Electric versus diesel: Green supply chain network design with carbon footprint labeling

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
Ensieh Ghaedyheidary, Samir Elhedhli
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引用次数: 0

Abstract

Transportation electrification and carbon footprint labeling are strong indicators of environmental commitment in green supply chains. We study this framework and assess its environmental and financial sustainability. We consider cradle-to-gate operations, from manufacturing to retail, mandate the use of electric trucks whenever their range allows, and impose a cap on product carbon footprints as would be advertised on a carbon label. We optimize the locations of distribution centers, the allocation of demand, and the transportation choices between diesel trucks and electric trucks. We account for the nonlinear relationship between emissions and payload for diesel trucks, focus on two representative functional forms- concave and convex- and propose a mixed-integer nonlinear optimization model to minimize costs and CO2-equivalent emissions. We use Lagrangean relaxation to decompose the model by echelon and isolate the convex and concave nonlinearity in an easy-to-solve subproblem. We then design a Lagrangean heuristic based on the solution of one of the subproblems, which has proven efficient and near-optimal. Based on a case study, we evaluate the impact of the emission function and the carbon label on the supply chain network, as well as the trade-off between the use of diesel trucks and electric trucks. We find that the relationship between emissions and payload for diesel trucks significantly influences the adoption of electric trucks. When concave, as would be the case for steady driving conditions, long hauls, well-maintained infrastructure, and light traffic, conventional diesel trucks continue to be the cost-efficient option, especially when electric truck mileage costs are high and the cap on unit emissions is elevated. In contrast, when diesel emissions are convex, corresponding to challenging driving conditions such as urban delivery, congested road networks, stop-and-go traffic, and degraded road infrastructure, transportation emissions dominate total emissions, diesel truck usage decreases, and electric trucks become a better choice even if the cap on unit emissions is high and diesel trucks are cheaper to operate. Furthermore, extending the range of electric trucks increases their usage under convex emissions but not under concave emissions, especially when the cap on carbon footprint is not tight.
电动与柴油:绿色供应链网络设计与碳足迹标签
交通电气化和碳足迹标签是绿色供应链中环境承诺的有力指标。我们研究这一框架,并评估其环境和财务可持续性。我们考虑从生产到零售,从摇篮到大门的运营,只要范围允许,就强制使用电动卡车,并按照碳标签上的广告规定,对产品的碳足迹设定上限。我们优化配送中心的位置,需求分配,以及柴油卡车和电动卡车之间的运输选择。我们考虑了柴油卡车排放与有效载荷之间的非线性关系,重点关注两种具有代表性的函数形式——凹函数和凸函数——并提出了一个混合整数非线性优化模型,以最小化成本和二氧化碳当量排放。利用拉格朗日松弛法对模型进行阶梯形分解,将凸、凹非线性分离为易解子问题。然后,我们基于其中一个子问题的解设计了一个拉格朗日启发式算法,该算法被证明是有效的和接近最优的。基于一个案例研究,我们评估了排放函数和碳标签对供应链网络的影响,以及使用柴油卡车和电动卡车之间的权衡。我们发现柴油车排放与有效载荷之间的关系对电动卡车的采用有显著影响。在凹面情况下,如稳定的驾驶条件、长距离行驶、维护良好的基础设施和交通量较少的情况下,传统柴油卡车仍然是经济高效的选择,尤其是在电动卡车行驶里程成本高、单位排放上限提高的情况下。相反,当柴油排放呈凸形时,对应于城市交付、道路网络拥堵、走走停停、道路基础设施退化等具有挑战性的驾驶条件,交通运输排放占总排放的主导地位,柴油卡车的使用减少,即使单位排放上限较高,柴油卡车的运营成本也较低,电动卡车成为更好的选择。此外,扩大电动卡车的行驶里程会增加其在凸排放下的使用量,而在凹排放下则不会增加,尤其是在碳足迹上限不严格的情况下。
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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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