Multi-task scheduling in cloud remanufacturing system integrating reuse, reprocessing, and replacement under quality uncertainty

This study focuses on the multi-task scheduling problem for the cloud remanufacturing system incorporating reuse, reprocessing, and replacement operations considering quality uncertainty. A framework of the cloud remanufacturing under quality differences is first constructed, and a mathematical model is then established to characterize the multi-task scheduling problem in the cloud platform. In this framework, five quality grades are considered, and each grade is assigned a specific remanufacturing line. To efficiently address this problem, the nonlinear grey wolf optimization (NLGWO) algorithm with a hybrid sequential solution representation for remanufacturing line selection, task sequencing, and service searching and matching is introduced. Additionally, the nonlinear strategy and the crisscross optimization method are embedded in the NLGWO to balance the exploration and exploitation capabilities while enhancing its population updating mechanisms. To evaluate the proposed method, a real-world case study is designed and implemented under actual remanufacturing conditions. Three different optimization problems, including makespan optimization, cost optimization, and multi-objective optimization problems, are addressed using the NLGWO and its four baseline meta-heuristic methods. The computational results demonstrate that the NLGWO can efficiently solve all the optimization problems in the case study, outperforming its baseline algorithms in terms of solution accuracy, computing speed, and convergence performance.