A fast efficient local search-based algorithm for multi-objective supply chain configuration problem

Zhang, Xin ORCID: 0000-0003-3636-6453, Zhan, Zhi-Hui ORCID: 0000-0003-0862-0514 and Zhang, Jun ORCID: 0000-0001-7835-9871 (2020) A fast efficient local search-based algorithm for multi-objective supply chain configuration problem. IEEE Access, 8. pp. 62924-62931. ISSN 2169-3536


Supply chain configuration (SCC) plays an important role in supply chain management. This paper focuses on a multi-objective SCC (MOSCC) problem for minimizing both the cost of goods sold and the lead time simultaneously. Some existing population-based methods use the evolution of a population to obtain the optimal Pareto set, but they are time-consuming. In this paper, an Efficient Local Search-based algorithm with rank (ELSrank) is designed to solve the MOSCC problem. Firstly, instead of use of population, two solutions (xA and xB) are generated by the greedy strategy, which have the minimal cost and the minimal time, respectively. They approximately locate in two sides of the Pareto front (PF). Secondly, with the consideration of the problem characteristics, a local search (LS) is proposed to find competitive solutions among the common neighborhood of two given solutions. If xA and xB are chosen to execute the proposed LS, solutions along the link path (the approximate PF) of xA and xB can be found. This way, the solutions along the whole PF can be found. The comparative experiments are conducted on six instances from the real-life MOSCC problems, and the results show that ELSrank performs better than other start-of-the-art algorithms, especially on the large scale problem instances.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/46347
DOI 10.1109/ACCESS.2020.2983473
Official URL https://ieeexplore.ieee.org/document/9047897
Subjects Current > FOR (2020) Classification > 4602 Artificial intelligence
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords supply chain, supply chain management, cost of goods lead time, algorithms
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