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

Lijun He, Raymond Chiong, Wenfeng Li, Gregorius Satia Budhi, Yu Zhang

Abstract: 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.