Pub Date : 2026-04-01Epub Date: 2026-01-22DOI: 10.1016/j.tre.2026.104695
Angela S. Bergantino , Mattia Borsati , Xavier Fageda , Mario Intini
Airline financial distress is a global phenomenon, yet its market implications remain underexplored. A notable case is the restructuring of Italy’s flag carrier, Alitalia, which went bankrupt and ceased operations on October 14, 2021. The following day, ITA Airways took over, inheriting parts of Alitalia’s network while operating under a distinct governance and management structure. This article examines how this transition has affected Italy’s aviation market and fare dynamics. By estimating price regressions at the route level using monthly fare data from 2017 to 2023, and by accounting for the non-random selection of routes retained by the airline after the reorganization, we find that the restructuring led to lower fares in the domestic market but higher prices on international routes, particularly for long-haul flights. In response to competitive pressures, ITA has adopted a more complex pricing strategy: functioning as a low-cost carrier domestically while raising fares on long-haul routes.
{"title":"Too big to stay? The restructuring of Italy’s flag carrier and its consequences on airline pricing","authors":"Angela S. Bergantino , Mattia Borsati , Xavier Fageda , Mario Intini","doi":"10.1016/j.tre.2026.104695","DOIUrl":"10.1016/j.tre.2026.104695","url":null,"abstract":"<div><div>Airline financial distress is a global phenomenon, yet its market implications remain underexplored. A notable case is the restructuring of Italy’s flag carrier, <em>Alitalia</em>, which went bankrupt and ceased operations on October 14, 2021. The following day, <em>ITA Airways</em> took over, inheriting parts of <em>Alitalia</em>’s network while operating under a distinct governance and management structure. This article examines how this transition has affected Italy’s aviation market and fare dynamics. By estimating price regressions at the route level using monthly fare data from 2017 to 2023, and by accounting for the non-random selection of routes retained by the airline after the reorganization, we find that the restructuring led to lower fares in the domestic market but higher prices on international routes, particularly for long-haul flights. In response to competitive pressures, <em>ITA</em> has adopted a more complex pricing strategy: functioning as a low-cost carrier domestically while raising fares on long-haul routes.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104695"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-21DOI: 10.1016/j.tre.2026.104689
Yugang Yu , Xiaoting Jiao , Libo Sun
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.
{"title":"Linear carrot-and-stick: Compensation design with ordering delegation and demand updating","authors":"Yugang Yu , Xiaoting Jiao , Libo Sun","doi":"10.1016/j.tre.2026.104689","DOIUrl":"10.1016/j.tre.2026.104689","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104689"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-21DOI: 10.1016/j.tre.2025.104654
Chang Zhou, Yiming Yan, David Z. W. Wang
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.
{"title":"Collaborative freight transport service with high-frequency bus transit systems: Optimal bus operation strategies","authors":"Chang Zhou, Yiming Yan, David Z. W. Wang","doi":"10.1016/j.tre.2025.104654","DOIUrl":"10.1016/j.tre.2025.104654","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104654"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the accelerating breakthroughs in autonomous driving technology have catalyzed massive capital inflows into Robotaxi development, prompting ride-hailing service leading firms and emerging startups to commercialize Robotaxi service. However, considering the passengers’ attitude differences towards Robotaxi service and its co-opetition with traditional ride-hailing services, firms face complex strategic trade-offs when entering the Robotaxi market without giving up current ride-hailing services. In this paper, we develop a differentiated consumer utility model involving a traditional ride-hailing firm and a Robotaxi firm to examine the incentive for the traditional ride-hailing firm to also develop Robotaxi service, and we find that the effects of intra-firm competition and inter-firm competition lead to two counterintuitive results: (1) when passengers prefer human-driven service over Robotaxi service, the traditional ride-hailing firm surprisingly prefers to develop Robotaxi service; (2) when passengers exhibit preferences for Robotaxi service over human-driven service, the traditional ride-hailing firm is more likely to develop Robotaxi service only when the investment is less efficient, especially when the operational cost advantage of Robotaxi service is significant. Further, we find that the traditional ride-hailing firm’s Robotaxi development brings higher passenger surplus but may hurt the overall social welfare.
