Solving extended hybrid-flow-shop problems using active schedule generation and genetic algorithms

Abstract

We propose a hybrid approach for solving hybrid-flow-shop problems based on the combination of genetic algorithms and a modified Giffler & Thompson (G&T) algorithm. Several extensions of the hybrid-fiow-shop are considered and discussed in the context of a real-world example. The genome in the GA encodes a choice of rules to be used to generate production schedules via the G&T algorithm. All constraints to the scheduling task are observed by the G&T algorithm. Therefore, it provides a well suited representation for the GA and leads to a decoupling of domain specific details and genetic optimization. The proposed method is applied to the optimization of a batch annealing plant.