No Yes Yes 1000Minds PAPRIKA Yes No Yes Yes No Yes Analytica   No Yes Yes No Yes Yes Criterium DecisionPlus AHP No No Yes No No No DecideIT MAUT Yes No Yes Yes Yes Yes Decision Lab   Yes No Yes No Yes No Decision Lens AHP, ANP Yes   Yes Yes   Yes D-Sight MAUT, PROMETHEE Yes No Yes Yes Yes Yes Expert Choice AHP Yes No Yes Yes Yes Yes Hiview3   No No Yes Yes No No Logical Decisions AHP, MAUT Yes No Yes Yes Yes No MakeItRational AHP Yes No Yes Yes No Yes MindDecider AHP Yes Yes Yes Yes Yes No Rational Focal Point (RFP), RFP Smarter Government   AHP, ANP   Yes   Yes   Yes   Yes   Yes   Yes TreeAge Pro   No No Yes No Yes No Very Good Choice ELECTRE Yes No Yes Yes Yes No

22. CONCLUSION These methods can provide solutions to increasing complex management prob- lems . They provide better understanding of inherent features of decision problem, promote the role of participants in decision-making processes, facilitate compromise and collective decisions and provide a good platform to understand the perception of models and analysts in a realistic scenario. The methods help to improve quality of decisions by making them more explicit, rational and efficient. Negotiating, quantifying and communicating the priorities are also facilitated with the use of these methods. It should be noted that methods and results are not necessarily comparable. Every method has its restrictions, mostly due to model assumptions, which should be considered when the method is used.

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