A multiobjective evolutionary algorithm for achieving energy efficiency in production environments integrated with multiple automated guided vehicles

作者：

Highlights：

• Automated guided vehicles (AGVs) are used for energy-efficient job-shop scheduling.

• A new multiobjective mathematical model is formulated for the problem.

• An effective multiobjective evolutionary algorithm (EMOEA) is designed.

• Opposition-based learning is employed to balance exploration and exploitation.

• Results confirm the validity of the model and efficacy of the proposed EMOEA.

摘要

•Automated guided vehicles (AGVs) are used for energy-efficient job-shop scheduling.•A new multiobjective mathematical model is formulated for the problem.•An effective multiobjective evolutionary algorithm (EMOEA) is designed.•Opposition-based learning is employed to balance exploration and exploitation.•Results confirm the validity of the model and efficacy of the proposed EMOEA.

论文关键词：Energy efficiency,Automated guided vehicles,Job-shop scheduling,Evolutionary algorithms,Multiobjective optimization,Opposition-based learning

论文评审过程：Received 5 September 2020, Revised 24 January 2022, Accepted 25 January 2022, Available online 2 February 2022, Version of Record 3 March 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108315