Developing an integrated decision making model in supply chain under demand uncertainty using genetic algorithm and network data envelopment analysis

Abstract

Nowadays, organisations have recognised the importance of integrated decision making to improve supply chain performance. Since organisations cooperate with each other as a network, any ineffectiveness and inefficiency will be getting more highlighted and integration has become more important. This research describes a four echelon supply chain including supplier, producer, distributor and customer levels. The considered problem is a location routing inventory problem with uncertain demand. To validate integrated mathematical model several problems have been generated and solved using GAMS software. Results show solving time increases exponentially as problems dimension increases, which represents problem's complexity. Therefore, a heuristic genetic algorithm base on NDEA selection method is proposed. To evaluate proposed algorithm's effectiveness, generated problems have been solved by proposed method and three famous selection methods. Obtained results are compared by Wilcoxon test which represents the proposed algorithm's effectiveness.