Pub Date : 2025-09-01Epub Date: 2025-07-29DOI: 10.1016/j.trb.2025.103284
Qingying He , Wei Liu , Tian-Liang Liu , Qiong Tian
This study examines the routing and scheduling of an integrated system of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) for maritime surveillance. The uncertainties in air and maritime conditions can cause delays in the movements of UAVs and USVs. We introduce a robust coordinated path planning approach for the UAV-USV system, optimizing operational efficiency while accounting for UAV/USV travel time unreliability. Specifically, we propose a novel robust compact formulation for the coordinated path planning problem using the budgeted uncertainty sets. To solve this complex problem, we decompose it into a master problem, i.e., a set partitioning problem, and a subproblem that deals with the robust resource-constrained elementary shortest paths. Furthermore, we propose a customized branch-and-price-and-cut solution algorithm to efficiently solve the robust path planning problem. Numerical studies illustrate that our approach can produce solutions that are significantly more robust than those that ignore uncertainty.
{"title":"Robust coordinated path planning for unmanned aerial vehicles and unmanned surface vehicles in maritime monitoring with travel time uncertainty","authors":"Qingying He , Wei Liu , Tian-Liang Liu , Qiong Tian","doi":"10.1016/j.trb.2025.103284","DOIUrl":"10.1016/j.trb.2025.103284","url":null,"abstract":"<div><div>This study examines the routing and scheduling of an integrated system of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) for maritime surveillance. The uncertainties in air and maritime conditions can cause delays in the movements of UAVs and USVs. We introduce a robust coordinated path planning approach for the UAV-USV system, optimizing operational efficiency while accounting for UAV/USV travel time unreliability. Specifically, we propose a novel robust compact formulation for the coordinated path planning problem using the budgeted uncertainty sets. To solve this complex problem, we decompose it into a master problem, i.e., a set partitioning problem, and a subproblem that deals with the robust resource-constrained elementary shortest paths. Furthermore, we propose a customized branch-and-price-and-cut solution algorithm to efficiently solve the robust path planning problem. Numerical studies illustrate that our approach can produce solutions that are significantly more robust than those that ignore uncertainty.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"199 ","pages":"Article 103284"},"PeriodicalIF":6.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722124","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 : 2025-09-01Epub Date: 2025-06-24DOI: 10.1016/j.trb.2025.103266
Jing-Peng Wang , Hai Wang , Peng Liu , Hai-Jun Huang
In a ride-sourcing system, dispatching order requests to available drivers entails a comprehensive consideration of factors such as pickup proximity, order rewards, driver rating, safety behavior, passenger preferences, real-time road conditions, and other relevant variables. Inefficient dispatch processes often result in service cancellation by either the customer or the driver. This paper represents a pioneering effort to examine order dispatching strategy and pricing scheme while taking service cancellation behaviors into account. By assuming the platform has limited knowledge of the valuation of service of each customer and the reservation earning rate of each driver, we develop a two-period model that captures the dynamic decision-making processes of multiple stakeholders (customers, drivers, and platform) and formulate the platform’s order-dispatching problem as a stochastic programming model. Within a greedy approximation framework, our analysis reveals the significant implications of pricing scheme for critical performance metrics while considering service cancellation. These include the matching probability (probability of customer-driver acceptance for platform’s match results), the platform’s rewards, and the effects on the platform’s order-dispatching decisions. Specifically, within the realm of linear pricing, the matching probability demonstrates a positive correlation with trip distance, and thereby establishes a consistent dispatching order compared with one that does not consider service cancellation. Conversely, with nonlinear pricing (whether sublinear or superlinear), extended trip distance is generally associated with a reduced matching probability when it exceeds a threshold; this results in prioritizing orders with intermediate trip distances in order-dispatching decisions. Moreover, numerical experiments support that an integration of sublinear, superlinear, and linear pricing is conducive to optimizing rewards across short-, intermediate, and long-distance trips. Finally, scenarios of unimodal distributions of customer’s valuation of service and driver’s reservation earning rate consistently yield the highest rewards, through sublinear, linear, and superlinear pricing schemes.
