A New Method for Supply Chain Optimization with Facility Fail Risks

Supply chain optimization models typically assume that facilities never fail. However, in the real world cases facilities are always subject to disruptions of various sorts due to natural disasters, strikes, machine breakdowns, power outages, and other factors. This paper investigates an integrated supply chain optimization problem that optimizes facility locations, customer allocations, and inventory management decisions when facilities are subject to disruption risks. When a facility fails, its customers may be reassigned to other operational facilities in order to avoid the high penalty costs associated with losing service. The problem is formulated as a mixed integer nonlinear programming to minimize the sum of the expected total costs. The model simultaneously determines the location of distribution centers and the allocation of disruption affected customer to distribution centers. In order to solve the proposed model, an effective solution approach based on genetic algorithm is presented. Finally, computational results for several instances of the problem are given to validate the effectiveness of the proposed model and algorithm.