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Integrating drivers' differences in optimizing green supply chain management at tactical and operational levels

机译：整合驾驶员的差异，在战术和运营层面优化绿色供应链管理

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摘要

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.
机译：在过去的几十年中，绿色供应链管理（GSCM）得到了研究人员的极大关注。但是，尽管在设计和管理绿色供应链（GSC）方面人为因素（例如驾驶员的差异）的影响被忽略，尽管它们在采用和实现最佳绿色战略中至关重要。在本文中，开发了一种新颖的方法来整合驾驶员的差异，以检查驾驶员对燃油消耗和CO_2排放的影响，以绿色驾驶指数（GDI）表示，以在战术和运营管理层面优化绿色供应链。更具体地说，提出了一种更现实的混合整数非线性规划模型，以处理驾驶员，驾驶员和车辆类型不同的多站点，多产品和多时期的总体生产计划（APP）设置。提出的模型旨在最大程度地减少整个供应链的总成本和CO_2排放量。而且，其目的在于得出车辆，驾驶员和目的地之间的最佳分配，以及对选定驾驶员的最佳选择和培训。开发了两种解决方案。具体而言，第一种方法在考虑约束条件中的温室气体（GHG）限制的同时，将整个供应链总成本的单个目标函数最小化，而第二种方法则将双目标函数（总成本和GHG）最小化。进行了数值研究和敏感性分析，以确认对两种提议配方的验证。结果表明，随着CO_2排放允许限值变得更加严格，该模型选择了具有更高GDI的驱动因素。结果表明，应在GSCM中考虑驾驶员的差异，从而以最小的成本和最小的CO_2排放量制定切实可行的计划。

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