{"title":"Order dispatching strategy and pricing scheme in ride-sourcing markets with consideration of service cancellation","authors":"Jing-Peng Wang , Hai Wang , Peng Liu , Hai-Jun Huang","doi":"10.1016/j.trb.2025.103266","DOIUrl":"10.1016/j.trb.2025.103266","url":null,"abstract":"<div><div>In a ride-sourcing system, dispatching order requests to available drivers entails a comprehensive consideration of factors such as pickup proximity, order rewards, driver rating, safety behavior, passenger preferences, real-time road conditions, and other relevant variables. Inefficient dispatch processes often result in service cancellation by either the customer or the driver. This paper represents a pioneering effort to examine order dispatching strategy and pricing scheme while taking service cancellation behaviors into account. By assuming the platform has limited knowledge of the valuation of service of each customer and the reservation earning rate of each driver, we develop a two-period model that captures the dynamic decision-making processes of multiple stakeholders (customers, drivers, and platform) and formulate the platform’s order-dispatching problem as a stochastic programming model. Within a greedy approximation framework, our analysis reveals the significant implications of pricing scheme for critical performance metrics while considering service cancellation. These include the matching probability (probability of customer-driver acceptance for platform’s match results), the platform’s rewards, and the effects on the platform’s order-dispatching decisions. Specifically, within the realm of linear pricing, the matching probability demonstrates a positive correlation with trip distance, and thereby establishes a consistent dispatching order compared with one that does not consider service cancellation. Conversely, with nonlinear pricing (whether sublinear or superlinear), extended trip distance is generally associated with a reduced matching probability when it exceeds a threshold; this results in prioritizing orders with intermediate trip distances in order-dispatching decisions. Moreover, numerical experiments support that an integration of sublinear, superlinear, and linear pricing is conducive to optimizing rewards across short-, intermediate, and long-distance trips. Finally, scenarios of unimodal distributions of customer’s valuation of service and driver’s reservation earning rate consistently yield the highest rewards, through sublinear, linear, and superlinear pricing schemes.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"199 ","pages":"Article 103266"},"PeriodicalIF":5.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365327","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 : 2025-09-01Epub Date: 2025-07-08DOI: 10.1016/j.trb.2025.103264
Gabriel Nova , C. Angelo Guevara , Stephane Hess , Thomas O. Hancock
Discrete choice analysis aims to understand and predict decision-makers’ behaviour, a goal that is crucial across several disciplines, including transportation. This type of analysis has relied predominantly on static representations of preferences, principally through the Random Utility Maximisation (RUM) model, due to its ease of implementation, economic interpretability, and statistical formality. However, this model assumes that individuals possess complete information about all attributes of alternatives and that they can process and recall this information instantaneously, which may not align with actual human behaviour. In contrast, the Decision Field Theory (DFT) model from mathematical psychology explicitly incorporates the repeated scrutiny of attributes and recall effects within the decision-making process, which enables it to model attention weights, but lacks microeconomic interpretability and clear statistical parameter identification. This paper introduces the RUM-DFT model, which seeks to integrate strengths of both approaches. Through Monte Carlo simulations, the proposed model is shown to be able to: (i) recover parameters related to the deliberation process, (ii) replicate the dynamic behaviour of utilities during deliberation as observed in practice, (iii) maintain economic interpretability by estimating coefficients that can be used to calculate the marginal indirect utilities, and (iv) highlight the pitfalls of using a RUM model that disregards the true dynamics of data generation process. The SwissMetro case study is employed also to evaluate the RUM-DFT model using a real-world dataset, demonstrating the viability and superior goodness-of-fit of the proposed model.