{"title":"Should traditional ride-hailing firms develop robotaxi service? Intra-firm and inter-firm competition analysis","authors":"Baozhuang Niu , Hongzhi Wang , Guang Xiao , Haotao Xu","doi":"10.1016/j.tre.2026.104697","DOIUrl":"10.1016/j.tre.2026.104697","url":null,"abstract":"<div><div>In recent years, the accelerating breakthroughs in autonomous driving technology have catalyzed massive capital inflows into Robotaxi development, prompting ride-hailing service leading firms and emerging startups to commercialize Robotaxi service. However, considering the passengers’ attitude differences towards Robotaxi service and its co-opetition with traditional ride-hailing services, firms face complex strategic trade-offs when entering the Robotaxi market without giving up current ride-hailing services. In this paper, we develop a differentiated consumer utility model involving a traditional ride-hailing firm and a Robotaxi firm to examine the incentive for the traditional ride-hailing firm to also develop Robotaxi service, and we find that the effects of intra-firm competition and inter-firm competition lead to two counterintuitive results: (1) when passengers prefer human-driven service over Robotaxi service, the traditional ride-hailing firm surprisingly prefers to develop Robotaxi service; (2) when passengers exhibit preferences for Robotaxi service over human-driven service, the traditional ride-hailing firm is more likely to develop Robotaxi service only when the investment is less efficient, especially when the operational cost advantage of Robotaxi service is significant. Further, we find that the traditional ride-hailing firm’s Robotaxi development brings higher passenger surplus but may hurt the overall social welfare.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104697"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-27DOI: 10.1016/j.tre.2026.104701
Yun Liu , Jun Xia , Zhou Xu
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.
{"title":"Drone scheduling optimization for continuous sea area monitoring","authors":"Yun Liu , Jun Xia , Zhou Xu","doi":"10.1016/j.tre.2026.104701","DOIUrl":"10.1016/j.tre.2026.104701","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104701"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-27DOI: 10.1016/j.tre.2026.104710
Majid Karimi , Nima Zaerpour , René de Koster
In warehouses, products are often not stored in their optimal positions, elongating retrieval and order picking time. A main reason is that storage assignment is based on historical demand frequency, whereas current demand patterns might just differ. However, as many warehouses are now automated or robotized, opportunities exist to dynamically and opportunistically reposition product loads based on real known demand and still reduce the makespan (the total time needed for retrieval, storage, and optional repositioning). We investigate the optimal retrieval of a known block of requests by explicitly additionally allowing in-between repositioning options. Surprisingly, in spite of the extra work and time involved, we show opportunistic repositioning may indeed be beneficial for reducing the makespan. We study the problem for two automated unit-load storage warehouses: automated storage and retrieval (AS/R) crane-based systems and robotic mobile fulfillment (RMF) systems, which have different travel metrics for the retrieval robots. The data-driven storage and repositioning (DDSR) problem, formulated as an integer linear program, leverages actual customer order data. The problem appears to be intractable for realistic systems due to the combinatorial nature of the possible repositions. We then reformulate the model, making it more tractable for moderate-sized problems. This model appears to beat real-life storage assignment heuristics like closest-open location assignment or demand-frequency class-based storage (even when these have full foresight of demand changes). The benefits appear to be around a 14%-30% shorter makespan, depending on the number of loads to be retrieved. For larger rack space utilization, the benefits decrease (since there are fewer options for repositioning). The method is sufficiently fast to be used in real warehouse systems, e.g., by using a rolling horizon policy where repositions are calculated for the next block of requests while the current requests are executed. Our method offers managers an additional powerful tool to reduce system response time and thereby increase throughput capacity by smarter scheduling of their automated equipment and more efficient use of available storage space.
