Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach

In recent years, many of the developments in logistics are connected to the need of information of efficient supply chain flow. An important issue in the logistics system is to find the network strategy that can give the least cost of the physical distribution flow. In this paper, we consider the logistic chain network problem formulated by 0–1 mixed integer linear programming model. The design tasks of this problem involve the choice of the facilities (plants and distribution centers) to be opened and the distribution network design to satisfy the demand with minimum cost. As the solution method, we propose the spanning tree-based genetic algorithm by using Prüfer number representation. We design the feasibility criteria and develop the repairing procedure for the infeasible Prüfer number, so that it can work for relatively large size problems. The efficacy and the efficiency of this method are demonstrated by comparing its numerical experiment results with those of traditional matrix-based genetic algorithm and professional software package LINDO.