Designing and planning of closed-loop supply chains for risk and economical optimization

Abstract

Markets increasing competition, coupled with a growing concern towards environment, have created the need to invest in the design and operation optimization of supply chains in order to reduce their ecological footprint while minimizing costs and risks. Design and planning decisions should be considered simultaneously with reverse logistics activities and the supply chain should be seen as a closed loop system. Furthermore, these decisions are often subject to different sources of uncertainty. In this paper, a mixed integer linear programming (MILP) formulation is developed for the design and planning of supply chains with forward and reverse flows, with the goal of maximizing the expected Net Present Value (NPV) and simultaneously minimize the risk, taking into account products demand uncertainty. The optimal solutions of the bi-objective model are drawn in a Pareto curve obtained through the σ-constraint method. The model was applied to a case study where the establishment of a European supply chain is studied.