Quantum Evolutionary Algorithm for Parallel Flowshop Scheduling Problem in Chemical Industry

This paper studies the typical batching and scheduling problem of parallel flowshop, which is characterized by multi-product, multi-stage, parallel production, production coordination between stages, limit of equipment production capacity and inventory capacity, parallel equipment selection and limitation of batch quantity. This paper presents modified quantum evolutionary algorithm(MQEA) with continuous codes representing the batch size on the every stage to enhance the ability to handle constraints. The computational results show that the MQAE may find optimal or suboptimal solutions in a short run time for all the instances.