A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks

Abstract

The optimal design of a supply chain network is a challenging problem, especially for large networks where there are multiple objectives. Such problems are usually formulated as mixed integer programs. Solving this type of network design problem takes a long time using exact algorithms and for large-scale problems it is not even possible. This has given rise to the use of meta-heuristic techniques. In this paper, an effective tabu search algorithm for solving multi-product, multi-objective, multi-stage supply chain design problems is proposed. The desirable characteristics of the algorithm are developed, coded and tested. The results of the developed algorithm are compared with the results obtained by an improved augmented ε-constraint algorithm embedded in the General Algebraic Modeling System (GAMS) software for small-scale, medium-scale, and large-scale instances of multi-objective supply chain problems. Experimental results have shown that the developed algorithm is capable of obtaining high quality solutions within a short computation time, in addition to performing well in other measures such as solution diversity.