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Transportation research, Part E. Logistics and transportation review
Elsevier Science Ltd.
Transportation research, Part E. Logistics and transportation review

Elsevier Science Ltd.

1366-5545

Transportation research, Part E. Logistics and transportation review/Journal Transportation research, Part E. Logistics and transportation review
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    Algorithmic pricing in supply chains: implications for product quality, pricing, and profits

    Song K.Yang H.Huang H.Chen J....
    1.1-1.23页
    查看更多>>摘要：© 2026 Elsevier Ltd.The rapid adoption of algorithmic pricing by retailers, enabled by big data analytics, is reshaping decisions in supply chains and affecting consumer surplus. We develop a game-theoretic model to examine how a retailer’s operation under an algorithmic-pricing regime, compared with a uniform-pricing regime, influences the manufacturer’s product quality and wholesale pricing decisions, as well as profits and consumer surplus. We uncover three key findings. First, algorithmic pricing affects product quality through two opposing effects: the demand segmentation effect, which encourages quality improvement by better matching products to heterogeneous consumers, and the profit compression effect, which discourages quality investment when the consumer distribution is highly skewed. Second, algorithmic pricing generates asymmetric profit impacts for supply chain members. While the retailer benefits more directly from pricing precision, both firms can benefit, particularly under a balanced mix of consumer types, through increased market coverage and reduced channel conflict. Third, when algorithmic reliability is high and consumer heterogeneity is moderate, algorithmic pricing can improve consumer surplus by aligning prices with willingness-to-pay and incentivizing higher quality. As reliability improves and the consumer distribution becomes more balanced, the system can achieve a tripartite win–win that benefits the manufacturer, the retailer, and consumers. These findings highlight the dual, condition-dependent role of algorithmic pricing as both a coordination tool and a quality-enhancement mechanism in supply chains. They also offer managerial implications for the strategic deployment of algorithmic pricing tools and inform policy debates on regulating algorithm-driven markets.
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    With whom to ally? Alliance strategy for EV battery supplier considering echelon utilization and disassembly recycling

    Fang Z.Guo Y.Lou G.Lai Z....
    1.1-1.34页
    查看更多>>摘要：© 2026 Elsevier Ltd.The rapid expansion of the electric vehicle (EV) industry has heightened the need for sustainable and efficient closed-loop supply chains (CLSC) that can simultaneously improve economic returns and mitigate environmental impacts. To address this challenge, this study develops a game-theoretic model from the perspective of the power battery supplier and examines four inter-firm alliance modes: Non-alliance (N), supplier-manufacturer alliance (SM), supplier-recycler alliance (SR), and comprehensive alliance (SMR). The results reveal that (1) in the forward supply chain, suppliers under the SM and SMR modes consistently achieve higher battery capacity and EV sales. In the reverse supply chain, suppliers in alliance modes (SM, SR, SMR) are able to pay lower recycling prices while securing higher recycling quantities. (2) When recycling competition is weak, alliance with the manufacturer improves economic performance, whereas that with the recycler enhances environmental outcomes; however, the two benefits cannot be achieved simultaneously. By contrast, under intense recycling competition, forming a comprehensive alliance allows suppliers to improve both environmental and economic performance. (3) When extending the analysis to include suppliers’ investment in echelon utilization technology innovation, increased recycling competition intensity leads to a decline in the supplier’s echelon utilization performance, thereby amplifying the advantage of the comprehensive alliance.
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    Digital twin-based dynamic co-scheduling with AGV energy management in sea-rail intermodal automated container terminals

