Quantum Evolutionary Algorithm for Chemical Batch Scheduling Problem

Abstract

This paper presents an improved particle swarm optimization combined with quantum evolutionary algorithm (QAE). In the algorithm, continuous coding represents weight information of the batches’ sequence to enhance the ability of handling the constraints. The batch separation strategy unifies the relationship of scheduling time into minimum time span between batches and brings about the feasible processing sequence. Scheduling generation and repair strategies are proposed to obtain feasible solutions. In order to verify the performance of the QAE algorithm, the well-know benchmark scheduling instances are tested. The computational results show that the QAE may find optimal or suboptimal solutions in a short run time for all the instances.