Generic vs. Engineered Evolutionary Algorithms in Batch Scheduling with Recourse

This paper considers a case study of a batch chemical scheduling problem on a moving horizon with significant uncertainties in demand. The scheduling problem is represented as a two-stage stochastic integer program and solved by a stage-decomposition based hybrid algorithm with an evolutionary algorithm for the first-stage and mathematical programming for the second-stage. We describe an engineered evolutionary algorithm with systematic inclusion of process knowledge versus a generic evolutionary algorithm. The former exploits the hierarchical structure of operation, batching and scheduling decisions in the solution space representation and the mutation operator. Comparative numerical experiments show that the coverage of the feasible search space is significantly improved and the convergence to good solutions is faster.