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Impact of network nestedness on resistance and recovery of supply chain resilience 网络筑巢性对供应链弹性抗力和恢复的影响
IF 1 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.ijpe.2025.109854
Sihang Chen , Junqin Lin , Xiaopo Zhuo , Libo Yin , Jiaxin Shen
Network nestedness, which refers to the hierarchical structure of interconnections within a network, plays an important role in supply chain resilience but remains understudied. We use data from listed firms in China between 2002 and 2022 to construct buyer-supplier networks and measure nestedness using the SNODF metric. We validate SNODF's robustness across varying levels of network completeness. Listed firms are centrally positioned in our networks, making them crucial focal points for analysis. Our empirical results show that network nestedness has a dual effect on supply chain resilience: it weakens short-term resistance to disruptions but enhances long-term recovery. This trade-off arises because hierarchical structures concentrate vulnerability at hub nodes while enabling coordinated resource reallocation after a disruption. We examine two managerial levers that moderate these effects: (1) supplier concentration, an external strategy that attenuates the negative effect on resistance but dampens recovery gains; and (2) corporate digitalization, an internal strategy that mitigates initial losses and enhances recovery. These findings imply that firms should balance two approaches: (1) mitigating risk through supplier diversification to reduce dependence on dominant hubs, and (2) leveraging digital technologies to improve recovery capabilities, thus strengthening long-term resilience.
网络嵌套性是指网络内互连的层次结构,在供应链弹性中起着重要作用,但仍未得到充分研究。我们使用2002年至2022年中国上市公司的数据来构建买方-供应商网络,并使用SNODF度量嵌套性。我们在不同级别的网络完整性上验证了SNODF的健壮性。上市公司在我们的网络中处于中心位置,使它们成为分析的关键焦点。实证结果表明,网络嵌套性对供应链弹性具有双重影响:它削弱了对中断的短期抵抗,但增强了长期恢复。之所以会出现这种权衡,是因为分层结构将漏洞集中在集线器节点上,同时在中断后支持协调的资源重新分配。我们研究了缓和这些影响的两种管理杠杆:(1)供应商集中,这是一种外部战略,可以减轻对阻力的负面影响,但会抑制复苏收益;(2)企业数字化,这是一种内部战略,可以减轻初始损失并提高恢复能力。这些发现表明,企业应该平衡两种方法:(1)通过供应商多样化来降低风险,以减少对主导中心的依赖;(2)利用数字技术来提高恢复能力,从而增强长期弹性。
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引用次数: 0
Integrated production and maintenance planning in imperfect hybrid manufacturing–remanufacturing systems with outsourcing and carbon emissions 考虑外包和碳排放的不完全混合制造-再制造系统的集成生产与维修计划
IF 1 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.ijpe.2025.109862
Mohammed Merghem , Mohammed Haoues , Ahmed Senoussi , Mohammed Dahane , Nadia Kinza Mouss
This study investigates the integrated planning of production, maintenance, and quality control in a hybrid manufacturing-remanufacturing system, accounting for deterioration, variability in the quality of returned products, carbon emissions, and outsourcing opportunities. The network consists of a manufacturer collaborating with an outsourcing remanufacturing provider. The manufacturer operates a single failure-prone machine to produce new products and to remanufacture returned ones. Recovered products that the manufacturer cannot process are sent to the outsourcing provider for remanufacturing. The system generates harmful emissions, potentially leading to environmental taxes and sanctions. We formulate a mixed-integer nonlinear programming model to determine the optimal integrated manufacturing, remanufacturing, outsourcing, and preventive maintenance plan. Eventually, the proposed strategy minimizes total economic costs and defects and ultimately reduces carbon emissions. We use a global solver for solving small instances, while a genetic algorithm metaheuristic is developed for larger ones. Extensive computational experiments reveal that the developed genetic algorithm is highly efficient, achieving gaps of less than 0.95% within shorter execution times for small instances and significantly outperforming the solver in larger ones. The results show that the integrated outsourcing strategy, combined with accounting for carbon emissions from both new and remanufactured products, significantly reduces the reliance on new products, leading to notable cost savings and environmental benefits. These savings become more pronounced as the number of returns increases.