    Li J.Chang D.Wen F.Moon I....
    1.1-1.40页
    查看更多>>摘要：© 2026 Elsevier Ltd.To fully leverage the advantages of sea-rail intermodal transport, the automation upgrade of the railway center station (RCS) is essential for enabling seamless connectivity between the RCS and the terminal via automated guided vehicles (AGVs). This transformation introduces complex scheduling challenges for sea-rail intermodal automated container terminals (SRIACTs), including multi-directional container flows, coordination among diverse equipment, and AGV charging requirements with battery management. To address these challenges, this paper investigates the multi-equipment collaborative scheduling problem in SRIACTs with consideration of AGV charging. A mixed-integer programming model is formulated with sequencing, timing, and energy constraints, aiming to jointly minimize makespan and total charging time. To improve computational efficiency in large-scale cases, an improved genetic algorithm based on a decomposition-iteration framework is developed according to problem-specific features. Furthermore, to address operational uncertainties, a digital twin-based hybrid rescheduling framework is extended to enable real-time monitoring, disturbance detection, and rapid response to AGV status and battery levels, thereby enhancing system resilience and scheduling flexibility. Extensive numerical experiments are conducted to validate the effectiveness of the proposed algorithm and rescheduling framework. On this basis, comparative analyses are performed on bi-objective formulations and the flexible charging strategy. Additionally, sensitivity analyses examine the impacts of key factors, including objective weights, charging thresholds, rescheduling thresholds, and the number and layout of charging facilities. The findings provide valuable insights for terminal operators in formulating integrated scheduling strategies, optimizing AGV charging plans, and scientifically deploying charging infrastructure during the RCS automation process, thereby promoting sustainable and intelligent terminal operations.
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    Strategic bundling of information products: Managing consumer group tastes and product versions

    Li M.Ji X.Perera S.C.
    1.1-1.23页
    查看更多>>摘要：© 2026 .Bundling strategies are widely used in the information product market. Unlike physical products, information products typically have limited marginal costs, offer multiple versions, and differ in functionality rather than purely in quality. Motivated by the widespread use of bundling in retail and digital platforms, this study explores how firms can optimize bundling strategies for information products, taking into account varying consumer group tastes and quality differentiation between product versions. We consider a monopolistic firm that produces both a physical product and an information product, deciding on the version to offer as well as its bundling and pricing strategy. In addition to individual-level valuation heterogeneity, we also account for consumers’ group-level tastes toward information products, which influence both individual valuations and the perceived quality of different versions. Our findings reveal that bundling serves not only as a tool for price discrimination but also enables firms to effectively manage heterogeneous group preferences. Specifically, bundling is optimal when shared quality is either very low or very high, and firms may strategically choose between the basic and premium versions depending on consumer heterogeneity. Interestingly, under certain market conditions, bundling can function as a substitute for traditional versioning strategies. Moreover, the taste gap and the quality gap driven by group taste significantly impact a firm’s versioning and bundling strategies. These findings provide managerial insights into how firms can optimize pricing and product offerings in digital markets.
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    Collaborative freight transport service with high-frequency bus transit systems: Optimal bus operation strategies

    Zhou C.Yan Y.Wang D.Z.W.
    1.1-1.27页
    查看更多>>摘要：© 2026 Elsevier Ltd.In the presence of a rapidly growing demand for urban delivery, existing bus services are recommended to offer collaborative freight transport services, especially during off-peak hours when the bus service capacity is excessive for passenger transportation. While the impact of freight transport on the transit service quality has not been explicitly considered in the literature on the topic of collaborative freight transport, this study aims to investigate, from a bus operator’s perspective, how to determine the optimal bus operation strategies to ensure the freight transport demand can be met while a certain level of bus passenger transport service quality is maintained. A mathematical programming approach is applied to formulate the problem, with the objective of minimizing both the operator’s costs, consisting of the bus operation costs and penalty imposed from unsatisfied freight transport demand, and users’ costs focusing primarily on the passengers’ travel time costs. The main bus operation strategies include bus vehicle seating capacity, fleet size, and bus headway, to be optimized to achieve the objective from the operator’s perspective. A generalized Benders decomposition-based solution algorithm is developed to solve the formulated problem efficiently, with completed algorithmic convergence proof. Numerical experiments are carried out to validate the model formulation and solution efficiency. Some of the numerical results indicate a tendency for bus headway to be set longer, leading to longer waiting times, and lower service quality for passenger transport, especially when freight transport demand is high. This highlights the importance of this study in offering bus service operators analysis tools in managing the trade-off between supplying freight transport service and the compromised passenger transport service quality.
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    Climate shock impacts on supply chains: the case of the truckload spot market

