Transportation Research Part E-Logistics and Transportation Review最新文献_第10页

Enhancing carsharing pricing and operations through integrated choice models

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-01-30 DOI: 10.1016/j.tre.2025.103993

Beatriz Brito Oliveira , Selin Damla Ahipasaoglu

Balancing supply and demand in free-floating one-way carsharing systems is a critical operational challenge. This paper presents a novel approach that integrates a binary logit model into a mixed integer linear programming framework to optimize short-term pricing and fleet relocation. Demand modeling, based on a binary logit model, aggregates different trips under a unified utility model and improves estimation by incorporating information from similar trips. To speed up the estimation process, a categorizing approach is used, where variables such as location and time are classified into a few categories based on shared attributes. This is particularly beneficial for trips with limited observations as information gained from similar trips can be used for these trips effectively. The modeling framework adopts a dynamic structure where the binary logit model estimates demand using accumulated observations from past iterations at each decision point. This continuous learning environment allows for dynamic improvement in estimation and decision-making. At the core of the framework is a mathematical program that prescribes optimal levels of promotion and relocation. The framework then includes simulated market responses to the decisions, allowing for real-time adjustments to effectively balance supply and demand. Computational experiments demonstrate the effectiveness of the proposed approach and highlight its potential for real-world applications. The continuous learning environment, combining demand modeling and operational decisions, opens avenues for future research in transportation systems.

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引用次数: 0

Should live streaming be adopted for agricultural supply chain considering platform’s quality improvement and blockchain support?

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-01-30 DOI: 10.1016/j.tre.2024.103950

Xiaoping Xu , Xinru Chen , Jinyan Hou , T.C.E. Cheng , Yugang Yu , Li Zhou

Platforms empower farmers to address the quality and safety issues of agricultural products by offering blockchain technology and improving product quality. Recently, live streaming becomes a new sales channel to sell products, and plays a key role in enhancing the visibility of agricultural products and introducing detailed information of agricultural products in real time. This disrupted the long-term cooperative relationship between the platforms and farmers. Based on this, our paper explores that whether and how the agricultural supply chain members should adopt the live streaming channel. We build a game model that consists of a farmer, a platform, and an influencer, and investigate the live streaming introduction strategies in the agricultural supply chain in the context of the platform assisting the farmer (the improvement of quality level and adoption of blockchain). We capture two characteristics of the live streaming channel, i.e., the live streaming channel’s impact on market size (live streaming value) and additional profit per unit product derived from the influencer’s personal influence (influencer value). We interestingly find that when the live streaming value is low, introducing live streaming negatively impacts the farmer’s profit at the low influencer value in the farmer’s live streaming. When the live streaming value is low (high), introducing live streaming damages the platform’s profit at the moderate (low) influencer value in the platform’s live streaming. Therefore, adopting the live streaming channel is not necessarily profitable for agricultural supply chain members. We also extend our model to check the robustness of our findings. This study is the first to explore live streaming introduction strategies in the agricultural supply chain in the context of the platform assisting the farmer, and significantly contributes to the literature and provides valuable guidance for farmers and platforms on when to adopt the live streaming channel in the agricultural supply chain.

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引用次数: 0

Returns management in a supply chain considering freight insurance and consumer disappointment aversion

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-01-29 DOI: 10.1016/j.tre.2025.103975

Yueqing Bian, Tiaojun Xiao

Returns tend to cut profit and leave consumers unhappy, making returns management a critical issue in supply chain operations. This paper investigates the role of consumer disappointment aversion, stemming from product value uncertainty, in shaping critical operational decisions in a return-focused supply chain, including the pricing policy, the manufacturer’s returns collection strategy (direct returns from consumers or indirect buybacks from the e-tailer), and the e-tailer’s return freight insurance strategy (e-tailer-return freight insurance or consumer-return freight insurance). Through a game-theoretical approach, we reveal several compelling insights. First, as consumer disappointment aversion rises, both the manufacturer and the e-tailer are compelled to lower prices to maintain demand, despite an overall decline in profitability. Second, regarding the impacts of consumer disappointment aversion on the supply chain equilibrium, two pivotal findings are uncovered. For the e-tailer, high consumer disappointment aversion motivates the e-tailer-return freight insurance strategy. Furthermore, how consumer disappointment aversion shapes the manufacturer’s returns collection strategy is strongly linked to the e-tailer’s choice of return freight insurance strategy. Specifically, in certain cases, in anticipation of the e-tailer’s adoption of e-tailer-return freight insurance strategy, the manufacturer is incentivized to prefer direct returns when consumer disappointment aversion is relatively low. However, the manufacturer is incentivized to select direct returns when facing a relatively high consumer disappointment aversion, anticipating the e-tailer’s adoption of consumer-return freight insurance strategy. Moreover, our analysis indicates that each equilibrium has the potential to achieve Pareto efficiency and even a win–win–win outcome that benefits all parties including consumers.