本研究探讨了制造-再制造混合系统中生产、维修和质量控制的整合计划,考虑了退化、退货质量的可变性、碳排放和外包机会。该网络由制造商与外包再制造供应商合作组成。制造商操作一台容易发生故障的机器生产新产品,并对退回的产品进行再制造。制造商无法加工的回收产品被发送给外包供应商进行再制造。该体系产生有害排放物,可能导致环境税和制裁。我们建立了一个混合整数非线性规划模型来确定最优的集成制造、再制造、外包和预防性维护计划。最终,提出的策略使总经济成本和缺陷最小化,并最终减少碳排放。我们使用全局求解器来解决小实例,而开发了遗传算法元启发式来解决较大的实例。大量的计算实验表明,所开发的遗传算法是高效的,在较小的实例中,在较短的执行时间内实现了小于0.95%的间隙,并且在较大的实例中显著优于求解器。研究结果表明,综合考虑新产品和再制造产品碳排放的外包策略,显著降低了企业对新产品的依赖,带来了显著的成本节约和环境效益。随着回报次数的增加,这些节省变得更加明显。
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引用次数: 0
Nudging the last mile: Combining behavioral insights and monetary incentives for sustainable delivery choices 推动最后一英里:结合行为洞察力和可持续交付选择的金钱激励
IF 1 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Pub Date : 2026-01-01 Epub Date: 2025-11-11 DOI: 10.1016/j.ijpe.2025.109855
Eleonora Rizzitello , Giovanna Lo Nigro , Simona Mancini , Margaretha Gansterer
Last-mile delivery, the final leg of the supply chain to consumers, significantly impacts the environment, particularly in the grocery sector, due to the perishable nature of food items often necessitating expedited delivery methods. The role of consumer behavior in this process has been overlooked, and their preferences in a trade-off between the benefits of grocery home delivery and the environment have yet to be clarified. This paper bridges this gap by integrating behavioral logistics with key drivers of green consumer behavior to optimize grocery delivery, leveraging primary consumer data to enhance efficiency and minimize environmental impact. Using a two-part methodology, the research combines a controlled experiment and an optimization model. By partnering with an Italian supermarket, this research examines the impact of financial incentives, green nudges, and peer collaboration on consumer grocery delivery choices and routing optimization. The findings reveal that moral green nudges outweigh financial incentives and amplify the effect, and peer influence drives the adoption of shared and more sustainable delivery systems. The optimization model quantifies these behavioral insights, demonstrating cost reductions of up to 42.5% through flexible delivery scheduling and 76.8% via a shared cart mechanism. The findings provide valuable insights for practitioners and policymakers willing to intervene in the crossing of consumer choices and grocery delivery efficiency by presenting an innovative and ready-to-implement solution, which we denote as “the shared cart”. In showing the importance of social pressure for collaborative pro-environmental behavior, the shared cart practically embodies the potential for safeguarding the environment, while enabling cost-saving for last-mile delivery in the grocery sector.
最后一英里配送是供应链的最后一段,对环境的影响很大,特别是在食品杂货行业,因为食品的易腐性往往需要加速配送方法。消费者行为在这一过程中的作用一直被忽视,他们在食品杂货送货上门的好处和环境之间的权衡偏好尚未得到澄清。本文通过整合行为物流与绿色消费者行为的关键驱动因素来优化杂货配送,利用原始消费者数据来提高效率并最大限度地减少对环境的影响,从而弥合了这一差距。本研究采用两部分方法,将对照实验和优化模型相结合。通过与一家意大利超市合作,本研究考察了财政激励、绿色推动和同行合作对消费者食品杂货配送选择和路线优化的影响。研究结果显示,道德上的绿色推动超过了经济激励,并放大了效果,同行影响推动了采用共享和更可持续的交付系统。优化模型量化了这些行为洞察,表明通过灵活的交货计划可降低高达42.5%的成本,通过共享推车机制可降低76.8%的成本。研究结果为从业者和政策制定者提供了有价值的见解,他们愿意通过提出一种创新的、随时可以实施的解决方案(我们称之为“共享购物车”)来干预消费者选择和杂货配送效率的交叉。在展示社会压力对协同环保行为的重要性方面,共享购物车实际上体现了保护环境的潜力,同时为食品杂货行业的最后一英里配送节省了成本。
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引用次数: 0
An intelligent Digital Twin based on machine learning for interpretable decision-making in manufacturing 基于机器学习的制造业可解释决策智能数字孪生
IF 1 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Pub Date : 2026-01-01 Epub Date: 2025-11-04 DOI: 10.1016/j.ijpe.2025.109841
Stefano Genetti, Giorgio Scarton, Marco Formentini, Giovanni Iacca
In the context of Industry 4.0, several technologies converge to orchestrate improvements in business performance. Among these, Artificial Intelligence and Digital Twins stand out as some of the most promising. These two technologies are connected through the concept of intelligent Digital Twins (iDTs), which enhance standard Digital Twins with intelligent capabilities while keeping humans at the core of the process. One of the main obstacles to the broad adoption of iDTs in operations and supply chain management is the reliance on opaque AI models, which often limit trust and acceptability among operations experts and managers. To address this, it is critical to design iDTs that not only leverage the advanced capabilities of AI but also provide interpretable and actionable insights to stakeholders. In this paper, we present an action research in Adige Spa to develop an iDT framework for production scheduling. Our framework integrates interpretable machine learning techniques, employing evolutionary learning to produce decision trees that are transparent by design. Additionally, we incorporate Large Language Models to explain decision tree policies in natural language, enhancing user understanding. The framework also facilitates human interaction, allowing users to express preferences and guide the tree learning process. Results in a hybrid flow shop setting demonstrate that the proposed iDT framework delivers interpretable and effective decision-support policies while empowering users to influence and refine its outcomes, hence bridging the gap between AI-driven insights and real-world applicability.
