Applying GA-VNS approach to supply chain network model with facility and route disruptions

Abstract

In this paper, a supply chain network (SCN) model which simultaneously considers the disruption risks of facility and route is proposed. As most of conventional studies have focused either on facility disruption solely or on route disruption solely, simultaneously considering the disruption risks of facility and route in the SCN model can reinforce the efficiency and stability for its implementation. The SCN model with the disruption risks is formulated as a nonlinear 0–1 integer programming model, and a hybrid metaheuristic (pGA-VNS) approach which combines genetic algorithm (GA) with variable neighborhood search (VNS) is used for solving the nonlinear 0–1 integer programming model. In numerical experiment, various-sized SCN models with the disruption risks at each stage are presented and they are used for comparing the performance of the pGA-VNS approach with those of some conventional approaches (GA and VNS as single metaheuristic approaches and various GA-VNSs as hybrid metaheuristic approaches). Experimental results show that the pGA-VNS approach outperforms conventional GA, VNS and GA-VNS approaches, and the efficiency and stability of the SCN model with the disruption risks are also proved.