Pub Date : 2026-01-27DOI: 10.1016/j.tre.2026.104688
Josu Blanco, Antònia Tugores, José J. Ramasco, Massimiliano Zanin
The modelling and understanding of how flight delays propagate between airports of a country or region is a major topic of research in air transport that has been tackled through different techniques in the last decade. Much less attention has been devoted to the large-scale structure of the propagation, i.e. if and how delays can propagate between regional networks and continents. By leveraging on two complementary analysis approaches, we show how such propagation takes place across the main world regions, describing how it is modulated by seasons, the number of flights connecting them and their relative distance. We further propose a methodology to detect which airports act as gateways for global-scale propagation, and discuss the operational applications of these findings.
{"title":"The structure of global delay propagation in air transport","authors":"Josu Blanco, Antònia Tugores, José J. Ramasco, Massimiliano Zanin","doi":"10.1016/j.tre.2026.104688","DOIUrl":"10.1016/j.tre.2026.104688","url":null,"abstract":"<div><div>The modelling and understanding of how flight delays propagate between airports of a country or region is a major topic of research in air transport that has been tackled through different techniques in the last decade. Much less attention has been devoted to the large-scale structure of the propagation, i.e. if and how delays can propagate between regional networks and continents. By leveraging on two complementary analysis approaches, we show how such propagation takes place across the main world regions, describing how it is modulated by seasons, the number of flights connecting them and their relative distance. We further propose a methodology to detect which airports act as gateways for global-scale propagation, and discuss the operational applications of these findings.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104688"},"PeriodicalIF":8.8,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072516","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 : 2026-01-23DOI: 10.1016/j.tre.2026.104683
Hatef Kiabakht, Mohsen S. Sajadieh
The rapid growth of e-commerce has significantly increased the complexity of warehouse operations, particularly in robotic mobile fulfillment centers (RMFCs), where decision-making under uncertain customer demand poses substantial challenges. This study proposes an integrated two-stage stochastic optimization model that jointly addresses item-to-pod assignment, pod positioning, order allocation, pod selection, and sequencing decisions. Item shortages are explicitly incorporated into the model to enhance operational robustness under demand uncertainty.
To manage the resulting computational complexity, we develop a prioritization-based item assignment strategy combined with a clustering-oriented order allocation mechanism, embedded within a tailored heuristic algorithm. Computational experiments show that the proposed heuristic achieves near-optimal performance on small-scale instances, with solution gaps of approximately 9–14% relative to exact solutions. For large-scale instances, the heuristic consistently outperforms the solver’s incumbent solutions by 9–29% and yields substantially better results than established metaheuristic benchmarks, including genetic algorithms and simulated annealing, whose deviations increase to 45–56% as problem size grows, while maintaining practical computation times.
Sensitivity analyses further demonstrate that increasing pod capacity and improving replenishment center placement can reduce robot travel distances by up to 30%. In addition, lower demand dispersion and structured item assignment significantly mitigate shortages and enhance overall system efficiency. Comparative experiments against sequential planning approaches confirm that the integrated stochastic framework delivers up to 20% reductions in robot travel under high demand variability, albeit at moderately higher computational cost.
From a managerial perspective, these improvements translate into substantial operational and economic benefits. Industry benchmarks suggest that even moderate reductions in robot travel distance (15–20%) can yield annual cost savings ranging from several hundred thousand dollars in medium-scale facilities to multi-million-dollar savings in large-scale RMFC deployments. Overall, the results highlight the strong practical value of integrated stochastic planning for improving efficiency and resilience in robotic fulfillment systems.
