Multi-objective decision model for green supply chain management

• Published in: Cogent business & management Vol. 7; no. 1; pp. 1 - 33
• Main Authors: Balasubramanian, Sreejith, Shukla, Vinaya, Rosas, Jose-Saavedra
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 2020; Cogent; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Costs; GHG emissions; green supply chain management; Greenhouse effect; Hierarchies; Linear programming; multi-objective optimization; rubber industry; Supply chain management; Supply chains
• ISSN: 2331-1975; 2331-1975
• DOI: 10.1080/23311975.2020.1783177

In this paper, a multi-objective linear programming model was developed which sought to simultaneously optimize total costs and total GHG emissions for the Thai Rubber supply chain. The model was solved by the ε -constraint method which computed the Pareto optimal solution. Each point in the Pareto set entailed a different design of quantity of rubber product flow between the supply chain entities and transport modes and routes. The result obtained show the trade-offs between costs and GHG emissions. It appears that improvements in cost reductions are only possible by compromising on and allowing for higher GHG emissions. From the Pareto set of solutions, each point is equally effective solution for achieving significant cost reductions without compromising too far on GHG emissions. Scenarios analysis were considered to examine the impact of transportation and distribution restructuring on the trade-off between GHG emissions and costs vis-à-vis the baseline model. Overall, the model developed in this research, together with its Pareto optimal solutions analysis, shows that it can be used as an effective tool to design a new and workable GSCM model for the Thai Rubber industry.