Location and allocation decisions in a two-echelon supply chain with stochastic demand – A genetic-algorithm based solution

This study addresses a facility location and task allocation problem of a two-echelon supply chain against stochastic demand. Decisions include locating a number of factories among a finite set of potential sites and allocating task assignment between factories and marketplaces to maximize profit. The study represents the addressed location–allocation problem by bi-level stochastic programming and develops a genetic algorithm with efficient greedy heuristics to solve the problem. The contribution of the study pivots on a formal representation of system configuration design and operations optimization for a two-echelon supply chain. The proposed solution algorithm can find near optimal solution while consuming less computational time for large-size problems as compared to an optimization-based tool. In addition, this study investigates the industrial-cluster effect in a two-echelon supply chain by using the proposed algorithm. Experiments reveal that the proposed algorithm can efficiently yield nearly optimal solutions against stochastic demands.