{"title":"“Keeping up with changing customer demand”: An adaptive data-driven approach for storage and repositioning decisions in automated g warehouses","authors":"Majid Karimi , Nima Zaerpour , René de Koster","doi":"10.1016/j.tre.2026.104710","DOIUrl":"10.1016/j.tre.2026.104710","url":null,"abstract":"<div><div>In warehouses, products are often not stored in their optimal positions, elongating retrieval and order picking time. A main reason is that storage assignment is based on historical demand frequency, whereas current demand patterns might just differ. However, as many warehouses are now automated or robotized, opportunities exist to dynamically and opportunistically reposition product loads based on real known demand and still reduce the makespan (the total time needed for retrieval, storage, and optional repositioning). We investigate the optimal retrieval of a known block of requests by explicitly additionally allowing in-between repositioning options. Surprisingly, in spite of the extra work and time involved, we show opportunistic repositioning may indeed be beneficial for reducing the makespan. We study the problem for two automated unit-load storage warehouses: automated storage and retrieval (AS/R) crane-based systems and robotic mobile fulfillment (RMF) systems, which have different travel metrics for the retrieval robots. The data-driven storage and repositioning (DDSR) problem, formulated as an integer linear program, leverages actual customer order data. The problem appears to be intractable for realistic systems due to the combinatorial nature of the possible repositions. We then reformulate the model, making it more tractable for moderate-sized problems. This model appears to beat real-life storage assignment heuristics like closest-open location assignment or demand-frequency class-based storage (even when these have full foresight of demand changes). The benefits appear to be around a 14%-30% shorter makespan, depending on the number of loads to be retrieved. For larger rack space utilization, the benefits decrease (since there are fewer options for repositioning). The method is sufficiently fast to be used in real warehouse systems, <em>e.g.</em>, by using a rolling horizon policy where repositions are calculated for the next block of requests while the current requests are executed. Our method offers managers an additional powerful tool to reduce system response time and thereby increase throughput capacity by smarter scheduling of their automated equipment and more efficient use of available storage space.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104710"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-19DOI: 10.1016/j.tre.2026.104677
Xiaohan Zhou , Shaopeng Zhong , Hai Yang , Yunhai Gong , Xiantao Xiao , Yu Jiang
This study develops a multi-objective bi-level programming model to identify the Pareto optimal combined regulatory strategy that simultaneously accounts for passengers, taxi drivers, ridesourcing vehicle (RSV) drivers, and the transportation network company (TNC). The upper level determines four regulatory controls, including the RSV fleet cap, taxi fare rate, government-guided RSV fare rate, and TNC wage rate floor, while the lower level obtains the steady-state market performance, which is formulated as a fixed-point problem and approximated through iterative agent-based simulations. To solve the model, a multi-objective Bayesian optimization algorithm is developed. Based on the DiDi dataset collected from Hangzhou City in 2018, our experiments demonstrate that no regulatory strategy can simultaneously benefit all stakeholders. If the government considers maximizing vehicle utilization as a secondary criterion, then it should decrease the RSV fleet cap, impose higher fare rates, and allow the TNC to pay lower wages, compared with the benchmark scenario. Furthermore, it is recommended that the government should avoid regulations that primarily favor passengers or the TNC, as our results reveal that such policies could harm other stakeholders and reduce vehicle utilization by up to 11.6%. Finally, if passengers’ impatience is overlooked, taxi drivers may lose 23.3% of potential profits.
