Robust optimization for closed-loop supply chain network design considering carbon policies under uncertainty

Abstract

Climate change attributed by greenhouse gas emissions have triggered some countries to implement various carbon regulatory mechanisms to curb and regulate industrial carbon emissions. To be effective, the industry environmental footprint needs to address holistically via closed-loop supply chains (CLSC). This research proposes an optimization approach to address CLSC design problem with carbon footprint considerations. It integrates the carbon emission policies into supply chain's strategic, tactical and operational selection decisions. A robust counterpart of the proposed model is developed based on three alternative uncertainty sets. The model extends further to investigate the impact of the three commonly practiced carbon regulatory policies including carbon cap, carbon tax, and carbon cap-And-Trade on the supply chain strategic and operational decisions. Numerical results indicate that carbon cap-And-Trade policy is the most flexible and efficient policy as compared to carbon cap and carbon tax policies. This study provides insightful observations with respect to robust optimization, CLSC network decisions, and carbon emissions. The proposed robust optimization models could be useful to decision-makers to achieve a robust supply chain network which can withstand any possible uncertainty in a given uncertainty set.