A Robust Competitive Global Supply Chain Network Design under Disruption: The Case of Medical Device Industry

In this paper, a comprehensive model is proposed to design a Global Supply Chain (GSC) for a medical device manufacturer under disruption in the presence of pre-existing competitors and price inelasticity of product demand. Therefore, a static competition between the distributors facilities to more efficiently gain a further market share under an Economic Cooperation Organization Trade Agreement (ECOTA) is considered. This competition condition is affected by disruption occurrence. The aim of the proposed model is to maximize the expected net after-tax profit of GSC under disruption and normal situation at the same time. To effectively deal with disruption, some restorative strategies are adopted in the design of GSC network. In addition, an uncertainty of the business environment is modeled using a robust optimization technique based on a concept of uncertainty budget. To tackle the proposed Mixed-Integer Nonlinear Programming (MINLP) model, a hybrid Taguchi-based Memetic Algorithm (MA) with an adaptive population size is developed that incorporates a customized Adaptive Large Neighborhood Search (ALNS) as its local search heuristic. A fitness landscape analysis is used to improve the systematic procedure of neighborhood selection in the proposed ALNS. A computational results illustrate the efficiency of the proposed model and solution algorithm in dealing with global disruptions under uncertainty and competition pressure.