在工业4.0的背景下,几种技术融合在一起,以协调业务绩效的改进。其中,人工智能和数字双胞胎是最有前途的。这两种技术通过智能数字双胞胎(iDTs)的概念连接在一起,它通过智能功能增强了标准数字双胞胎,同时将人类置于流程的核心。在运营和供应链管理中广泛采用人工智能的主要障碍之一是对不透明的人工智能模型的依赖,这往往限制了运营专家和管理人员之间的信任和可接受性。为了解决这个问题,关键是要设计不仅利用人工智能的先进功能,而且为利益相关者提供可解释和可操作的见解的idt。在本文中,我们提出了一个行动研究阿迪格温泉开发生产调度的iDT框架。我们的框架集成了可解释的机器学习技术,采用进化学习来产生设计透明的决策树。此外,我们结合了大型语言模型来用自然语言解释决策树策略,提高用户的理解能力。该框架还促进了人类互动,允许用户表达偏好并指导树学习过程。混合流程车间设置的结果表明,拟议的iDT框架提供了可解释和有效的决策支持政策,同时授权用户影响和改进其结果,从而弥合了人工智能驱动的见解与现实世界适用性之间的差距。
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引用次数: 0
Leveraging AI to enhance firms’ resource efficiency: ecological modernization theory and resource-based view perspectives 利用人工智能提高企业资源效率:生态现代化理论与资源基础视角
IF 1 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Pub Date : 2026-01-01 Epub Date: 2025-06-30 DOI: 10.1016/j.ijpe.2025.109723
Lang Zhao , Jiawei Xu , Baofeng Zhang, Jianjun Lu
How to leverage advanced technologies to enhance resource efficiency under conditions of intensified resource scarcity is becoming a pressing issue that urgently needs to be addressed. Drawing on ecological modernization theory and resource-based view, this study explores the relationship between AI and firms’ resource efficiency under different environmental pressures. Using a comprehensive dataset of Chinese listed firms, the findings reveal that artificial intelligence (AI) adoption significantly improves resource efficiency, primarily reflected in more efficient management of energy, materials, and waste. Green continuous innovation capabilities fully mediates the relationship between AI and resource efficiency. Moreover, the positive effect of AI on resource efficiency is amplified under stringent environmental pressures, indicating that firms facing higher pollution governance pressure and carbon emission pressure derive greater benefits from AI technologies. Moreover, the study further reveals four combination patterns of pollution governance pressure and carbon emission pressure at different levels, which result in differentiated outcomes in the relationship between AI and resource efficiency. Among these, high carbon emission pressure is a necessary condition for driving firms to use AI technology to enhance resource efficiency. Our study not only contributes to the theoretical understanding of the relationship between AI, external environmental pressures, and resource efficiency, but also provides some valuable insights for managers and policymakers on how to adopt AI and formulate effective environmental regulations to enhance resource efficiency.
