A Fast Efficient Local Search-Based Algorithm for Multi-Objective Supply Chain Configuration Problem

Abstract

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 $\boldsymbol {E}$ fficient $\boldsymbol {L}$ ocal ${S}$ earch-based algorithm with rank (ELSrank) is designed to solve the MOSCC problem. Firstly, instead of use of population, two solutions ( $\boldsymbol {x} _{{\boldsymbol{A}}}$ and ${x}_{{\boldsymbol{B}}}$ ) 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 $\boldsymbol {x} _{{\boldsymbol{A}}}$ and $\boldsymbol {x} _{{\boldsymbol{B}}}$ are chosen to execute the proposed LS, solutions along the link path (the approximate PF ) of $\boldsymbol {x} _{{\boldsymbol{A}}}$ and $\boldsymbol {x} _{{\boldsymbol{B}}}$ 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.