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引用次数: 0

The flex-route transit planning problem with meeting points

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-01-28 DOI: 10.1016/j.tre.2025.103981

Mingyang Li , Lingxiao Wu , Yadong Wang , Jinjun Tang , Tao Feng

As an innovative alternative to ridesharing, flex-route transit (FRT) is widely acknowledged as a promising solution, especially in scenarios in which transportation demand is low or dispersed. This paper addresses the FRT planning problem with meeting points (FRTPP-MP), which conceptualizes each passenger’s pick-up/drop-off request as a set of points (i.e., a cluster) containing the designated pick-up/drop-off point and alternative points (i.e., meeting points), stipulating that only one point in each cluster needs to be visited to fulfill the request. The aim is to minimize both the travel cost of vehicles and the walking cost of passengers by simultaneously optimizing the routes of vehicles and the selection of nodes within their respective clusters. We formulate the FRTPP-MP as a mixed-integer programming (MIP) model and develop an exact branch-and-price (BAP) algorithm to solve it. To tackle the specific challenges of cluster visit restrictions in the pricing problem, we design a tailored bidirectional label correction algorithm (TBLCA), which is further enhanced by a novel acceleration strategy. Extensive computational experiments are conducted based on benchmark instances generated from a real-life FRT system. The numerical results highlight our solution algorithm’s satisfactory performance. Furthermore, managerial insights from a sensitivity analysis suggest that introducing meeting points can substantially reduce the costs associated with FRT.

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引用次数: 0

A multi-mode hybrid electric vehicle routing problem with time windows

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-01-27 DOI: 10.1016/j.tre.2025.103976

Yupeng Jiang , Wei Hu , Wenjuan Gu , Yongguang Yu , Meng Xu

The increasing focus on energy consumption and carbon emissions has spurred interest in application of the hybrid electric vehicles (HEVs) for logistics and transportation. This paper proposes a multi-mode hybrid electric vehicle routing problem with time windows (MM-HEVRPTW), in which the HEVs are allowed to operate in combustion, electric, charging, and boost modes. The MM-HEVRPTW simultaneously optimizes the routing decision and operation mode on each road segment while considering constraints such as the battery level and customer time windows. We express the problem as a mixed-integer linear programming model, aiming to minimize the total travel cost on different operation modes. An improved adaptive large neighborhood search (ALNS) algorithm is proposed to address this challenging problem. The algorithm introduces a new customer removal number scheme, two new operators, and combines a local search process and an infeasibility penalization scheme to obtain improved solutions. Computational experiments have been performed on sets of new instances which are generated by modifying existing benchmark instances. Results of different scales of numerical experiments and evaluation of optimization components demonstrate the excellent performance of the proposed ALNS. Furthermore, we investigate the effect of using multi-mode for HEVs and the impact of battery charging and discharging rates on the total travel cost.

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引用次数: 0

Equity-based vaccine delivery by drones: Optimizing distribution in disease-prone regions

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-01-27 DOI: 10.1016/j.tre.2025.103979

Hamid R. Sayarshad

This study introduces a model that combines dynamic disease modeling and an optimization approach for drone-based vaccine delivery to achieve fair distribution and enhance equity in vaccine access across different regions, including rural areas and small cities. Our approach aims to achieve optimal allocation of vaccines by considering regional infection rates and equilibrium vaccination rates, which allows us to forecast vaccine demand effectively. To achieve this, we employ a region-specific dynamic disease model that considers population size, infection rates, and vaccination rates. Utilizing this dynamic disease model with a well-structured delivery network minimizes travel and healthcare costs resulting from insufficient vaccination delivery while ensuring equitable distribution. Our model also considers logistical factors specific to drone vaccine delivery, including routing and recharging plans, payload capacity, flight range, and regional vaccine demand. These considerations are crucial to addressing the unique challenges rural areas and small cities face in accessing healthcare services. This study also investigates the essential trade-offs between minimizing delivery costs and mitigating healthcare burdens during a pandemic response. We study drone vaccine delivery during the COVID-19 pandemic to validate our model, explicitly focusing on Orange County (OC) and small cities. The results of this study have important practical implications for designing drone-based vaccine delivery systems that prioritize fairness and equitable access, especially in smaller cities and rural areas. It highlights that cities with lower populations but higher transmission rates may require more vaccines, while larger cities with lower rates need fewer.