    Hsu S.Balthrop A.Pellathy D.Kulpa T....
    1.1-1.16页
    查看更多>>摘要：© 2025 Elsevier Ltd.Climate shocks increasingly disrupt supply chains, yet research has focused primarily on mitigation strategies (i.e., carbon reduction), leaving adaptation strategies comparatively understudied. We begin to fill this gap by studying how transportation managers within a supply chain respond to climate-related shocks, defined as a month in which a state’s exposure to extreme temperature or precipitation events rises significantly, measured by the custom University of Tennessee Climate Index (UTCI), which combines anomalies in high/low temperature and heavy precipitation with population exposure. Drawing on structured interviews with transportation managers, we uncover beliefs that shippers tend to be less demand-responsive in the short-term to climate-related shocks, often prioritizing the desire to move freight at any reasonable cost. Motor carriers, in contrast, are more sensitive to price. To test these qualitative assessments, we regress monthly state-level truckload spot market data from the contiguous 48 states on the UTCI in reduced-form two-way fixed effects specifications, finding that a one-standard-deviation increase in climate shocks increases freight prices by 1.9%, with minimal effects on freight volume, indicating that market adjustments occur primarily through price rather than quantity. We further estimate IV specifications based on three-stage least squares (3SLS) models to disentangle the net causal effects from the reduced form specification. Consistent with our interviews, we find motor carriers are more sensitive than shippers to climate shocks. The results have important implications, offering shippers, carriers, and brokers with concrete price-change benchmarks they can use to budget transportation spend, design contract–spot portfolios, and plan capacity during climate shocks.
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    Linear carrot-and-stick: Compensation design with ordering delegation and demand updating

    Yu Y.Jiao X.Sun L.
    1.1-1.19页
    查看更多>>摘要：© 2026 Elsevier Ltd.Delegating ordering to the salesforce leverages local market knowledge but complicates incentive alignment. Motivated by data from a major Amazon apparel seller, we study a Linear Carrot-and-Stick (LCS) scheme that couples a sales commission (“carrot”) with a leftover inventory penalty (“stick”). Using weekly SKU-level transaction data from June 2017 to May 2019, we observe that the adoption of LCS decreased the firm’s total shipments and sales relative to the prior Linear Pure-Commission Scheme (LPS). To interpret these patterns and offer design guidance, we develop a two-period principal-agent model in which the salesperson updates demand forecasts based on realized outcomes and also chooses the effort and places orders. We show that the optimal commission reflects the salesperson’s ability to convert effort into sales, while the penalty ratio balances overstocking liabilities with understocking opportunity costs, akin to the critical ratio in the newsvendor problem. To ensure that the salesforce utility remains competitive despite inventory penalties, we examine a utility protection mechanism, finding that higher values for both the components, carrot and stick, are essential for retaining a valuable person who faces attractive employment alternatives. A numerical study of the partner’s top-selling SKUs indicates that LCS can deliver a win-win outcome, improving both firm profitability and salesperson motivation compared to LPS. We further extend the analysis to information asymmetry, target-based demand updating, Bayesian demand updating, and a two-product setting, all of which widely confirm the robustness of our findings.
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    Drone scheduling optimization for continuous sea area monitoring

