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Returnable-after-deep-trial policies in e-commerce supply chains with opportunistic consumers 有机会主义消费者的电子商务供应链中的深度试后退货政策
IF 8.8 1区 工程技术 Q1 ECONOMICS Pub Date : 2026-02-09 DOI: 10.1016/j.tre.2026.104728
Zhengzheng Chen, Tiaojun Xiao
In response to consumer desire for a deep experience of product performance, some online sellers have strengthened their traditional money back guarantee by implementing the “returnable after a deep trial” (RADT) policy. But such a policy has also encouraged opportunistic behavior. Considering such opportunistic behavior for consumers, we build an e-commerce supply chain model to investigate the price decisions and the return strategy (implementing RADT or not) of online sellers under two selling modes: agency selling and reselling. We show that the RADT is often expensive for consumers. Besides, counterintuitively, when the online seller allows for an increased degree of deep trial, the seller is highly likely to decrease the retail price and the refund amount, simultaneously. Moreover, opportunistic consumers also have strategic value: although consumers’ opportunistic behavior under the RADT seemingly harms the online seller, in fact, the seller may generate profits by strategically encouraging opportunistic behavior. Furthermore, under agency selling, when consumers’ benefit from opportunistic behavior is not extremely small, and most ordinary consumers directly return the product, the RADT is optimal; while under reselling, the RADT is optimal if most ordinary consumers directly return the product. Surprisingly, more opportunistic consumers increase the online seller’s incentive to adopt the RADT policy.
为了满足消费者对产品性能深度体验的需求,一些网络卖家通过实施“深度试用后退货”(RADT)政策,加强了传统的退款保证。但这样的政策也鼓励了机会主义行为。考虑到消费者的这种机会主义行为,我们建立了一个电子商务供应链模型,研究了代理销售和转售两种销售模式下网络卖家的价格决策和退货策略(是否实施RADT)。我们表明,对于消费者来说,RADT通常是昂贵的。此外,与直觉相反的是,当在线卖家允许深度试用程度增加时,卖家极有可能同时降低零售价格和退款金额。此外,机会主义消费者还具有战略价值:尽管在RADT下消费者的机会主义行为表面上损害了网络卖家,但实际上卖家可能通过战略鼓励机会主义行为而获得利润。此外,在代理销售下,当消费者从机会主义行为中获得的利益不是极小,且大多数普通消费者直接退货时,RADT最优;在转售情况下,如果大多数普通消费者直接退货,RADT是最优的。令人惊讶的是,更多机会主义消费者增加了在线卖家采用RADT政策的动机。
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引用次数: 0
Techno-economic optimization for containership green transition under net-zero emission target: a predict-then-optimize framework 净零排放目标下集装箱船绿色转型的技术经济优化:预测-优化框架
IF 8.8 1区 工程技术 Q1 ECONOMICS Pub Date : 2026-02-09 DOI: 10.1016/j.tre.2026.104731
Leqi Zhou , Jian Gang Jin , Yuling Gao , Çağatay Iris , Tolga Bektaş , Jiang Hang Chen
Shipping companies are under growing pressure to meet net-zero emission target under tightening regulations. To tackle this challenge, this paper extends the techno-economic predict-then-optimize framework to make long-term strategic decisions that jointly minimize greenhouse gas emissions and lifecycle costs over the planning horizon. The framework integrates a vessel-specific fuel consumption prediction model for comprehensive estimation of future emissions, and an optimization model that strategically schedules and combines multiple emission reduction methods. The optimization explicitly accounts for the techno-economic trade-offs among slow steaming, maintenance, fuel switching, and capital investments for retrofits, while ensuring compliance with carbon intensity indicator (CII) regulations and the overall net-zero emission target. A case study on several containerships demonstrates the applicability of the model, with numerical experiments showing that the predict-then-optimize framework achieves a 67% carbon reduction by 2040. Further investigations of the strategies, including illustration of transition plans, comparisons of different strategies, evaluation of marginal costs for each method, and sensitivity analysis on interest rates and regulations, are conducted to provide insights into lifecycle cost-minimizing and emission regulation-compliant strategy design.
