An energy-efficient multi-objective scheduling for flexible job-shop-type remanufacturing system. (February 2023)

• Record Type: Journal Article
• Authors: Zhang, Wenkang; Zheng, Yufan; Ahmad, Rafiq
• Is Part Of: Journal of manufacturing systems. Volume 66(2023)
• Page Start: 211
• Page End: 232
• Publication Date: 2023-02
• Subjects: Remanufacturing system -- Job-shop-type reprocessing shop -- Energy consumption -- Flexible scheduling -- Improved grey wolf optimization; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2022.12.008
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract: 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. Highlights: An energy-aware scheduling problem of remanufacturing systems with flexible job-shop-type reprocessing shop is proposed. A mixed-integer programming model is established to characterize the energy-aware scheduling problem. An improved grey wolf optimization algorithm with upgraded population updating mechanisms is developed for this problem. Experiments and a real-world case study are performed to demonstrate the effectiveness of the proposed algorithm.