The dynamic programming approach to the multicriterion optimization problem.
Kim, Kwang Bog
Hartman, James K.
Howard, Gilbert T.
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Decision makers are often confronted with problems for which there exist several distinct measures of success. Such problems can often be expressed in terms of linear or nonlinear programming models with several "criterion" functions instead of single objective functions. A variety of techniques have been applied to multicriterion problems, but the approach used here, "The Dynamic Programming Approach to Multicriterion Optimization Problem," is based on the concept that the ideal solution to a multiobjective problem must be a pareto optimal solution. In many cases simply narrowing the set of candidate solutions to the set of all pareto optimal solutions may enable the decision maker to find the compromise being sought. The determination of nondominated points and corresponding nondominated values (pareto optimal solution) related to the multicriterion optimization problem is approached through the use of dynamic programming. The dynamic programming approach has an attractive property which provides the basis for generation of nondominated solutions at each stage by the decomposition method. By using recursive equations we can find out the nondominated points and corresponding nondominated solutions of multiaggregate return function.
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