在日益严格的监管下,航运公司面临着越来越大的压力,需要实现净零排放目标。为了应对这一挑战,本文扩展了技术-经济预测-然后优化框架,以制定长期战略决策,在规划范围内共同减少温室气体排放和生命周期成本。该框架集成了一个用于综合估计未来排放的船舶特定燃料消耗预测模型,以及一个战略性地安排和组合多种减排方法的优化模型。优化明确考虑了慢速航行、维护、燃料转换和改造资本投资之间的技术经济权衡,同时确保符合碳强度指标(CII)法规和总体净零排放目标。对几艘集装箱船的案例研究证明了该模型的适用性,数值实验表明,预测-然后优化框架到2040年实现了67%的碳减排。本文还对这些策略进行了进一步的研究,包括对过渡计划的说明、不同策略的比较、每种方法的边际成本评估以及对利率和法规的敏感性分析,从而为生命周期成本最小化和符合排放法规的策略设计提供见解。
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引用次数: 0
Recycling strategies under carbon policies with platform financing 碳政策下的平台融资回收策略
IF 8.8 1区 工程技术 Q1 ECONOMICS Pub Date : 2026-02-09 DOI: 10.1016/j.tre.2026.104735
Zhitang Li , Zhifeng Qian , Peng He
Given governmental regulations and environmental repercussions associated with apparel manufacturing and disposal, many manufacturers engage in recycling. Typically, manufacturers either recycle directly from consumers or through retail platforms. However, retail platforms often encounter financial constraints when developing recycling businesses. A key challenge in reverse logistics lies in the efficient and cost-effective collection and processing of used apparel, particularly given the complexities of governmental regulations and financial constraints. Against this backdrop, we develop an apparel closed-loop supply chain model to examine recycling strategies under different carbon policies (e.g., carbon emission reduction subsidies and carbon tax policies) and a hybrid financing scheme that incorporates both equity financing and bank loans. Our key findings are as follows. Firstly, from the perspective of the economic benefits of the supply chain entities, carbon subsidies provide stronger incentives for manufacturers and platforms to recycle more effectively than carbon tax policies. Secondly, in situations where the initial market demand for new products is low and the equity financing ratio is high, firms are inclined to set higher retail and wholesale prices. Thirdly, higher equity financing ratios prompt manufacturers to offer greater recycling incentive prices. Finally, we derive the optimal choice between the government’s carbon emission reduction subsidy and carbon tax policy, and discuss the most effective clothing recycling strategies for manufacturers and retail platforms. In the extended model, we investigate the optimal recycling strategy in the case of equity financing failures.
考虑到与服装制造和处置相关的政府法规和环境影响,许多制造商从事回收利用。通常,制造商要么直接从消费者那里回收,要么通过零售平台回收。然而,零售平台在发展回收业务时经常遇到资金限制。逆向物流的一个关键挑战在于有效和具有成本效益的收集和处理旧服装,特别是考虑到政府法规的复杂性和财政限制。在此背景下,我们开发了一个服装闭环供应链模型,以研究不同碳政策(例如,碳减排补贴和碳税政策)下的回收策略,以及一个结合股权融资和银行贷款的混合融资方案。我们的主要发现如下。首先,从供应链主体的经济效益来看,碳补贴比碳税政策更能有效地激励制造商和平台进行回收。其次,在新产品的初始市场需求较低,股权融资比例较高的情况下,企业倾向于设定较高的零售和批发价格。第三,更高的股权融资比率促使制造商提供更高的回收激励价格。最后,我们推导出政府碳减排补贴与碳税政策之间的最优选择,并讨论了制造商和零售平台最有效的服装回收策略。在扩展模型中,我们研究了股权融资失败情况下的最优回收策略。
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引用次数: 0
Hierarchical planning and scheduling for bulk ports via network flow and deep reinforcement learning-guided constraint programming 基于网络流和深度强化学习引导约束规划的大容量端口分层规划与调度
IF 8.8 1区 工程技术 Q1 ECONOMICS Pub Date : 2026-02-06 DOI: 10.1016/j.tre.2026.104714
Xuan Lu , Yu Zhang , Xuri Xin , Hang Yang , Huanhuan Li , Lanbo Zheng , Zaili Yang
In this research, an integrated inbound and outbound operational planning and scheduling problem is addressed for complex and large bulk ports. The practice of moving homogeneous dry bulk cargoes on a fixed terminal is changing as raw materials of different types are transported from/to the same terminals. It raises a new research challenge where unloading, stacking, reclaiming, conveying and loading operations must be coordinated to import/export blended products according to the tight specifications of customers. This paper aims to maximise resource utilisation and to satisfy demands as early as possible. The essence of the problem is to design the routing of product flows throughout the port logistics network such that supply and demand are matched optimally. This study presents a new framework that enables the modelling of the planning part as a multi-commodity flow problem and the scheduling part as a constraint programming (CP) problem. A novel dual-engine optimisation method that synergistically combines CP with deep reinforcement learning (DRL) is proposed to accelerate the scheduling phase. The method leverages DRL agents to fix key variables, thereby effectively accelerating the optimisation process of the CP solver. Comprehensive numerical experiments are conducted on real data sets as well as instances derived from real scenarios to validate the effectiveness of the proposed approach, demonstrating significant improvements in port scheduling efficiency. Additionally, strategic management analyses offer actionable insights to support decision-making in bulk port operations. The proposed methods provide a generalised methodology adaptable to a broad range of complex combinatorial optimisation problems in port logistics and beyond, paving the way for more intelligent and sustainable dry bulk port management.
