Low carbon chance constrained supply chain network design problem: a Benders decomposition based approach

Abstract

This paper proposes a chance constrained based green supply chain network design model addressing carbon emissions and carbon trading issues. The model determines the optimal flow of materials as well as emissions across the supply chain network. The basic model has been further extended into two models addressing different carbon emission issues. This study has contributed to the body of existing green supply chain literature through addressing uncertainties of suppliers’ capacities, plants’ capacities, warehouses’ capacities and demand for sustainable supply chain network design problem. This study applies Benders decomposition algorithm to handle chance constrained sustainable supply chain network design problem. The proposed models are illustrated with suitable examples and results are carefully analyzed and discussed. The results demonstrated that the flow of materials across the supply chain network varies with the change of the probability as well as carbon credit price. The number of openings of the plants is also influenced with the change of carbon credit price. Similarly, variable cost and variable emissions have been found increased and decreased, respectively with the increase of carbon credit price for the base model. The model is also equipped with dissimilar carbon prices for handling cap and trade scenario. This paper may help managers to deal uncertainties as well as managing emissions of a supply chain network.