{"title":"A random utility maximisation model considering the information search process","authors":"Gabriel Nova , C. Angelo Guevara , Stephane Hess , Thomas O. Hancock","doi":"10.1016/j.trb.2025.103264","DOIUrl":"10.1016/j.trb.2025.103264","url":null,"abstract":"<div><div>Discrete choice analysis aims to understand and predict decision-makers’ behaviour, a goal that is crucial across several disciplines, including transportation. This type of analysis has relied predominantly on static representations of preferences, principally through the Random Utility Maximisation (RUM) model, due to its ease of implementation, economic interpretability, and statistical formality. However, this model assumes that individuals possess complete information about all attributes of alternatives and that they can process and recall this information instantaneously, which may not align with actual human behaviour. In contrast, the Decision Field Theory (DFT) model from mathematical psychology explicitly incorporates the repeated scrutiny of attributes and recall effects within the decision-making process, which enables it to model attention weights, but lacks microeconomic interpretability and clear statistical parameter identification. This paper introduces the RUM-DFT model, which seeks to integrate strengths of both approaches. Through Monte Carlo simulations, the proposed model is shown to be able to: (i) recover parameters related to the deliberation process, (ii) replicate the dynamic behaviour of utilities during deliberation as observed in practice, (iii) maintain economic interpretability by estimating coefficients that can be used to calculate the marginal indirect utilities, and (iv) highlight the pitfalls of using a RUM model that disregards the true dynamics of data generation process. The SwissMetro case study is employed also to evaluate the RUM-DFT model using a real-world dataset, demonstrating the viability and superior goodness-of-fit of the proposed model.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"199 ","pages":"Article 103264"},"PeriodicalIF":5.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580273","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 : 2025-09-01Epub Date: 2025-06-29DOI: 10.1016/j.trb.2025.103276
Yao Deng , Zhi-Chun Li , Sean Qian , Wei Ma
With the proliferation of ride-hailing services, curb space in urban areas has become highly congested due to the massive passenger pick-ups and drop-offs. Particularly during peak hours, the massive ride-hailing vehicles waiting to drop off obstruct curb spaces and even disrupt the flow of mainline traffic. However, there is a lack of an analytical model that formulates and mitigates the congestion effects of ride-hailing drop-offs in curb spaces. To address this issue, this paper proposes a novel bi-modal two-tandem bottleneck model to depict the commuting behaviors of private vehicles (PVs) and ride-hailing vehicles (RVs) during the morning peak in a linear city. In the model, the upstream bottleneck models the congestion on highways, and the downstream curbside bottlenecks depict the congestion caused by RV drop-offs in curb spaces, PV queue on main roads, and the spillover effects between them in the urban area. The proposed model can be solved in a closed form under eight different scenarios. A time-varying optimal congestion pricing scheme, combined curbside pricing and parking pricing, is proposed to achieve the social optimum. It is found that potential waste of road capacity could occur when there is a mismatch between the highway and curbside bottlenecks, and hence the optimal pricing should be determined in a coordinated manner. A real-world case from Hong Kong shows that the limited curb space and main road in the urban area could be the major congestion bottleneck. Expanding the capacity of the curb space or the main road in the urban area, rather than the highway bottleneck, can effectively reduce social costs. This paper highlights the critical role of curbside management and provides policy implications for the coordinated management of highways and curb spaces.
{"title":"Modeling the curbside congestion effects of ride-hailing services for morning commute using bi-modal two-tandem bottlenecks","authors":"Yao Deng , Zhi-Chun Li , Sean Qian , Wei Ma","doi":"10.1016/j.trb.2025.103276","DOIUrl":"10.1016/j.trb.2025.103276","url":null,"abstract":"<div><div>With the proliferation of ride-hailing services, curb space in urban areas has become highly congested due to the massive passenger pick-ups and drop-offs. Particularly during peak hours, the massive ride-hailing vehicles waiting to drop off obstruct curb spaces and even disrupt the flow of mainline traffic. However, there is a lack of an analytical model that formulates and mitigates the congestion effects of ride-hailing drop-offs in curb spaces. To address this issue, this paper proposes a novel bi-modal two-tandem bottleneck model to depict the commuting behaviors of private vehicles (PVs) and ride-hailing vehicles (RVs) during the morning peak in a linear city. In the model, the upstream bottleneck models the congestion on highways, and the downstream curbside bottlenecks depict the congestion caused by RV drop-offs in curb spaces, PV queue on main roads, and the spillover effects between them in the urban area. The proposed model can be solved in a closed form under eight different scenarios. A time-varying optimal congestion pricing scheme, combined curbside pricing and parking pricing, is proposed to achieve the social optimum. It is found that potential waste of road capacity could occur when there is a mismatch between the highway and curbside bottlenecks, and hence the optimal pricing should be determined in a coordinated manner. A real-world case from Hong Kong shows that the limited curb space and main road in the urban area could be the major congestion bottleneck. Expanding the capacity of the curb space or the main road in the urban area, rather than the highway bottleneck, can effectively reduce social costs. This paper highlights the critical role of curbside management and provides policy implications for the coordinated management of highways and curb spaces.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"199 ","pages":"Article 103276"},"PeriodicalIF":5.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517237","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 : 2025-09-01Epub Date: 2025-06-20DOI: 10.1016/j.trb.2025.103253
G.E. CANTARELLA , C. FIORI , P. VELONÀ
Deterministic process (DP) models for day-to-day dynamic assignment can be cast in the general two-equation assignment modelling approach, including the following:
- the arc cost updating recursive equation in the case of day-to-day dynamic assignment; instances are exponential smoothing (ES) or moving average (MA) filters;
- the arc flow updating recursive equation in the case of day-to-day dynamic assignment; instances are ES filters.