在本研究中,针对复杂的大型散装港口,提出了一个综合的进出港作业计划与调度问题。随着不同类型的原材料从同一码头运输到同一码头,在固定码头运输均质干散货的做法正在发生变化。它提出了一个新的研究挑战,卸载、堆放、回收、输送和装载操作必须协调,以进出口混合产品,根据客户的严格规格。本文旨在最大限度地利用资源,尽早满足需求。问题的实质是在整个港口物流网络中设计产品流的路线,使供需最优匹配。本文提出了一种新的框架,将规划部分建模为多商品流问题,将调度部分建模为约束规划问题。提出了一种将CP与深度强化学习(DRL)协同结合的新型双引擎优化方法,以加速调度阶段。该方法利用DRL代理来固定关键变量,从而有效地加快了CP求解器的优化过程。在真实数据集和真实场景实例上进行了全面的数值实验,验证了所提方法的有效性,表明该方法显著提高了端口调度效率。此外,战略管理分析提供可操作的见解,以支持散装港口运营的决策。提出的方法提供了一种通用的方法,适用于港口物流及其他领域的广泛复杂组合优化问题,为更智能和可持续的干散货港口管理铺平了道路。
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引用次数: 0
Drone scheduling optimization for shore-to-ship delivery and waste recycling 岸到船运输和废物回收的无人机调度优化
IF 8.8 1区 工程技术 Q1 ECONOMICS Pub Date : 2026-02-05 DOI: 10.1016/j.tre.2026.104708
Tingsong Wang , Haiqing Zhou , Xuecheng Tian
Traditional shore-to-ship delivery and waste recycling predominantly rely on replenishment vessels, facing critical challenges such as low operational efficiency, high costs, and prolonged waiting times during multi-vessel operations. To address these challenges, this paper addresses the Drone Scheduling Problem in Shore-to-Ship Delivery and Recycling (DSP-SSDR). Specifically, we develop a mixed-integer programming (MIP) model to minimize the total completion time of all required tasks while accounting for practical constraints including drone load capacity, time windows, battery consumption, multiple trips, and dynamic base station return. To efficiently solve this problem, an adaptive large neighborhood search (ALNS) algorithm is proposed and improved, which dynamically adapts removal and repair operators and incorporates a two-layer local search to improve the solution quality. Numerical experiments using practical realistic test cases demonstrate that the proposed ALNS algorithm achieves superior performance compared to the commercial solver Gurobi, particularly in computational efficiency for large-scale instances.
传统的岸到船运输和废物回收主要依靠补给船,面临着操作效率低、成本高、多船作业时等待时间长等严峻挑战。为了解决这些挑战,本文讨论了岸到船交付和回收(DSP-SSDR)中的无人机调度问题。具体来说,我们开发了一个混合整数规划(MIP)模型,以最小化所有所需任务的总完成时间,同时考虑到实际约束,包括无人机负载能力、时间窗口、电池消耗、多次行程和动态基站返回。为了有效地解决这一问题,提出并改进了一种自适应大邻域搜索(ALNS)算法,该算法动态地适应移除算子和修复算子,并结合两层局部搜索来提高解的质量。使用实际测试用例的数值实验表明,与商业求解器Gurobi相比,所提出的ALNS算法具有优越的性能,特别是在大规模实例的计算效率方面。
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引用次数: 0
Shipping service design for wind-assisted ships 风助船舶船舶服务设计
IF 8.8 1区 工程技术 Q1 ECONOMICS Pub Date : 2026-02-03 DOI: 10.1016/j.tre.2026.104720
Shuanglu Zhang, Xiuwen Wang, Lu Zhen
Wind-assisted propulsion systems (WAPS) have emerged as a promising technology in the shipping industry. The utilization of wind energy can provide auxiliary thrust and hence reduce the fuel consumption as well as carbon emissions of wind-assisted ships. However, the rigid structure of a traditional shipping service is often suboptimal for harnessing wind energy effectively. This paper explores a shipping service design problem for wind-assisted ships, which is formulated as a two-stage stochastic mixed-integer programming model. The first-stage decisions determine the optimal port visit sequence of all ports, while the second-stage decisions adapt the ship’s schedule under a set of wind scenarios to minimize expected total voyage costs, including fuel, operational, and delay-related expenses. A Benders decomposition algorithm is utilized to solve the stochastic model. The model is applied to a realistic trans-Pacific case study. The results of a comparative analysis against a conventional shipping case indicate the superiority of wind-assisted ships in reducing both costs and carbon emissions. Furthermore, a comprehensive sensitivity analysis reveals that the economic advantage of the integration of WAPS technology and stochastic optimization is robust, providing shipping companies with a practical and profitable strategy towards sustainable operations.