    Liu Y.Xia J.Xu Z.
    1.1-1.17页
    查看更多>>摘要：© 2026 Elsevier Ltd.Drones equipped with industrial sensors offer a promising solution for environmental surveillance. This paper studies a new drone scheduling problem for sea area emission surveillance, where drones are utilized to monitor vessel emissions across a continuous sea area for a given planning horizon. The challenges of this optimization problem stem from the varying monitoring requirements within a continuous area due to vessel dynamics and the operational issues of drone deployment, such as multi-trip operations. To address these issues, we discretize the continuous sea area using hexagonal grids and represent the problem through a time-expanded network, resulting in a mixed-integer linear programming formulation for its optimization. To solve large-scale instances, we propose a Lagrangian relaxation-based approach enhanced with a customized lower bounding heuristic. Numerical experiments demonstrate that our approach is very effective and efficient in obtaining high-quality solutions. We conduct a real-world case study based on the Gulf of Mexico’s AIS data to examine the practical implementation of the proposed optimization tool. Furthermore, we investigate how the drone’s operational factors, including the sensor range, endurance, and operational flexibility, affect the monitoring performance.
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    The efficacy of decentralized disaster relief resource allocation within communities: The role of community-based sharing captains

    Wang O.Li Z.Chen C.
    1.1-1.22页
    查看更多>>摘要：© 2026 Elsevier Ltd.Motivated by numerous observations that neighbors want to help and be helped by each other, this study investigates the feasibility of a decentralized resource allocation strategy where sharing captains distribute disaster relief resources within their community. Here sharing captains are residents themselves who step up during a disaster and take on the role of sharing/distributing resources with/to their neighbors. Using data from two socioeconomically different communities in Seattle, we simulate and compare the efficacy of the proposed decentralized strategy and the status quo fixed-point distribution method that relies on residents to come and get resources on their own. Our findings reveal that the decentralized approach significantly reduces residents’ deprivation costs (a measure on residents’ suffering due to resource shortage) and reaches 100% resource coverage faster than the fixed-point distribution strategy. For both communities, our experiments suggest that an effective range of sharing captains is between 30 and 40. Though the success of the decentralized strategy lies fundamentally on residents’ willingness to share, a satisfactory outcome can be reached even when a substantial share of residents (40%) are unwilling to share with anybody. This is in contrast to only 3% and 7% of the residents in these two communities who are found to be unwilling to share with anybody. Furthermore, sharing captains’ own biases in distributing resources appear to have a marginal effect on the resource allocation outcomes. On selecting sharing captains, a comprehensive strategy considering multiple factors (sharing preferences, number of social ties, and civic engagement) shall be adopted.
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    Stochastic modeling and design of truck platooning strategies considering platoon dynamics

    Gupta R.Roy D.Chakrabarti S.Koonthalakadu Baby D....
    1.1-1.49页
    查看更多>>摘要：© 2026 The Author(s).Road transportation via trucks is a dominant mode for long-haul freight transport across countries. However, due to their significant dependence on fossil fuels, trucks are a large contributor to carbon emissions. Hence, new technology-driven solutions such as truck platoons are gaining momentum. While platoons promise to reduce fuel costs and emissions, they may increase transportation time due to additional coordination delays, such as the time required for platoon formation. In this research, we examine the performance trade-offs between platoon fuel savings and excess delay costs resulting from waiting for platoon formation among three platoon formation strategies: intermittent, continuous, and opportunistic. We develop a novel Closed Queuing Network model that captures the dynamics of platoons, as well as the stochasticity in truck travel times, and provides realistic estimates of platoon wait times and vehicle throughput. The platoon formation delays and size-dependent travel times are modeled using merging and load-dependent nodes, respectively, and analyzed through a continuous-time Markov chain. Our study provides key insights into the impact of increasing platoon size on performance measures, including system throughput and mean waiting time. With platooning, the network throughput capacity is reduced; however, fuel savings are realized. For a given network topology, we can identify an optimal platoon formation strategy that maximizes the throughput and fuel efficiency, while simultaneously minimizing vehicle waiting costs.
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