{"title":"Integrated approach for operations in robotic mobile fulfillment centers under order uncertainty","authors":"Hatef Kiabakht, Mohsen S. Sajadieh","doi":"10.1016/j.tre.2026.104683","DOIUrl":"10.1016/j.tre.2026.104683","url":null,"abstract":"<div><div>The rapid growth of e-commerce has significantly increased the complexity of warehouse operations, particularly in robotic mobile fulfillment centers (RMFCs), where decision-making under uncertain customer demand poses substantial challenges. This study proposes an integrated two-stage stochastic optimization model that jointly addresses item-to-pod assignment, pod positioning, order allocation, pod selection, and sequencing decisions. Item shortages are explicitly incorporated into the model to enhance operational robustness under demand uncertainty.</div><div>To manage the resulting computational complexity, we develop a prioritization-based item assignment strategy combined with a clustering-oriented order allocation mechanism, embedded within a tailored heuristic algorithm. Computational experiments show that the proposed heuristic achieves near-optimal performance on small-scale instances, with solution gaps of approximately 9–14% relative to exact solutions. For large-scale instances, the heuristic consistently outperforms the solver’s incumbent solutions by 9–29% and yields substantially better results than established metaheuristic benchmarks, including genetic algorithms and simulated annealing, whose deviations increase to 45–56% as problem size grows, while maintaining practical computation times.</div><div>Sensitivity analyses further demonstrate that increasing pod capacity and improving replenishment center placement can reduce robot travel distances by up to 30%. In addition, lower demand dispersion and structured item assignment significantly mitigate shortages and enhance overall system efficiency. Comparative experiments against sequential planning approaches confirm that the integrated stochastic framework delivers up to 20% reductions in robot travel under high demand variability, albeit at moderately higher computational cost.</div><div>From a managerial perspective, these improvements translate into substantial operational and economic benefits. Industry benchmarks suggest that even moderate reductions in robot travel distance (15–20%) can yield annual cost savings ranging from several hundred thousand dollars in medium-scale facilities to multi-million-dollar savings in large-scale RMFC deployments. Overall, the results highlight the strong practical value of integrated stochastic planning for improving efficiency and resilience in robotic fulfillment systems.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104683"},"PeriodicalIF":8.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033364","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 : 2026-01-23DOI: 10.1016/j.tre.2026.104690
Olivia Wang , Zhengyang Li , Cynthia Chen
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.
{"title":"The efficacy of decentralized disaster relief resource allocation within communities: The role of community-based sharing captains","authors":"Olivia Wang , Zhengyang Li , Cynthia Chen","doi":"10.1016/j.tre.2026.104690","DOIUrl":"10.1016/j.tre.2026.104690","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104690"},"PeriodicalIF":8.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033361","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 : 2026-01-23DOI: 10.1016/j.tre.2026.104699
Hongtao Hu , Kejian Jiang , Zhu Wang , Jia Shu
Achieving robustness has become an essential issue due to the significant volatility of the logistics networks. Current works prioritize the demand uncertainty, but not sufficiently consider the financial budget uncertainty in warehousing. This deficiency renders the collaborators with weak financial endurance intractable to maintain the scheme robustness, impairing the overall network resilience. Therefore, inspired by the Nash equilibrium, a logistics network design method focusing on capacity sharing is proposed. This method allows participants to share capacity in the distribution centers, improving resilience and reducing costs. Firstly, a two-stage robust model considering the uncertainty of demand and financial budget is established to minimize the operating costs of the logistics network. Then, the Nash equilibrium-based constraints are incorporated into the model to ensure a fair distribution of benefits and costs among participants. Subsequently, a two-stage method is designed with an enhanced column and constraint generation algorithm (CCG) using optimal cut, and reverse Nash equilibrium-based constraints are proposed for the worst financial condition. The effectiveness of the algorithm and model is verified through a series of numerical benchmarks and sensitivity analysis for Nash equilibrium-based constraints, sharing restrictions, uncertainty of demand and financial budget. The results show that the proposed method is efficient and flexible when incorporating capacity sharing and highlighting the influence of the Nash equilibrium-based constraints. Finally, it presents that the Nash equilibrium-based constraints are more suitable for logistics networks through sharing alliances.
{"title":"Enhanced column-and-constraint generation algorithm for robust logistics network design problem with capacity sharing","authors":"Hongtao Hu , Kejian Jiang , Zhu Wang , Jia Shu","doi":"10.1016/j.tre.2026.104699","DOIUrl":"10.1016/j.tre.2026.104699","url":null,"abstract":"<div><div>Achieving robustness has become an essential issue due to the significant volatility of the logistics networks. Current works prioritize the demand uncertainty, but not sufficiently consider the financial budget uncertainty in warehousing. This deficiency renders the collaborators with weak financial endurance intractable to maintain the scheme robustness, impairing the overall network resilience. Therefore, inspired by the Nash equilibrium, a logistics network design method focusing on capacity sharing is proposed. This method allows participants to share capacity in the distribution centers, improving resilience and reducing costs. Firstly, a two-stage robust model considering the uncertainty of demand and financial budget is established to minimize the operating costs of the logistics network. Then, the Nash equilibrium-based constraints are incorporated into the model to ensure a fair distribution of benefits and costs among participants. Subsequently, a two-stage method is designed with an enhanced column and constraint generation algorithm (C<span><math><mo>&</mo></math></span>CG) using optimal cut, and reverse Nash equilibrium-based constraints are proposed for the worst financial condition. The effectiveness of the algorithm and model is verified through a series of numerical benchmarks and sensitivity analysis for Nash equilibrium-based constraints, sharing restrictions, uncertainty of demand and financial budget. The results show that the proposed method is efficient and flexible when incorporating capacity sharing and highlighting the influence of the Nash equilibrium-based constraints. Finally, it presents that the Nash equilibrium-based constraints are more suitable for logistics networks through sharing alliances.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104699"},"PeriodicalIF":8.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033363","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}
