Risk Management in Production Planning under Uncertainty by Multi-Objective Hybrid Evolutionary Algorithms

We consider production planning problems with uncertainties in the 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 production planning problem under uncertainty is solved by a stage decomposition approach using a multi-objective evolutionary algorithm which takes the expected scenario costs and a risk criterion into account to compute the first-stage variables. The second-stage scenario decisions are handled by mathematical programming. Results from numerical experiments for a multi-product batch plant are presented.