风辅助推进系统(WAPS)在航运业中已经成为一项很有前途的技术。风能的利用可以提供辅助推力,从而降低风助船舶的燃料消耗和碳排放。然而,传统航运服务的刚性结构往往不是有效利用风能的最佳选择。本文研究了风助船舶的船舶服务设计问题,将其表述为一个两阶段随机混合整数规划模型。第一阶段的决策确定所有港口的最佳港口访问顺序,而第二阶段的决策在一组风情景下调整船舶的时间表,以最小化预期的总航行成本,包括燃料、操作和延误相关费用。采用Benders分解算法求解随机模型。该模型应用于一个现实的跨太平洋案例研究。与传统船舶案例的对比分析结果表明,风助船舶在降低成本和碳排放方面具有优势。此外,综合敏感性分析表明,将WAPS技术与随机优化相结合的经济优势是稳健的,为航运公司的可持续运营提供了切实可行的盈利策略。
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引用次数: 0
Resilient RoRo fleet scheduling for mixed EV and ICEV transport demand: An optimization framework for EV dedicated service strategy 电动汽车和电动汽车混合运输需求的弹性滚装机队调度:电动汽车专用服务策略的优化框架
IF 8.8 1区 工程技术 Q1 ECONOMICS Pub Date : 2026-02-03 DOI: 10.1016/j.tre.2026.104719
Yukuan Wang , Ryan Wen Liu , Jingxian Liu , Lichao Yang , Yang Liu , Miquel Angel Piera Eroles
The surge in electric vehicles (EVs) is causing a structural disruption to high-density, short-sea Roll-on/Roll-off (RoRo) transportation, driven by stricter safety regulations and unique transport protocols. Operators like those in China’s Qiongzhou Strait have implemented an ‘EV Dedicated Service’ (EVDS) strategy. This strategy involves a complex coordination problem between dedicated EV-carrying vessels and separate vessels for transporting drivers. However, this emerging scheduling paradigm has been insufficiently studied. This paper proposes a multi-objective mixed integer programming model for the RoRo fleet scheduling with a novel methodological approach to formulate EVDS mechanism. Additionally, we develop an Adaptive Large Neighborhood Search − based heuristic algorithm, featuring novel problem-specific neighborhood structures. Realistic instances validated the algorithm’s performance against benchmark methods. The results also revealed the balance between economic efficiency and service levels across three different demand scenarios (Low-Season, Normal-Day, and Peak-Season). Furthermore, the analysis reveals the strategic value of flexible deployment for EV-certified vessels. We also introduce a method to quantify operational resilience by analyzing the impact of elastic capacity planning on alleviating port congestion. The findings provide a robust decision-support framework for RoRo operators and policymakers navigating the surge in EV transport demand.
