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# 310-2slides2 - ues of decision variables model becomes a...

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2.1 Decision Making by Quantitative Analysis Katta G. Murty Lecture slides For problems which are very important, or very complex, good solutions cannot be developed without the aid of quantitative analysis. Quantitative analysis requires the representation of the problem using a mathematical model. Mathematical modeling is a criti- cal part of the quantitative approach to decision making. Con- structing mathematical models, to represent real world problems reasonably closely, is an essential skill for all engineers. Two types of factors e ff ecting system performance: Uncontrollable factors: Factors such as environmental factors, not under the control of the decision maker. Controllable inputs: Factors whose values can be con- trolled by the decision maker, which a ff ect the functioning of the system. These factors are called the decision variables in the model. 24

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Deterministic models: If all uncontrollable inputs are known exactly and do not vary, system performance depends only on val-
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Unformatted text preview: ues of decision variables, model becomes a deterministic model. No uncertaity. Stochastic or Probabilistic models: When uncontrollable inputs are uncertain and subject to variation. Here system per-formance uncertain even when the values of all decision variables are f xed. We study deterministic models in this course. 25 Steps in Decision Making by Quantitative Analysis : Model Building : Build a mathematical model that represents how system functions (identify decision variables, constraints on them, and objective function that measures system per-formance). Solve Model : Use an e ﬃ cient algorithm to get an optimum solution. Implement solution, or Update Model & Repeat : Check solution for practical feasibility. If it is not, make necessary changes in model & repeat. Also, many times decision makers use their practical knowl-edge to transform optimum solution into an implementable solution. 26...
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