Hybrid Algorithm for Risk Conscious Chemical Batch Planning Under Uncertainty

Thomas Tometzki () and Sebastian Engell ()
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Thomas Tometzki: Technische Universität Dortmund, Process Dynamics and Operations Group, Department of Biochemical and Chemical Engineering
Sebastian Engell: Technische Universität Dortmund, Process Dynamics and Operations Group, Department of Biochemical and Chemical Engineering

A chapter in Modeling, Simulation and Optimization of Complex Processes, 2012, pp 293-304 from Springer

Abstract: Abstract 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.

Keywords: Risk Measure; Hybrid Algorithm; Master Problem; Scenario Cost; Mutation Strength (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-25707-0_24

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DOI: 10.1007/978-3-642-25707-0_24

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