在资源日益稀缺的情况下,如何利用先进技术提高资源效率,已成为一个迫切需要解决的问题。本文运用生态现代化理论和资源基础理论,探讨了不同环境压力下人工智能与企业资源效率的关系。利用中国上市公司的综合数据集,研究结果表明,人工智能(AI)的采用显著提高了资源效率,主要体现在对能源、材料和废物的更有效管理上。绿色持续创新能力充分中介了人工智能与资源效率之间的关系。此外,在严峻的环境压力下,人工智能对资源效率的积极影响被放大,表明面临更高污染治理压力和碳排放压力的企业从人工智能技术中获得的收益更大。此外,研究还揭示了不同水平下污染治理压力和碳排放压力的四种组合模式,导致人工智能与资源效率关系的结果存在差异。其中,高碳排放压力是驱动企业利用人工智能技术提高资源效率的必要条件。我们的研究不仅有助于从理论上理解人工智能、外部环境压力和资源效率之间的关系,而且为管理者和政策制定者如何采用人工智能并制定有效的环境法规以提高资源效率提供了一些有价值的见解。
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引用次数: 0
Navigating trade-offs in online food delivery: The interplay of buy-online-and-pick-up-in-store and delay insurance 在线食品配送的权衡:在线购买和店内提货与延迟保险的相互作用
IF 1 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Pub Date : 2026-01-01 Epub Date: 2025-11-27 DOI: 10.1016/j.ijpe.2025.109866
Minjian Liu , Qi Dong , Yunbing Li , Shaofu Du
The rise of the online food delivery (OFD) industry has greatly facilitated consumers, but frequent delivery delays have damaged the shopping experience. In order to mitigate the negative effects of delivery delays, many OFD platforms have launched buy-online-and-pick-up-in-store (BOPS) and delay insurance services. In this context, the merchant needs to decide whether to implement BOPS and offer free delay insurance (FI) to consumers. BOPS allows consumers to place orders online and pick up products offline; FI provides platform compensation for consumers who experience delivery delays. Considering delivery delays, we study the impact of BOPS and FI services on consumer purchasing, merchant pricing, and the platform’s FI premium decisions. We then examine the merchant’s joint optimization strategies of these two services. Four scenarios are analyzed regarding whether the merchant adopts these two services. Interestingly, we find that BOPS can always expand or at least maintain the merchant’s market coverage, while FI may cause market coverage to decline. Whether a merchant implements BOPS depends on the fixed costs of implementation and the compensation of FI, and whether to offer FI depends on consumers’ hassle costs and the merchant’s unit operation cost in the online channel. Counter-intuitively, when the merchant’s unit operation cost is relatively high or relatively low, FI will increase the merchant’s motivation to implement BOPS. Finally, we find that none of the four service strategies is dominant for the merchant; the platform’s profits cannot be maximized if no services are provided; consumers can only maximize total surplus when BOPS is implemented.
在线外卖(OFD)行业的兴起极大地便利了消费者,但频繁的送货延误损害了购物体验。为了减轻交货延迟的负面影响,许多OFD平台推出了网上购买和店内提货(BOPS)和延迟保险服务。在这种情况下,商家需要决定是否实施BOPS,并向消费者提供免费延迟保险(FI)。BOPS允许消费者在线下订单,线下提货;FI为经历交付延迟的消费者提供平台补偿。考虑到交货延迟,我们研究了BOPS和FI服务对消费者购买、商家定价和平台FI溢价决策的影响。然后,我们研究了商家对这两种服务的联合优化策略。分析了商家是否采用这两种服务的四种场景。有趣的是,我们发现BOPS总是可以扩大或至少保持商家的市场覆盖率,而FI可能会导致市场覆盖率下降。商家是否实施BOPS取决于实施的固定成本和FI的补偿,是否提供FI取决于消费者的麻烦成本和商家在网上渠道的单位运营成本。与直觉相反,当商家的单位运营成本较高或较低时,FI会增加商家实施BOPS的动机。最后,我们发现这四种服务策略对商家来说都不是主导策略;如果不提供服务,平台的利润就无法最大化;只有当BOPS实施时,消费者才能最大化总剩余。
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引用次数: 0
Environmental uncertainties and reciprocal investments within coopetition: The contingent role of appropriability mechanisms 环境不确定性与合作中的互惠投资:适当性机制的偶然作用
IF 1 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Pub Date : 2025-12-01 Epub Date: 2025-10-01 DOI: 10.1016/j.ijpe.2025.109811
Chandrasekararao Seepana , Antony Paulraj , Aneesh Datar
This study investigates the effects of market and technology uncertainties on reciprocal investments and the subsequent moderating role of appropriability mechanisms (i.e., patents, contracts, lead time, knowledge complexity). The study forwards several hypotheses that draw upon transaction cost economics and contingency theory. These hypotheses are tested using survey data of 313 firms that engage in horizontal coopetition relationships. Results suggest that both market and technology uncertainties lead rival partners to commit to reciprocal investments. The results further indicate that partners’ use of formal appropriability negatively moderates the relationship between market uncertainty and reciprocal investments, while informal appropriability positively moderates this relationship. Alternatively, formal appropriability positively moderates the relationship between technology uncertainty and investments, whereas informal appropriability negatively moderates this relationship. Contrary to the prevailing practice of viewing appropriability as a standalone mechanism, our findings point towards the significance of viewing appropriability mechanisms dichotomously, especially in the case of uncertain environments. As the findings suggest, employing more relevant appropriability mechanisms based on the type of uncertainties in which partners operate could lead to better outcomes for the partners pursuing coopetition relationships.
