Optimizing job release and scheduling jointly in a reentrant hybrid flow shop

作者：

Highlights：

• A model of RHFS-OJRSJ is proposed to optimize energy consumption and makespan.

• An IMOEA/IOD is designed to bridge the gap of job release and scheduling problem.

• The genetic algorithm is employed to generate an initially best job release plan.

• Inverse optimization is employed to further optimize the job release plan.

摘要

•A model of RHFS-OJRSJ is proposed to optimize energy consumption and makespan.•An IMOEA/IOD is designed to bridge the gap of job release and scheduling problem.•The genetic algorithm is employed to generate an initially best job release plan.•Inverse optimization is employed to further optimize the job release plan.

论文关键词：Job release,Scheduling,Reentrant hybrid flow shop,Inverse optimization,IMOEA/IOD,Adaptive neighborhood updating strategy

论文评审过程：Received 13 February 2021, Revised 19 July 2022, Accepted 22 July 2022, Available online 28 July 2022, Version of Record 10 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118278