Carbon market sensitive robust optimization model for closed loop supply chain network design under uncertainty

Abstract

The adoption of closed-loop supply chain (CLSC) network is one of the effective approaches to reduce carbon emissions. In current globalization, inherent uncertainty exists in business environment so there is a need to be design robust supply chains. This paper proposes a deterministic mixed integer linear programming (MILP) model integrating economics and carbon emission considerations including selection of production technologies and transportation mode as a part of CLSC network strategic and tactical decisions. The robust counterpart of the proposed deterministic model is developed based on three alternative uncertainty sets to represent the imprecise input parameters. The robust counterpart is used to study the supply chain performance by considering the two most globally practiced carbon regulatory policies; carbon tax policy and carbon trading policy. Numerical results show that total cost of the proposed robust optimization model under each uncertainty set is greater than the total cost of deterministic model. The additional cost is due to solution space of each uncertainty set to accommodate any uncertainty level. As uncertainty level increases the overall supply chain cost worsen. Moreover, the results suggest that carbon tax rate has direct relation with overall supply chain cost whereas having carbon market trading flexibility in carbon trading policy, this policy is more efficient policy as compared to carbon tax policy. Furthermore, the proposed robust optimization model is useful for mangers to achieve not only a robust supply chain network design which can withstand any possible uncertainty level but also significant reduction in carbon emissions by choosing suitable carbon-efficient policy.