本研究探讨了市场和技术不确定性对互惠投资的影响,以及相应的适当性机制(即专利、合同、交货时间、知识复杂性)的调节作用。本文运用交易成本经济学和权变理论提出了若干假设。这些假设通过对313家从事横向合作关系的企业的调查数据进行检验。结果表明,市场和技术的不确定性导致竞争伙伴承诺互惠投资。结果进一步表明,合作伙伴使用正式可占性负向调节市场不确定性与互惠投资之间的关系,而非正式可占性正向调节这种关系。另外,正式的适宜性正向调节技术不确定性与投资之间的关系,而非正式的适宜性则负向调节这种关系。与将适当性视为一种独立机制的普遍做法相反,我们的研究结果指出了二分法看待适当性机制的重要性,特别是在不确定环境的情况下。正如研究结果所表明的那样,根据合作伙伴所处的不确定性类型,采用更相关的适宜性机制,可能会为追求合作关系的合作伙伴带来更好的结果。
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引用次数: 0
From invisible to visible: How artificial intelligence facilitates generativity in product architecture 从不可见到可见:人工智能如何促进产品架构的生成
IF 1 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Pub Date : 2025-12-01 Epub Date: 2025-09-29 DOI: 10.1016/j.ijpe.2025.109807
Jianyu Zhao , Yingbo Xu , Bo Zou , Xi Xi , Wei Liu
Artificial intelligence (AI) reshapes the business landscape. The role of AI applications in product innovation has received much attention, yet the impact of AI technologies on the product itself remains insufficient. To address this question, we developed a theoretical framework around the resource-based view and the concept of generativity. We theorize and investigate the impact of AI application on generativity in a product architecture and whether generativity promotes product sales. Using a new energy vehicle (NEV) as a sample, we set a research context of digital manufacturing in which we argue that AI applications facilitate the generation of intelligent functions in NEVs, and intelligent functions promote vehicle sales. We further find that AI–large language model (LLM) adoption weakens the positive relationship between intelligent functions and vehicle sales. We aim to contribute to AI application and generativity studies, and we provide evidence for manufacturers to develop appropriate AI system strategies.
人工智能(AI)重塑了商业格局。人工智能应用在产品创新中的作用备受关注,但人工智能技术对产品本身的影响仍然不足。为了解决这个问题,我们围绕资源基础观点和生成概念开发了一个理论框架。我们理论化并研究了人工智能应用对产品架构中生成性的影响,以及生成性是否促进了产品销售。本文以新能源汽车为例,设定了数字化制造的研究背景,认为人工智能应用促进了新能源汽车智能功能的生成,智能功能促进了汽车的销售。我们进一步发现,人工智能大语言模型(LLM)的采用削弱了智能功能与汽车销量之间的正相关关系。我们的目标是为人工智能应用和生成性研究做出贡献,并为制造商制定适当的人工智能系统策略提供证据。
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引用次数: 0
Supplier concentration and operational efficiency during catastrophic risks 灾难性风险期间的供应商集中度和运营效率
IF 1 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Pub Date : 2025-12-01 Epub Date: 2025-08-28 DOI: 10.1016/j.ijpe.2025.109777
Lu Shen , Kevin Zheng Zhou , Yan Ye
Governance for reliability has gained prominence in the post-COVID era. However, it remains unclear whether supplier concentration enhances reliable efficiency amidst supply chain risks, especially during catastrophic risks like the COVID-19 pandemic. Our research leverages the COVID-19 pandemic to assesses the impacts of supplier concentration by analyzing data from 1870 Chinese listed firms from 2017 through 2022. The results reveal a significant decline in operational efficiency as the severity of the pandemic increases, with this decline being more pronounced among firms with high supplier concentration. However, the negative interactive impact of supplier concentration and pandemic severity decreases when firms possess high levels of digitalization or relational ties. These findings highlight the importance of distinguishing between different types of supply chain risks when assessing the impact of supplier concentration and reveals risk-mitigating strategies that firms can adopt to navigate the paradoxical effects of supplier concentration amid catastrophic risks.