{"title":"Pareto optimal regulatory strategies for coupled ridesourcing and taxi markets with impatient passengers","authors":"Xiaohan Zhou , Shaopeng Zhong , Hai Yang , Yunhai Gong , Xiantao Xiao , Yu Jiang","doi":"10.1016/j.tre.2026.104677","DOIUrl":"10.1016/j.tre.2026.104677","url":null,"abstract":"<div><div>This study develops a multi-objective bi-level programming model to identify the Pareto optimal combined regulatory strategy that simultaneously accounts for passengers, taxi drivers, ridesourcing vehicle (RSV) drivers, and the transportation network company (TNC). The upper level determines four regulatory controls, including the RSV fleet cap, taxi fare rate, government-guided RSV fare rate, and TNC wage rate floor, while the lower level obtains the steady-state market performance, which is formulated as a fixed-point problem and approximated through iterative agent-based simulations. To solve the model, a multi-objective Bayesian optimization algorithm is developed. Based on the DiDi dataset collected from Hangzhou City in 2018, our experiments demonstrate that no regulatory strategy can simultaneously benefit all stakeholders. If the government considers maximizing vehicle utilization as a secondary criterion, then it should decrease the RSV fleet cap, impose higher fare rates, and allow the TNC to pay lower wages, compared with the benchmark scenario. Furthermore, it is recommended that the government should avoid regulations that primarily favor passengers or the TNC, as our results reveal that such policies could harm other stakeholders and reduce vehicle utilization by up to 11.6%. Finally, if passengers’ impatience is overlooked, taxi drivers may lose 23.3% of potential profits.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104677"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-08DOI: 10.1016/j.tre.2025.104627
Mohammad Reza Ghorbanali Zadegan , Zhaomiao Guo
Transitioning to zero-emission heavy-duty freight vehicles, such as fuel cell electric vehicles (FCEVs), could contribute to mitigating the environmental footprint in the freight sector. However, sustainable and economical production, transportation, and storage of hydrogen fuels remain challenging. The goal of this study is to leverage the flexibility of logistics delivery to maximize the utilization of green hydrogen while balancing the freight operational costs. To achieve this goal, we develop a mixed integer programming (MIP) model that optimizes the planning, routing/scheduling, refueling, and on-site “green” hydrogen production to support the operations of FCEVs. The model strategically determines the locations and sizes of hydrogen refueling stations (HRSs) and the corresponding vehicle routing/refueling plans to minimize total costs, while utilizing solar-powered on-site hydrogen generation in freight operations. Our model was initially tested using the Solomon benchmark and later implemented in a real-world case study of freight distribution systems in Florida to evaluate its robustness and efficiency. Computational performance between exact methods and a customized Adaptive Large Neighborhood Search (ALNS) metaheuristic algorithm are also compared. Extensive sensitivity analyses were conducted on operational and environmental parameters to generate numerical insights. We found that strategic trade-offs in routing the FCEV fleet and placing HRSs, coupled with optimized solar hydrogen production, substantially reduce operational costs and enhance sustainability.
{"title":"Optimizing on-site green hydrogen consumption using heavy-duty hydrogen fuel cell electric vehicles","authors":"Mohammad Reza Ghorbanali Zadegan , Zhaomiao Guo","doi":"10.1016/j.tre.2025.104627","DOIUrl":"10.1016/j.tre.2025.104627","url":null,"abstract":"<div><div>Transitioning to zero-emission heavy-duty freight vehicles, such as fuel cell electric vehicles (FCEVs), could contribute to mitigating the environmental footprint in the freight sector. However, sustainable and economical production, transportation, and storage of hydrogen fuels remain challenging. The goal of this study is to leverage the flexibility of logistics delivery to maximize the utilization of green hydrogen while balancing the freight operational costs. To achieve this goal, we develop a mixed integer programming (MIP) model that optimizes the planning, routing/scheduling, refueling, and on-site “green” hydrogen production to support the operations of FCEVs. The model strategically determines the locations and sizes of hydrogen refueling stations (HRSs) and the corresponding vehicle routing/refueling plans to minimize total costs, while utilizing solar-powered on-site hydrogen generation in freight operations. Our model was initially tested using the Solomon benchmark and later implemented in a real-world case study of freight distribution systems in Florida to evaluate its robustness and efficiency. Computational performance between exact methods and a customized Adaptive Large Neighborhood Search (ALNS) metaheuristic algorithm are also compared. Extensive sensitivity analyses were conducted on operational and environmental parameters to generate numerical insights. We found that strategic trade-offs in routing the FCEV fleet and placing HRSs, coupled with optimized solar hydrogen production, substantially reduce operational costs and enhance sustainability.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104627"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-06DOI: 10.1016/j.tre.2025.104653
