Hybrid Algorithm for Risk Conscious Chemical Batch Planning Under Uncertainty
We consider planning problems of flexible chemical batch processes paying special attention to uncertainties in problem data. The optimization problems are formulated as two-stage stochastic mixed-integer models in which some of the decisions (first-stage) have to be made under uncertainty and the remaining decisions (second-stage) can be made after the realization of the uncertain parameters. The uncertain model parameters are represented by a finite set of scenarios. The risk conscious planning problem under uncertainty is solved by a stage decomposition approach using a multi-objective evolutionary algorithm which optimizes the expected scenario costs and the risk criterion with respect to the first-stage decisions. The second-stage scenario decisions are handled by mathematical programming. Results from numerical experiments for a multi-product batch plant are presented.
KeywordsRisk Measure Hybrid Algorithm Master Problem Scenario Cost Mutation Strength
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