Optimization Models in Green Supply Chain Management

Green Supply Chain (GSC) has attained a huge attention by researchers in the last few decades, but the effect of human aspects in design and managing GSCs has been ignored. In this research, we develop a novel approach for integrating drivers’ differences to examine their effect on fuel consumption and CO2 emissions in optimizing green supply chain in the tactical and operational management levels. More specifically, a more realistic mixed integer nonlinear programming model is proposed to deal with multi-site, multi-product, and multi-period Aggregate Production Planning (APP) setting while considering different levels of drivers and different types of vehicles. The model aims to minimize the total cost and CO2 emissions across the supply chain. In addition, it aims to derive an assignment between vehicles, drivers, and the destinations as well as an optimal selection and training of drivers. A numerical study is conducted to confirm the verification of the proposed model. The results of conducting sensitivity analysis demonstrated that, after considering green issues the total cost across the supply chain was increased. And the number of drivers for each level varies with different CO2 emission level, so the CO2 emission level that the company wants to achieve depends on the level of drivers' available. Also the assignments between vehicles and drivers vary with different CO2 emission levels and different distances.