Design of Supply Chain Networks under Risk and Disruptions

Abstract

Supply Chain Network design involves strategic decisions on the location of production plants, distribution centres, capacities and transportation quantities. Supply chains are subjected to different types of disruptions. Supply chain disruptions are unplanned and unanticipated events that disrupt the normal flow of goods and materials within a supply chain and, as a consequence, expose firms within the supply chain to operational and financial risks. In this research, two types of disruptions are considered capacity disruptions and demand disruptions. Each of these disruptions is considered separately and then a simulation based model considered the two types simultaneously. The first proposed model considers the effect of capacity disruptions on supply chain design. This model is formulated as a Genetic Algorithm (GA). The GA generates different designs of the supply chain network from the potential plants and distribution centres. For each design, Integer Linear Programming (ILP) is used to find the optimum links and quantities of products flowing through them with the objective of minimizing the design cost. Each plant has a failure probability. This model suggests after disruption event recovery plan based on using regular capacities of other plants or overtime to compensate as much as possible for the lost production. The second model is a developed Multi-objective Genetic Algorithm (MGA) which is developed to obtain a Robust SCND that considers total and partial failure of the production plants. The production plants are modulated and the failure probability of each module is considered. The MGA model is able to quantify the disruption cost and measure the robustness of the networks and the trade-off between the SC cost and the robustness of the network. In the Third proposed model the disruptions in the demand side are considered. A robust optimization approach is developed for the supply chain network design under demand uncertainty. The supply chain problem considered is a multi-product multi-period multi-echelon. The problem is formulated as a multi-objective model and solved using Goal programming (GP). The objectives are to maximize contribution, minimize the investment and disruptions costs. Instalment of production modules incrementally based on the demand at each planning period was considered.