在更严格的安全法规和独特的运输协议的推动下,电动汽车(ev)的激增正在对高密度、短海上滚装(RoRo)运输造成结构性破坏。琼州海峡的运营商已经实施了“电动汽车专用服务”(EVDS)战略。这一策略涉及到专用电动汽车运输船和单独运输司机的船只之间复杂的协调问题。然而,这一新兴的调度范式还没有得到充分的研究。本文提出了滚装机队调度的多目标混合整数规划模型,并提出了一种新的EVDS机制制定方法。此外,我们开发了一种基于自适应大邻域搜索的启发式算法,该算法具有新颖的问题特定邻域结构。实际实例验证了该算法与基准方法的性能。结果还揭示了三种不同需求情景(淡季、正常日和旺季)的经济效率和服务水平之间的平衡。此外,分析还揭示了电动汽车认证船舶灵活部署的战略价值。通过分析弹性容量规划对缓解港口拥堵的影响,提出了一种量化运营弹性的方法。研究结果为滚装运营商和政策制定者提供了强有力的决策支持框架,以应对电动汽车运输需求的激增。
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引用次数: 0
A model for queuing toll policies incorporating cost-sharing characteristics of carpooling in the transportation network with capacity constraints 考虑容量限制下拼车成本分担特征的排队收费政策模型
IF 8.8 1区 工程技术 Q1 ECONOMICS Pub Date : 2026-02-03 DOI: 10.1016/j.tre.2026.104700
Yanqin Chen , Changxin Liao , Jia Yao , David Z.W. Wang , Anthony Chen
This paper introduces capacity constraints into a multi-modal transportation network, including solo driving, carpooling, and public transit, and proposes two queuing toll policies based on the cost-sharing characteristics of carpooling to eliminate physical queues. By incorporating traveler heterogeneity in the value of time, a multi-class user equilibrium model is developed to assess the impacts of capacity constraints and queuing toll policies on traffic equilibrium under three cases: 1) case 1, no tolls are charged to any travelers. Physical queues may occur on the saturated links; 2) case 2, the queuing tolls are charged exclusively to solo drivers, and it takes into account the scenario that the physical queues are not completely eliminated in the saturated link only with carpooling; 3) case 3, the queuing tolls are charged to solo drivers and carpooling travelers (i.e., carpooling drivers and riders). Then, an improved route swapping algorithm is proposed to solve the equilibrium model. Finally, numerical analysis based on the Winnipeg network is conducted to demonstrate the properties of the problem and the performance of the proposed model and algorithm. The results show that the proposed queuing toll policies can improve travel efficiency by encouraging carpooling and eliminating physical queues. Moreover, the differential impacts of the three cases on heterogeneous travelers’ mode choices and travel efficiency are explored. These findings provide a theoretical basis for the practical implementation of queuing toll policies.
本文将容量约束引入到包括单独驾驶、拼车和公共交通在内的多式联运网络中,并提出了两种基于拼车成本分担特性的排队收费政策,以消除物理排队。在时间价值中考虑出行者异质性的基础上,建立了一种多类别用户均衡模型,评估了三种情况下容量约束和排队收费政策对交通均衡的影响:1)情况1,不向任何出行者收取任何通行费。在饱和链路上可能出现物理队列;(2)情形2,排队费仅对单独司机收取,并考虑了仅拼车时饱和路段物理排队未完全消除的情况;(3)情形3,排队费向单独驾驶和拼车的旅客(即拼车司机和乘客)收取。然后,提出了一种改进的路由交换算法来求解均衡模型。最后,以Winnipeg网络为例进行了数值分析,验证了问题的性质以及所提模型和算法的性能。结果表明,本文提出的排队收费政策能够通过鼓励拼车和消除物理排队来提高出行效率。此外,还探讨了三种情况对异质性出行者出行方式选择和出行效率的差异影响。研究结果为排队收费政策的实际实施提供了理论依据。
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引用次数: 0
Shaping the reality of temporary supply network for humanitarian relief through social media: A quasi-natural field experiment on the socio-technical system 通过社会媒体塑造人道主义救济临时供应网络的现实:社会技术系统的准自然现场实验
IF 8.8 1区 工程技术 Q1 ECONOMICS Pub Date : 2026-02-03 DOI: 10.1016/j.tre.2026.104706
Xiaochen Yue , Xiling Cui , Qianwen Wang , Baofeng Huo
Coordinating temporary supply networks (TSNs) presents significant challenges due to the heterogeneity of participant backgrounds, objectives, and values. This study investigates whether TSNs can function effectively as socio-technical systems to facilitate coordination and enhance performance. Leveraging a quasi-natural field experiment during the initial outbreak of COVID-19, this study employs ordinary least squares (OLS) regression analysis based on data from 225 relief actors participating in TSNs for humanitarian relief. It investigates how TSNs operate as socio-technical systems, emphasizing the cornerstone role of the technical subsystem. The results reveal a serial mediating effect: social media use improves delivery performance by fostering relief actors’ affective commitment and subsequent coordination practices. Moreover, the affordance of searchability provided by social media platforms positively moderates the relationship between affective commitment and coordination practices. This study enriches the TSN management literature by advancing a socio-technical system perspective and extends this framework by underscoring the pivotal role of the technical subsystem in an era of digital transformation. In addition, this study offers practical insights by emphasizing the critical role of digital technologies in TSNs for humanitarian relief and encouraging the strategic mobilization of social resources to strengthen coordination capabilities.