Even though ES filters for cost updating may well approximate MA filters, somebody in the scientific community argue against the underlying hypothesis of infinite memory for ES filters with respect to MA ones; numerical results support significant differences for small memory depths, say 2 or 3 days.
The main original contribution of this study is a formal fixed-point stability and bifurcation analysis of MA-ES DP models with memory depth 2, and a comparison with ES-ES DP. At this aim the Omega method 2.0, suitable for carrying out general fixed-point stability and bifurcation analysis has been developed and discussed. Extremely long proofs have not been included for brevity. This study focused on methodological aspects; thus, numerical examples were not included.
{"title":"Moving average vs. exponential smoothing cost-updating filters for day-to-day dynamic assignment: fixed-point stability and bifurcation theoretical analysis","authors":"G.E. CANTARELLA , C. FIORI , P. VELONÀ","doi":"10.1016/j.trb.2025.103253","DOIUrl":"10.1016/j.trb.2025.103253","url":null,"abstract":"<div><div>Deterministic process (DP) models for day-to-day dynamic assignment can be cast in the general two-equation assignment modelling approach, including the following:</div><div>- the arc cost updating recursive equation in the case of day-to-day dynamic assignment; instances are exponential smoothing (ES) or moving average (MA) filters;</div><div>- the arc flow updating recursive equation in the case of day-to-day dynamic assignment; instances are ES filters.</div><div>Even though ES filters for cost updating may well approximate MA filters, somebody in the scientific community argue against the underlying hypothesis of infinite memory for ES filters with respect to MA ones; numerical results support significant differences for small memory depths, say 2 or 3 days.</div><div>The main original contribution of this study is a formal fixed-point stability and bifurcation analysis of MA-ES DP models with memory depth 2, and a comparison with ES-ES DP. At this aim the Omega method 2.0, suitable for carrying out general fixed-point stability and bifurcation analysis has been developed and discussed. Extremely long proofs have not been included for brevity. This study focused on methodological aspects; thus, numerical examples were not included.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"199 ","pages":"Article 103253"},"PeriodicalIF":5.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322838","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 : 2025-09-01Epub Date: 2025-06-06DOI: 10.1016/j.trb.2025.103249
M. Arya Zamal , Albert H. Schrotenboer , Tom Van Woensel
The growth of e-commerce requires efficient integration of first-mile pickup, middle-mile consolidation, and last-mile delivery. These so-called integrated end-to-end logistics operations are particularly visible in metropolitan areas where fast delivery services are in high demand. Inspired by real-world practices at our industry partner, this paper introduces the Stochastic Dynamic Order-Assignment and Dispatching Problem (SDOA-DP). It concerns stochastic and dynamic pickup-and-delivery orders arising at an end-to-end logistics delivery platform, for which the company, as a decision maker, needs to determine in real-time how to assign orders to middle-mile linehaul schedules and when to dispatch first- and last-mile two-echelon vehicle routes. We model the SDOA-DP as a Markov Decision Process and propose a novel solution approach based on a parameterized Cost Function Approximation (CFA) for order assignment in the middle mile and a parameterized Adaptive Large Neighborhood Search (ALNS) for vehicle dispatch and two-echelon routing in the first and last-mile. The CFA balances the cost of using linehauls with the time slack available for first- and last-mile planning while ensuring time windows are met. The parameterization in the ALNS ensures that we balance routing cost and delivery speed by limiting the frequency and timing of dispatching vehicle routes. We learn the best value of the parameterization using Bayesian optimization. Computational experiments show that our approach yields a 22% on-average improvement compared to a baseline policy. If we learn a single best parameterization for various system settings, we observe almost as good cost savings, showing that our approach is robust and reliable for practitioners. Finally, we applied our method to a case study of our industry partner and showed that our method could potentially reduce daily costs by 30.5% across various operational contexts.
