A multi-criteria optimization model for planning of a supply chain network under demand uncertainty

Abstract

We consider the planning of a multi-product, multi-period, and multi-echelon supply chain network that consists of several existing plants at fixed places, some warehouses and distribution centers at undetermined locations, and a number of given customer zones. Unsure market demands are taken into account and modeled as a number of discrete scenarios with known probabilities. The supply chain planning model is constructed as a mixed-integer linear programming (MILP) problem to satisfy several conflict objectives, such as minimizing the total cost, raising the decision robustness to different product demand scenarios, lifting the local incentives, and reducing the total transport time. For purpose that a compensatory solution among all participants of the supply chain can be achieved, a two-phase fuzzy decision-making method is presented and, by means of application of it to a numerical example, proved effective in providing a compromised solution in an uncertain multi-echelon supply chain network.