由于参与者背景、目标和价值观的异质性,协调临时供应网络(tsn)提出了重大挑战。本研究探讨tsn是否能有效地作为社会技术系统来促进协调和提高绩效。本研究利用新冠肺炎疫情暴发初期的准自然现场实验,基于225名参与tsn的人道主义救援行为体的数据,采用普通最小二乘(OLS)回归分析。它研究tsn如何作为社会技术系统运作,强调技术子系统的基石作用。研究结果表明,社交媒体的使用通过促进救济行为者的情感承诺和后续协调实践来改善交付绩效。此外,社交媒体平台提供的可搜索性正向调节情感承诺与协调实践之间的关系。本研究通过推进社会技术系统视角丰富了TSN管理文献,并通过强调技术子系统在数字化转型时代的关键作用扩展了这一框架。此外,本研究强调数字技术在人道主义救援tsn中的关键作用,并鼓励战略性调动社会资源以加强协调能力,提供了实践见解。
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引用次数: 0
A carbon reduction incentive model for crowdsourced urban freight: Facilitating freight pooling and electric truck adoption 众包城市货运的碳减排激励模型:促进货运池和电动卡车的采用
IF 8.8 1区 工程技术 Q1 ECONOMICS Pub Date : 2026-02-02 DOI: 10.1016/j.tre.2026.104712
Siqi Shu , Xinyue Yang , Zhuang Ming , Xiaoxiang Na , Marc E.J. Stettler , Der-Horng Lee , Simon Hu
Urban freight transport faces significant decarbonization pressure, yet existing strategies such as freight pooling and electric truck adoption often struggle with limited uptake due to operational complexities, costs, and infrastructure challenges. Critically, current research lacks an integrated, operational incentive framework specifically designed for multi-stakeholder participation in urban crowdsourced logistics, where task-level operational decisions across multiple stakeholders play a central role in system-level carbon reduction. This study introduces a Carbon Reduction Incentive Model (CRIM) that addresses this gap. The CRIM incentivizes individual shippers and independent carriers within a crowdsourced logistics system by assigning task-level rewards for freight pooling and electric truck usage. Rewards are quantified by tonne-kilometer savings relative to conventional individual diesel deliveries, further adjusted by a time-based factor to encourage off-peak operations. The CRIM is embedded within an enhanced pick-up and delivery model that explicitly accounts for stakeholder cost components, vehicle heterogeneity, charging requirements, and time-sensitive feasibility (PDPTW-HEC). To optimize the system’s complex trade-off between costs and carbon emissions, a customized heuristic algorithm is developed. Scenario-based case studies using real-world data and international carbon accounting standards validate the proposed incentive model’s performance. Results demonstrate that CRIM can achieve 9.5–38.1% higher electric truck adoption and an 8.4–28.7% reduction in total carbon emissions. This framework offers a practical and scalable approach for designing and evaluating task-level carbon reduction incentives in urban freight operations.
城市货运面临着巨大的脱碳压力,但由于运营复杂性、成本和基础设施方面的挑战,现有的战略,如货运池和电动卡车的采用,往往难以得到有限的应用。关键的是,目前的研究缺乏一个专门为城市众包物流中的多利益相关者参与而设计的综合的运营激励框架,在城市众包物流中,跨多个利益相关者的任务级运营决策在系统级碳减排中发挥着核心作用。本研究引入了碳减排激励模型(CRIM)来解决这一差距。CRIM通过为货运池和电动卡车的使用分配任务级奖励,在众包物流系统中激励个体托运人和独立承运人。奖励是通过与传统的单个柴油交付相比节省的吨公里来量化的,并通过基于时间的因素进一步调整,以鼓励非高峰运营。CRIM嵌入在增强型取货和交付模型中,该模型明确考虑了利益相关者的成本组成、车辆异质性、充电要求和时间敏感性可行性(PDPTW-HEC)。为了优化系统成本和碳排放之间的复杂权衡,开发了一种定制的启发式算法。使用真实世界数据和国际碳会计标准的基于场景的案例研究验证了所提出的激励模型的性能。结果表明,CRIM可以使电动卡车采用率提高9.5-38.1%,总碳排放量减少8.4-28.7%。该框架为设计和评估城市货运业务中任务级碳减排激励措施提供了一种实用且可扩展的方法。
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引用次数: 0
期刊
Transportation Research Part E-Logistics and Transportation Review
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