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引用次数: 0

The integration of location and inventory decisions in supply chain networks: A literature review and future prospects

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-01-27 DOI: 10.1016/j.tre.2025.103970

Inez Puttemans, An Caris, Kris Braekers

Supply chain network design, a critical aspect of supply chain management, involves determining the physical network through which goods flow from suppliers to end customers. Strategic decisions involve locating facilities, capacity planning, and sourcing, which are crucial for cost-effectiveness and competitiveness. Traditionally, companies make these decisions in isolation, neglecting their interconnectivity with tactical and operational decisions. However, modern challenges such as evolving market dynamics and increasing competition necessitate integrated decision-making, particularly in inventory management and facility location, to enhance supply chain efficacy. Operations research and operations management techniques, such as the modeling of joint location and inventory decisions as a location-inventory problem (LIP), offer support in this integrated approach. In this paper, we present a review of recent contributions in the field of LIP research through an elaborated classification framework, which expands upon an existing classification framework. Our review reveals prevalent modeling assumptions in the current literature on LIP and provides insights into the evolving landscape of LIP research. By critically questioning these assumptions, we highlight the need for more realistic approaches in future LIP research. Based on this review, we identify specific future research directions, emphasizing their relevance to different contexts of LIP. Finally, we propose specific suggestions for future reviews.

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引用次数: 0

The influence of distributional fairness concern on quality co-creation mobile application supply chain: Exogenous and endogenous revenue-retaining mechanisms

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-01-26 DOI: 10.1016/j.tre.2025.103966

Lulu Xia , Kai Li , Yu Xiong

This research examines the quality co-creation practice in the mobile application supply chain composed of a mobile application developer and a mobile application distributor within a revenue-sharing framework. We analyze whether the distributional fairness concern of supply chain entities and the endogeneity or exogeneity of revenue-retaining ratio exert great influence on supply chain decisions and performance. Through analysis, we obtain following results. Firstly, the distributional fairness concern of supply chain entities drops down service price in both exogenous and endogenous revenue-retaining ratio contexts. Secondly, the developer’s emphasis on profit distribution fairness reduces service quality in the exogenous revenue-retaining context but enhances it in the endogenous revenue-retaining model. Thirdly, the developer achieves profit increment when he is the only one demonstrating distributional fairness concern but the distributor’s earnings shrink in his own distributional fairness concern situation. Finally, we derive in both exogenous and endogenous revenue-retaining contexts, the supply chain entities may simultaneously incorporate distributional fairness concern into internal decision making standards and fall into the prisoner’s dilemma. Additionally, we further explore three extended models: sequential strategic decision-making, considering the choice between exogenous and endogenous contracts, and bargaining over revenue-retaining ratio. Our findings demonstrate that, across all three extended models, supply chain entities may still fall into the prisoner’s dilemma.

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引用次数: 0

Enhancing resilience in supply chains through resource orchestration and AI assimilation: An empirical exploration

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-01-24 DOI: 10.1016/j.tre.2025.103980

Xingwei Lu , Xianhao Xu , Yi Sun

In the face of unprecedented global supply chain disruptions, enhancing supply chain resilience (SCR) has become a critical priority. This empirical study examines the crucial role of resource orchestration, encompassing internal reconfiguration and external integration, across the three dynamic capabilities of SCR: readiness, response, and recovery. Additionally, it investigates the moderating effect of AI assimilation on these relationships. Grounded in Resource Orchestration Theory (ROT), this research analyzes data from 388 supply chain executives in China. The findings demonstrate that resource orchestration significantly enhances SCR across most capabilities, with the notable exception of external integration’s effect on the recovery. Interestingly, AI assimilation emerges as a key moderator, strengthening the relationship between internal reconfiguration and the SCR capabilities of readiness and recovery, while exhibiting a negligible effect on the response. This study contributes to the academic discourse by illuminating the complex interactions among ROT, SCR, and AI assimilation, offering valuable guidance for developing effective SCR strategies and highlight the pivotal role of AI assimilation in navigating the complexities of modern global supply chain challenges.

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引用次数: 0

Multi-agent deep reinforcement learning-based truck-drone collaborative routing with dynamic emergency response

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review

Pub Date : 2025-01-24 DOI: 10.1016/j.tre.2025.103974

Wenhao Peng , Dujuan Wang , Yunqiang Yin , T.C.E. Cheng

In emergency disaster response, the dynamic nature and uncertainty of resource transportation pose significant challenges for vehicle routing planning. We address a truck-drone collaborative routing problem in humanitarian logistics, where a set of truck-drone tandems collaboratively deliver relief resources from a distribution center to a set of affected areas which is dynamically updated as disaster changes. In the truck-drone collaborative mode, as each truck performs the delivery services and serves as a mobile depot for the drone associated with it, the drone launches from its associated truck at a node, delivers relief resources to one affected area, and returns to rendezvous with the truck at the node or another node along the truck route. We cast the problem as a Markov game model with an event-driven method, which can effectively capture the dynamic changes in the states and node information of trucks and drones during relief resources delivery. To solve the model, we develop a multi-agent deep reinforcement learning algorithm, which combines prioritized experience replay and invalid action masking to improve the sample efficiency and reduce the decision space. We conduct extensive numerical studies to validate the effectiveness of the proposed method by comparing it with existing solution methods and two well-known heuristic rules, and discuss the impacts of some model parameters on the solution performance. We also assess the advantages of the truck-drone collaborative mode over the truck/helicopter-only mode through a case study of the 2008 Wenchuan earthquake.

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引用次数: 0