{"title":"End-to-end logistics in metropolitan areas: A stochastic dynamic order-assignment and dispatching problem","authors":"M. Arya Zamal , Albert H. Schrotenboer , Tom Van Woensel","doi":"10.1016/j.trb.2025.103249","DOIUrl":"10.1016/j.trb.2025.103249","url":null,"abstract":"<div><div>The growth of e-commerce requires efficient integration of first-mile pickup, middle-mile consolidation, and last-mile delivery. These so-called integrated end-to-end logistics operations are particularly visible in metropolitan areas where fast delivery services are in high demand. Inspired by real-world practices at our industry partner, this paper introduces the Stochastic Dynamic Order-Assignment and Dispatching Problem (SDOA-DP). It concerns stochastic and dynamic pickup-and-delivery orders arising at an end-to-end logistics delivery platform, for which the company, as a decision maker, needs to determine in real-time how to assign orders to middle-mile linehaul schedules and when to dispatch first- and last-mile two-echelon vehicle routes. We model the SDOA-DP as a Markov Decision Process and propose a novel solution approach based on a parameterized Cost Function Approximation (CFA) for order assignment in the middle mile and a parameterized Adaptive Large Neighborhood Search (ALNS) for vehicle dispatch and two-echelon routing in the first and last-mile. The CFA balances the cost of using linehauls with the time slack available for first- and last-mile planning while ensuring time windows are met. The parameterization in the ALNS ensures that we balance routing cost and delivery speed by limiting the frequency and timing of dispatching vehicle routes. We learn the best value of the parameterization using Bayesian optimization. Computational experiments show that our approach yields a 22% on-average improvement compared to a baseline policy. If we learn a single best parameterization for various system settings, we observe almost as good cost savings, showing that our approach is robust and reliable for practitioners. Finally, we applied our method to a case study of our industry partner and showed that our method could potentially reduce daily costs by 30.5% across various operational contexts.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"199 ","pages":"Article 103249"},"PeriodicalIF":5.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230486","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 : 2025-08-01Epub Date: 2025-06-04DOI: 10.1016/j.trb.2025.103250
Xuecheng Tian , Shuaian Wang , Yan Liu , Ying Yang
Fuel prices are a crucial and volatile component of operational costs in maritime transportation. This paper optimizes container ship bunkering decisions under the uncertainty of multi-port fuel prices, using data-driven optimization frameworks that integrate machine learning and mathematical programming models. We address two primary challenges: (i) incorporating spatiotemporal correlations between multi-port fuel prices into predictive models, and (ii) determining the most effective data-driven modeling framework for this problem. To address the first challenge, we develop a two-channel long short-term memory model specifically designed to capture the spatiotemporal dependencies of multi-port fuel prices. For the second challenge, we construct two data-driven modeling frameworks for ship bunkering management: a two-stage contextual deterministic programming model with point predictions (TDP framework) and a multistage contextual stochastic programming model with distributional estimates (MSD framework). Through comprehensive computational experiments using both real-world and synthetic data, we obtain two crucial insights: (i) accounting for the spatiotemporal correlations among multi-port fuel prices significantly improves the accuracy of fuel price predictions; and (ii) the TDP framework is more suited to container shipping routes with fewer ports, while the MSD framework offers advantages in contexts with a higher number of ports.
