Integrating drivers’ differences in optimizing green supply chain management at tactical and operational levels
- Citation data:
Computers & Industrial Engineering, ISSN: 0360-8352, Vol: 112, Page: 122-134
- Publication Year:
- Computer Science; Engineering
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Green Supply Chain Management (GSCM) has attained a huge attention by researchers in the last few decades. However, the effect of human aspects (e.g. drivers’ differences) in designing and managing green supply chains (GSCs) has been ignored despite the fact of their crucial importance in adopting and achieving optimal green strategies. In this paper, a novel approach is developed for integrating drivers’ differences to examine their effect on fuel consumption and CO 2 emissions, denoted by a Green Driving Index (GDI), in optimizing green supply chain at 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 with different levels of drivers and different types of vehicles. The proposed model aims to minimize the total cost and CO 2 emissions across the supply chain. Also, it aims to derive optimal assignments between vehicles, drivers, and the destinations as well as an optimal selection and training of the selected drivers. Two formulations of the problem are developed. Specifically, the first formulation minimizes a single objective function of total costs across the supply chain while considering the greenhouse gas (GHG) limits in the constraints whereas the second formulation minimizes a bi-objective function (total costs and GHG). A numerical study and sensitivity analyses are conducted to confirm the verification of the two proposed formulations. The results demonstrate that as CO 2 emissions allowable limits become stricter, the model selects drivers having higher GDIs. The results indicate that the drivers’ differences should be considered in GSCM to generate realistic plans with minimum costs and minimal CO 2 emissions.