An energy-efficient multi-objective scheduling for flexible job-shop-type remanufacturing system

This work considers a multi-objective scheduling issue for the remanufacturing system (RMS) including parallel disassembly/reassembly workstations and flexible job-shop-type reprocessing shops with the purpose of reducing completion time and energy usage by deciding the allocation/sequence of disassembly/reassembly jobs and determining the operation sequencing and workstation assignment of reprocessing jobs. A multi-objective mixed-integer programming model is first constructed to describe this scheduling problem mathematically. An improved grey wolf optimization (IGWO) algorithm with the global criterion (GC) multi-objective method and integrated float-number solution representation scheme is introduced to realize high-efficient scheduling. Various local neighborhood search strategies, random disturbance methods, and weighted distance updating mechanisms are integrated into IGWO to enhance its performance. A series of numeral instances are systematically designed and implemented to validate the effectiveness of IGWO. Finally, a case study is deployed to evaluate IGWO’s capability to address the practical remanufacturing scheduling problem. Experimental results reveal that the developed IGWO performs better than other existing methods in terms of solution accuracy, computing speed, solution stability, and convergence performance. Furthermore, the results of case study demonstrate IGWO’s superiority in solving the real-world remanufacturing scheduling problem in lower energy usage and time cost.