{"title":"Data-driven optimization for container ship bunkering management under fuel price uncertainty","authors":"Xuecheng Tian , Shuaian Wang , Yan Liu , Ying Yang","doi":"10.1016/j.trb.2025.103250","DOIUrl":"10.1016/j.trb.2025.103250","url":null,"abstract":"<div><div>Fuel prices are a crucial and volatile component of operational costs in maritime transportation. This paper optimizes container ship bunkering decisions under the uncertainty of multi-port fuel prices, using data-driven optimization frameworks that integrate machine learning and mathematical programming models. We address two primary challenges: (i) incorporating spatiotemporal correlations between multi-port fuel prices into predictive models, and (ii) determining the most effective data-driven modeling framework for this problem. To address the first challenge, we develop a two-channel long short-term memory model specifically designed to capture the spatiotemporal dependencies of multi-port fuel prices. For the second challenge, we construct two data-driven modeling frameworks for ship bunkering management: a two-stage contextual deterministic programming model with point predictions (TDP framework) and a multistage contextual stochastic programming model with distributional estimates (MSD framework). Through comprehensive computational experiments using both real-world and synthetic data, we obtain two crucial insights: (i) accounting for the spatiotemporal correlations among multi-port fuel prices significantly improves the accuracy of fuel price predictions; and (ii) the TDP framework is more suited to container shipping routes with fewer ports, while the MSD framework offers advantages in contexts with a higher number of ports.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"198 ","pages":"Article 103250"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242214","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 : 2025-08-01Epub Date: 2025-05-25DOI: 10.1016/j.trb.2025.103234
Zuhayer Mahtab , Shichun Hu , Maged Dessouky , Fernando Ordoñez
In major metropolitan areas, ride-sharing systems can help reduce traffic congestion and increase the transportation system’s efficiency. In this paper, we propose a Branch-and-Price based approach for solving the ride-share routing problem with flexible pickup and drop-off points. We assume a ride-sharing system where drivers have their own origins and destinations, where all the drivers’ and passengers’ information is known beforehand, and all the problem data information is static and deterministic. We assume that drivers can pick up or drop off passengers from or to flexible meeting points that are within a passenger’s walking time limit from their origin or destination and are determined on a continuous plane. We formulate a mixed integer nonlinear model for routing and selecting pickup and drop-off points. Our solution approach decomposes this problem in two: selecting pickup and drop-off points and a rideshare routing problem. We develop an efficient algorithm to select the best pickup and drop-off points and show computationally that it is more efficient at finding pickup and drop-off points than considering a fixed set of discrete meeting points. To evaluate the performance of our approach, we perform numerical experiments on a San Francisco Taxicab dataset. Results show that our approach is efficient, solving instances with up to 600 points within 31 CPU minutes. For these datasets, incorporating flexible pickup and drop-off points can reduce the total vehicle travel time of the rideshare system by 4% on average.