Zijian Zhao , Sen Li
This paper studies the discriminatory order assignment and payment-setting strategies for on-demand food-delivery platforms. We consider an on-demand food-delivery platform that coordinates customers, couriers, and restaurants to maximize the profit. It determines how to bundle orders, assign orders to couriers, and set payments to couriers in real-time. These decisions are made in a personalized manner, depending on the historical data collected from each of the couriers, such as the order acceptance and rejection rates under distinct scenarios of order assignment and payment values. A Markov Decision Process is formulated for the courier, capturing the decisions of the platform (including differentiated order assignment/bundling strategies and the discriminatory payment-settings decisions) while considering its dependence on the personalized work-related data of each individual courier. To derive the optimal policies, we propose a novel multi-action and multi-agent deep reinforcement learning framework, where a double Deep Q-Network is employed to develop discrete order assignment strategies, and double Proximal Policy Optimization is utilized to determine continuous payment decisions. Within this learning framework, we introduce a novel neural network architecture that leverages the Query-Key attention mechanism to transform multiplicative time complexities into additive computation complexity for order assignment, and we adopt a variable-length Bi-LSTM module that compresses variable-length order sequence into a fixed-dimensional feature space to enhance scalability. The proposed neural network and algorithmic framework was validated in a case study using real-world food-delivery data from Hong Kong. By comparing the proposed method with a vanilla MLP-based neural network architecture, we find that the proposed neural network architecture significantly enhances platform performance: it increases the number of orders served by 5.25%, reduces platform expenses by 10%, and improves the overall reward of the platform by over 50%. Additionally, our results reveal that couriers with higher order rejection rates receive more orders during peak hours but earn lower wages. This counterintuitive finding is attributed to a strategic approach by the platform to differentiate order allocation: instead of simply allocating fewer orders to couriers with higher rejection rates, the platform preferentially assigns longer-distance trips to couriers with a higher likelihood of order acceptance. These findings expose the implicit biases in the discriminatory algorithms used by the profit-maximizing platform and highlight potential areas for governmental regulatory intervention. The code of this paper is provided at https://github.com/RS2002/Discriminatory-Food-Delivery.
{"title":"Discriminatory order assignment and payment-setting of on-demand food-delivery platforms: A multi-action and multi-agent reinforcement learning framework","authors":"Zijian Zhao , Sen Li","doi":"10.1016/j.tre.2025.104653","DOIUrl":"10.1016/j.tre.2025.104653","url":null,"abstract":"<div><div>This paper studies the discriminatory order assignment and payment-setting strategies for on-demand food-delivery platforms. We consider an on-demand food-delivery platform that coordinates customers, couriers, and restaurants to maximize the profit. It determines how to bundle orders, assign orders to couriers, and set payments to couriers in real-time. These decisions are made in a personalized manner, depending on the historical data collected from each of the couriers, such as the order acceptance and rejection rates under distinct scenarios of order assignment and payment values. A Markov Decision Process is formulated for the courier, capturing the decisions of the platform (including differentiated order assignment/bundling strategies and the discriminatory payment-settings decisions) while considering its dependence on the personalized work-related data of each individual courier. To derive the optimal policies, we propose a novel multi-action and multi-agent deep reinforcement learning framework, where a double Deep Q-Network is employed to develop discrete order assignment strategies, and double Proximal Policy Optimization is utilized to determine continuous payment decisions. Within this learning framework, we introduce a novel neural network architecture that leverages the Query-Key attention mechanism to transform multiplicative time complexities into additive computation complexity for order assignment, and we adopt a variable-length Bi-LSTM module that compresses variable-length order sequence into a fixed-dimensional