Energy Performance Contracting (EPC) is a market-driven collaboration mechanism between enterprises and energy service companies, offering substantial potential to improve energy efficiency and reduce carbon emissions. However, integrating EPC into large-scale sustainable supply chain networks presents complex multi-criteria decision-making challenges, particularly in balancing economic performance, environmental sustainability, and social responsibility under dynamic operational conditions. To address these challenges, this study proposes a holistic EPC-integrated mathematical model and a hybrid solution framework based on a Dual Attention Graph Neural Network (DAGNN). The model extends the traditional triple-bottom-line framework by incorporating two additional dimensions—operational efficiency and product quality, and explicitly captures temporal dynamics, such as seasonal fluctuations in cost, profit, and demand, to more accurately assess EPC’s impact on supply chain sustainability. The dual attention architecture adopts two separate neural networks that learn context-aware importance of strategic and operational attributes by leveraging historical expert decisions. These learned weights enable adaptive prioritization of participant attributes under varying supply chain contexts and enhance the efficiency and interpretability of decision logic. The mathematical model is then transformed into an attention-enhanced bipartite graph representation and solved through a graph neural network, enabling efficient and accurate decision-making in large-scale settings. Experimental results on multi-period, multi-product instances demonstrate that the proposed approach achieves 92.88% solution accuracy relative to commercial solvers while reducing computational time by 99.96%. These results highlight the framework’s potential to provide real-time, scalable, and transparent decision support for EPC-integrated sustainable supply chains, thereby advancing the alignment of energy efficiency initiatives with holistic supply chain performance optimization.
{"title":"A dual attention graph neural network framework for sustainable supply chain optimization under energy performance contracting","authors":"Yuhan Guo , Runsheng Chen , Hamid Allaoui , Alok Choudhary , Wenhua Li","doi":"10.1016/j.tre.2026.104702","DOIUrl":"10.1016/j.tre.2026.104702","url":null,"abstract":"<div><div>Energy Performance Contracting (EPC) is a market-driven collaboration mechanism between enterprises and energy service companies, offering substantial potential to improve energy efficiency and reduce carbon emissions. However, integrating EPC into large-scale sustainable supply chain networks presents complex multi-criteria decision-making challenges, particularly in balancing economic performance, environmental sustainability, and social responsibility under dynamic operational conditions. To address these challenges, this study proposes a holistic EPC-integrated mathematical model and a hybrid solution framework based on a Dual Attention Graph Neural Network (DAGNN). The model extends the traditional triple-bottom-line framework by incorporating two additional dimensions—operational efficiency and product quality, and explicitly captures temporal dynamics, such as seasonal fluctuations in cost, profit, and demand, to more accurately assess EPC’s impact on supply chain sustainability. The dual attention architecture adopts two separate neural networks that learn context-aware importance of strategic and operational attributes by leveraging historical expert decisions. These learned weights enable adaptive prioritization of participant attributes under varying supply chain contexts and enhance the efficiency and interpretability of decision logic. The mathematical model is then transformed into an attention-enhanced bipartite graph representation and solved through a graph neural network, enabling efficient and accurate decision-making in large-scale settings. Experimental results on multi-period, multi-product instances demonstrate that the proposed approach achieves 92.88% solution accuracy relative to commercial solvers while reducing computational time by 99.96%. These results highlight the framework’s potential to provide real-time, scalable, and transparent decision support for EPC-integrated sustainable supply chains, thereby advancing the alignment of energy efficiency initiatives with holistic supply chain performance optimization.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104702"},"PeriodicalIF":8.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033362","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 : 2026-01-22DOI: 10.1016/j.tre.2026.104684
Rashika Gupta , Devika Koonthalakadu Baby , Debjit Roy , Shankar C. Subramanian , Sandip Chakrabarti
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.
{"title":"Stochastic modeling and design of truck platooning strategies considering platoon dynamics","authors":"Rashika Gupta , Devika Koonthalakadu Baby , Debjit Roy , Shankar C. Subramanian , Sandip Chakrabarti","doi":"10.1016/j.tre.2026.104684","DOIUrl":"10.1016/j.tre.2026.104684","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104684"},"PeriodicalIF":8.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033369","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 : 2026-01-22DOI: 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":"2026-01-22","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}
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":"2026-01-21","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 : 2026-01-21DOI: 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":"2026-01-21","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 : 2026-01-21DOI: 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.
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