Remanufacturing is considered as one of the major technologies for extending the life cycle and improving the remaining value of end-of-life (EoL) products. Different from the traditional common manufacturing process, the remanufacturing process has more influential factors due to the uncertain condition and complex physical structure of the EoL products. These factors will affect the planning process of feasible process routes and the scheduling process of machines allocation. Moreover the process planning and scheduling are related and interact with each other. Therefore, it is well worth researching the integrated process planning and scheduling problem in remanufacturing (IPPSR). This paper proposes the simulated annealing-based hyper-heuristic algorithm for solving the IPPSR under stochastic process time. Firstly, the mathematical model for IPPSR is proposed with the basic assumptions and notations. Then, taking GA, NSGA2 and SPEA2 as low-level heuristic algorithms to determine the optimal process routes under the task precedence constraints. Simultaneously, construct the simulated annealing as the high-level heuristic algorithm for achieving the minimum makespan of the scheduling process. Next, the performance of the proposed algorithm is validated through the comparison with the open-source dataset and algorithms. Our proposed algorithm can achieve the lowest makespan with lower iterations. Finally, the future research directions and challenges are discussed.
|Title of host publication||2022 27th International Conference on Automation and Computing (ICAC)|
|Publication status||Published - 10 Oct 2022|
|Event||2022 27th International Conference on Automation and Computing (ICAC) - Bristol, United Kingdom|
Duration: 1 Sept 2022 → 3 Sept 2022
|Conference||2022 27th International Conference on Automation and Computing (ICAC)|
|Period||1/09/22 → 3/09/22|
Bibliographical noteFunding Information:
Link to open-source and experiment benchmark data: https://github.com/mcfadd/Job Shop Schedule Problem https://github.com/jMetal/jMetalPy VII. ACKNOWLEDGEMENT The authors acknowledge the financial support of EU H2020 Project RECLAIM (RE-manufaCturing and Refurbishment LArge Industrial equipment) (No. 869884).
© 2022 IEEE.
- Hyper-heuristic algorithm
- integrated process planning and scheduling
- simulated annealing algorithm