A multi-population co-evolutionary algorithm for green integrated process planning and scheduling considering logistics system

Abstract

Nowadays, green manufacturing is an increasingly significant theme in the worldwide industry fields. System integration can fully exploit the potential of subsystems to achieve satisfying green and low-carbon manufacturing. In particular, integrated process planning and scheduling (IPPS) can obtain better process routes and scheduling schemes to realize more efficient and less energy-consuming production by utilizing the complementary attributes of the process and scheduling subsystems. Additional consideration of the shop logistics system including task assignment of automated guided vehicles (AGVs) can improve shop productivity while ensuring the smooth running of the whole manufacturing system. Taking into account AGV transportation, this paper studies a green multi-objective IPPS problem considering logistics system (MO_IPPS_LS) to minimize the maximum completion time and energy consumption simultaneously. A multi-population co-evolutionary algorithm (MPCEA) is proposed with a novel integrated encoding method. The co-evolutionary framework can ensure the diversity of the populations through a backtracking mechanism and different evolutionary operators including modified critical-path based local searching. The proposed MPCEA is tested on the open benchmarks with different proportions of transport resources. The comparative experimental results show the effectiveness and superiority of MPCEA for solving MO_IPPS_LS problem.