A Proposal of Resilient Supply Chain Network Planning Method with Two-stage Stochastic Programming and Latin Hypercube Sampling

The importance of risk management has been pointed out in supply chain management which stably supplies products with considering economic efficiency. In particular, supply chain disruptions caused by events with uncertain timing and scale, such as earthquakes and infectious disease pandemics, have sometimes caused great damage even to large companies. In this paper, we propose a planning method of resilient supply chain networks using two-stage stochastic programming and Latin hypercube sampling in order to determine appropriate material suppliers, manufacturers and wholesalers and their inventory levels in a risk-aware and efficient computational time in consideration of disruption risks. In the computational experiments, the optimality and robustness of the proposed method are evaluated.