Abstract
This paper researches on the remanufacturing rescheduling problems (RRP) for new job insertion. The objective is to minimize the total flow time and the instability at the same time. A bi-objective function is developed for RRP and water cycle algorithm (WCA) is employed and improved to solve the problem. A discretization strategy is proposed to make the WCA applicable for handling the RRP. An ensemble of local search operators is developed to improve the performance of the discrete WCA (DWCA) algorithm. Six real-life remanufacturing cases with different scales are solved by DWCA. The results and comparisons indicate the superiority of the proposed DWCA scheme over the famous bi-objective algorithm, NSGAII.
Keywords
This work was supported by National Nature Science Foundation under Grant 61603169, 61374187. This work was conducted within the Delta-NTU Corporate Lab for Cyber-Physical Systems with funding support from Delta Electronics Inc and the National Research Foundation (NRF) Singapore under the Corp Lab@University Scheme.
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Gao, K., Duan, P., Su, R., Li, J. (2017). Bi-objective Water Cycle Algorithm for Solving Remanufacturing Rescheduling Problem. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_54
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