A variable neighborhood search with an effective local search for uncapacitated multilevel lot-sizing problems

Yiyong Xiao, Renqian Zhang, Qiuhong Zhao, Ikou Kaku and Yuchun Xu

European Journal of Operational Research, 2014, vol. 235, issue 1, 102-114

Abstract: In this study, we improved the variable neighborhood search (VNS) algorithm for solving uncapacitated multilevel lot-sizing (MLLS) problems. The improvement is twofold. First, we developed an effective local search method known as the Ancestors Depth-first Traversal Search (ADTS), which can be embedded in the VNS to significantly improve the solution quality. Second, we proposed a common and efficient approach for the rapid calculation of the cost change for the VNS and other generate-and-test algorithms. The new VNS algorithm was tested against 176 benchmark problems of different scales (small, medium, and large). The experimental results show that the new VNS algorithm outperforms all of the existing algorithms in the literature for solving uncapacitated MLLS problems because it was able to find all optimal solutions (100%) for 96 small-sized problems and new best-known solutions for 5 of 40 medium-sized problems and for 30 of 40 large-sized problems.

Keywords: Metaheuristics; Multilevel lot-sizing (MLLS) problem; ADTS local search; Variable neighborhood search (VNS) (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:235:y:2014:i:1:p:102-114

DOI: 10.1016/j.ejor.2013.10.025

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