在后新冠时代,可靠性治理变得尤为突出。然而,目前尚不清楚供应商集中是否能在供应链风险中提高可靠的效率,特别是在COVID-19大流行等灾难性风险中。我们的研究通过分析2017年至2022年1870家中国上市公司的数据,利用COVID-19大流行来评估供应商集中度的影响。结果显示,随着疫情严重程度的增加,运营效率显著下降,在供应商集中度较高的公司中,这种下降更为明显。然而,当企业拥有高水平的数字化或关系关系时,供应商集中度和流行病严重程度的负面互动影响会降低。这些发现强调了在评估供应商集中的影响时区分不同类型供应链风险的重要性,并揭示了企业可以采用的风险缓解策略,以应对供应商集中在灾难性风险中的矛盾效应。
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引用次数: 0
Federated digital twins platform for smart city logistics: A knowledge-driven approach 智慧城市物流的联邦数字孪生平台:知识驱动的方法
IF 1 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Pub Date : 2025-12-01 Epub Date: 2025-08-25 DOI: 10.1016/j.ijpe.2025.109772
Yu Liu , Shenle Pan , Eric Ballot
Urban logistics faces increasing pressure from rising population densities, escalating delivery demands, and constrained urban resources. Traditional logistics systems struggle to adapt to real-time urban dynamics, leading to inefficiencies, congestion, and environmental concerns. A key challenge lies in mobilizing underutilized assets, such as off-hour freight parking, and adopting multimodal solutions to navigate diverse and increasingly strict regulations, thereby enhancing both sustainability and operational efficiency. However, effective management and utilization of these assets require real-time visibility, cross-stakeholder collaboration, and intelligent decision-making. This study proposes a federated digital twin platform to enhance logistics operations efficiency by integrating asset management and knowledge-driven operations management, relying on real-time asset visibility and delivery knowledge, such as destination characteristics and preferred logistics modalities. Unlike traditional logistics planning, which relies on static assumptions, our approach adapts to urban constraints by continuously querying real-time asset information and integrating logistics-related knowledge into operations management. To assess the effectiveness of this approach, an optimization-based simulation framework with decision-making tools is developed. The study evaluates multi-echelon logistics networks, incorporating micro-hubs, dynamic transshipment points, and multimodal logistics options, including on-foot porters, E-cargo bikes, and Road Autonomous Delivery Robots (RADRs). Findings demonstrate that integrating federated digital twins with knowledge-driven approaches, such as destination-based clustering and modality selection, reduces costs by over 50 % and emissions by more than 30 %. This study underscores the transformative potential of digital twins in enabling real-time, knowledge-driven operations management, and fostering more sustainable and efficient urban logistics systems.
人口密度上升、快递需求增加、城市资源受限等因素给城市物流带来越来越大的压力。传统的物流系统难以适应实时的城市动态,导致效率低下、拥堵和环境问题。一个关键的挑战在于调动未充分利用的资产,例如非工作时间的货运停车场,并采用多式联运解决方案来应对多样化和日益严格的法规,从而提高可持续性和运营效率。然而,这些资产的有效管理和利用需要实时可见性、跨涉众协作和智能决策。本研究提出了一个联合数字孪生平台,通过整合资产管理和知识驱动的运营管理,依靠实时资产可见性和交付知识,如目的地特征和首选物流模式,提高物流运营效率。与依赖静态假设的传统物流规划不同,我们的方法通过不断查询实时资产信息和将物流相关知识整合到运营管理中来适应城市约束。为了评估这种方法的有效性,开发了一个基于优化的仿真框架和决策工具。该研究评估了多级物流网络,包括微型枢纽、动态转运点和多式联运物流选择,包括步行搬运工、电子货运自行车和道路自动配送机器人(radr)。研究结果表明,将联合数字孪生与知识驱动的方法(如基于目的地的聚类和模式选择)相结合,可将成本降低50%以上,排放量降低30%以上。这项研究强调了数字孪生体在实现实时、知识驱动的运营管理以及培育更可持续、更高效的城市物流系统方面的变革潜力。
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引用次数: 0
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International Journal of Production Economics
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