Scheduling of remanufacturing systems based on meta-heuristic methods
Creators: Zhang, Wenkang
Publisher: name: University of Alberta Library
Year: 2023

<p>In recent decades, product remanufacturing has been increasingly promoted as a life-cycle approach because of the heightened energy issues generated by the fast expansion of industrial civilization. Remanufacturing is a synthetical and stringent production strategy for making the quality, appearance, reliability, and service performance of end-of-life products equal (to) or better than those of newly manufactured items. Using a combination of the sequential remanufacturing macroscopic phases including initial inspection, disassembly, reprocessing, reassembly, and final testing, the remanufactured products can have extended life cycles with incremental upgrades. Scheduling is always considered as an essential tool to improve the production efficiency of remanufacturing systems. However, due to the higher inherent complexity, flexibility, and uncertainty of remanufacturing systems compared to traditional manufacturing systems, the research on the scheduling issues in remanufacturing systems is highly challenging.<br>
Therefore, the proposed study aims to address the scheduling problems for centralized and decentralized remanufacturing systems by developing high-efficient meta-heuristic methods. For centralized remanufacturing systems, the scheduling problem and the integrated process planning and scheduling problem for a flexible job-shop-type remanufacturing system incorporating disassembly, reprocessing, and reassembly shops are solved to minimized the makespan of the studied remanufacturing system. Then, the energy-efficient multi-objective optimization for these two problems is carried out to optimize the makespan and energy consumption at the same time. For decentralized remanufacturing systems, the multi-phase integrated scheduling problem for the cloud remanufacturing system containing disassembly, inspection, reprocessing, reassembly, and testing is proposed. Then, the multi-task scheduling for the cloud remanufacturing system integrating reuse, reprocessing, and replacement under quality uncertainty is addressed.<br>
To solve the proposed scheduling problems, the frameworks for both centralized and decentralized scheduling problems are constructed first. The corresponding mathematical models are then established to characterize these problems and to guide the algorithmic design. The meta-heuristic algorithms with improvement strategies, such as float-number-based solution representation method, local neighborhood searching strategy, nonlinear strategy, and dynamic adaptive inertia weight strategy, are developed to solved the studied scheduling problems efficiently. A series of experiments and different real-world case studies are designed and implemented to evaluate the effectiveness of the developed single-objective and multi-objective algorithms. <br>
The contributions of this study lay the theoretical foundation of efficient problem-solving and scientific solution methods for the scheduling problems in remanufacturing systems. This study also provides useful algorithms to managers and engineers who face challenging scheduling problems in the actual remanufacturing environment. These meta-heuristic algorithms can help managers make better decisions about arranging remanufacturing jobs and allocating resources more efficiently. The proposed research can be extended to investigate the scheduling problems for remanufacturing systems considering more uncertain factors and dynamic events in future work.</p>