feature space to enhance scalability. The proposed neural network and algorithmic framework was validated in a case study using real-world food-delivery data from Hong Kong. By comparing the proposed method with a vanilla MLP-based neural network architecture, we find that the proposed neural network architecture significantly enhances platform performance: it increases the number of orders served by 5.25%, reduces platform expenses by 10%, and improves the overall reward of the platform by over 50%. Additionally, our results reveal that couriers with higher order rejection rates receive more orders during peak hours but earn lower wages. This counterintuitive finding is attributed to a strategic approach by the platform to differentiate order allocation: instead of simply allocating fewer orders to couriers with higher rejection rates, the platform preferentially assigns longer-distance trips to couriers with a higher likelihood of order acceptance. These findings expose the implicit biases in the discriminatory algorithms used by the profit-maximizing platform and highlight potential areas for governmental regulatory intervention. The code of this paper is provided at <span><span>https://github.com/RS2002/Discriminatory-Food-Delivery</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104653"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-10DOI: 10.1016/j.tre.2025.104655
Shuaiqi Zhao , Hualong Yang , Yadong Wang , Zaili Yang
Significant delays caused by disruption events, coupled with regular uncertainties, pose challenges to risk avoidance and vessel schedule recovery problem (RA-VSRP) in liner container shipping services. To address this, we propose a new optimization framework that incorporates a hybrid risk aversion measure with three recovery strategies, including sailing speed adjustment, port skipping, and transshipment. The framework systematically combines ex-ante decision-making and in-progress decision-making. The former helps shorten vessel schedule recovery time and costs by quickly responding to disruption events, while the latter improves the flexibility of selecting vessel schedule recovery strategies. By adopting a scenario-based approach to jointly capture regular uncertainties and disruption events, RA-VSRP is formulated as a chance-constrained two-stage stochastic programming model, where conditional value-at-risk (CVaR) is used as the risk measure. An exact Benders decomposition-based branch-and-cut algorithm is employed to efficiently solve the computationally challenging model. We develop two algorithmic variants based on alternative representations of CVaR. Extensive numerical experiments demonstrate the applicability of the model and the computational efficiency of the algorithm. The results show that the proposed framework can provide reliable vessel schedule recovery solutions through sailing speed adjustments, port skipping, and transshipment. The findings provide managerial insights for shipping companies regarding schedule recovery, risk aversion, and cost control.
{"title":"A risk-averse two-stage stochastic programming model for vessel schedule recovery in liner shipping service","authors":"Shuaiqi Zhao , Hualong Yang , Yadong Wang , Zaili Yang","doi":"10.1016/j.tre.2025.104655","DOIUrl":"10.1016/j.tre.2025.104655","url":null,"abstract":"<div><div>Significant delays caused by disruption events, coupled with regular uncertainties, pose challenges to risk avoidance and vessel schedule recovery problem (RA-VSRP) in liner container shipping services. To address this, we propose a new optimization framework that incorporates a hybrid risk aversion measure with three recovery strategies, including sailing speed adjustment, port skipping, and transshipment. The framework systematically combines ex-ante decision-making and in-progress decision-making. The former helps shorten vessel schedule recovery time and costs by quickly responding to disruption events, while the latter improves the flexibility of selecting vessel schedule recovery strategies. By adopting a scenario-based approach to jointly capture regular uncertainties and disruption events, RA-VSRP is formulated as a chance-constrained two-stage stochastic programming model, where conditional value-at-risk (CVaR) is used as the risk measure. An exact Benders decomposition-based branch-and-cut algorithm is employed to efficiently solve the computationally challenging model. We develop two algorithmic variants based on alternative representations of CVaR. Extensive numerical experiments demonstrate the applicability of the model and the computational efficiency of the algorithm. The results show that the proposed framework can provide reliable vessel schedule recovery solutions through sailing speed adjustments, port skipping, and transshipment. The findings provide managerial insights for shipping companies regarding schedule recovery, risk aversion, and cost control.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104655"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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