{"title":"The ridesharing routing problem with flexible pickup and drop-off points","authors":"Zuhayer Mahtab , Shichun Hu , Maged Dessouky , Fernando Ordoñez","doi":"10.1016/j.trb.2025.103234","DOIUrl":"10.1016/j.trb.2025.103234","url":null,"abstract":"<div><div>In major metropolitan areas, ride-sharing systems can help reduce traffic congestion and increase the transportation system’s efficiency. In this paper, we propose a Branch-and-Price based approach for solving the ride-share routing problem with flexible pickup and drop-off points. We assume a ride-sharing system where drivers have their own origins and destinations, where all the drivers’ and passengers’ information is known beforehand, and all the problem data information is static and deterministic. We assume that drivers can pick up or drop off passengers from or to flexible meeting points that are within a passenger’s walking time limit from their origin or destination and are determined on a continuous plane. We formulate a mixed integer nonlinear model for routing and selecting pickup and drop-off points. Our solution approach decomposes this problem in two: selecting pickup and drop-off points and a rideshare routing problem. We develop an efficient algorithm to select the best pickup and drop-off points and show computationally that it is more efficient at finding pickup and drop-off points than considering a fixed set of discrete meeting points. To evaluate the performance of our approach, we perform numerical experiments on a San Francisco Taxicab dataset. Results show that our approach is efficient, solving instances with up to 600 points within 31 CPU minutes. For these datasets, incorporating flexible pickup and drop-off points can reduce the total vehicle travel time of the rideshare system by 4% on average.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"198 ","pages":"Article 103234"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131233","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 : 2025-08-01Epub Date: 2025-05-24DOI: 10.1016/j.trb.2025.103247
Yidan Shangguan, Xuecheng Tian, King-Wah Pang, Shuaian Wang
Liquefied natural gas (LNG) is increasingly viewed as a promising fuel for dual-fuel ships due to its cost-effectiveness, low emissions, and alignment with regulatory requirements. However, the high methane content of LNG, ranging from 85% to 95%, presents a significant challenge because of the phenomenon of methane slip whereby unburned methane escapes from the engine’s combustion chamber and other parts of the storage and transportation systems. Methane slip, which peaks at low ship speeds and decreases at higher speeds, can lead to substantial environmental pollution if it is not properly managed. This study rigorously examines the impact of sailing speed on methane slip rates and recognizes the complexities of fuel usage in dual-fuel ships. We develop a nonlinear mixed-integer programming model designed for container shipping companies that aims to optimize fleet composition, sailing speed, and fuel usage strategies. The objective of the model is to minimize total operational costs, including fuel expenses and taxes related to carbon emissions and methane slip. To address the computational challenges posed by the model’s nonlinearity, we propose a tailored solution method that uses sailing time as a proxy for speed, discretizing these times for effective implementation. The validity of this method is supported by theoretical guarantees and demonstrated through numerical experiments. Our computational results indicate that accounting for methane slip in the operational management of dual-fuel ships can help mitigate financial losses under certain conditions.
{"title":"Optimizing dual-fuel ship operations considering methane slip","authors":"Yidan Shangguan, Xuecheng Tian, King-Wah Pang, Shuaian Wang","doi":"10.1016/j.trb.2025.103247","DOIUrl":"10.1016/j.trb.2025.103247","url":null,"abstract":"<div><div>Liquefied natural gas (LNG) is increasingly viewed as a promising fuel for dual-fuel ships due to its cost-effectiveness, low emissions, and alignment with regulatory requirements. However, the high methane content of LNG, ranging from 85% to 95%, presents a significant challenge because of the phenomenon of methane slip whereby unburned methane escapes from the engine’s combustion chamber and other parts of the storage and transportation systems. Methane slip, which peaks at low ship speeds and decreases at higher speeds, can lead to substantial environmental pollution if it is not properly managed. This study rigorously examines the impact of sailing speed on methane slip rates and recognizes the complexities of fuel usage in dual-fuel ships. We develop a nonlinear mixed-integer programming model designed for container shipping companies that aims to optimize fleet composition, sailing speed, and fuel usage strategies. The objective of the model is to minimize total operational costs, including fuel expenses and taxes related to carbon emissions and methane slip. To address the computational challenges posed by the model’s nonlinearity, we propose a tailored solution method that uses sailing time as a proxy for speed, discretizing these times for effective implementation. The validity of this method is supported by theoretical guarantees and demonstrated through numerical experiments. Our computational results indicate that accounting for methane slip in the operational management of dual-fuel ships can help mitigate financial losses under certain conditions.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"198 ","pages":"Article 103247"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131232","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 : 2025-07-01Epub Date: 2025-05-10DOI: 10.1016/j.trb.2025.103236
Tao Zhang , Shuaian Wang , Xu Xin
In this paper, we address the classical liner fleet deployment and slot allocation joint optimization problem in the maritime field with uncertain container transportation demand. We relax the assumption in existing studies that the demand distribution function is known because container transportation demand is deeply affected by the world’s economic and political landscape. With the help of advances in distributionally robust optimization theory, we develop a two-stage data-driven robust chance-constrained model. This distribution-free model requires only limited historical demand data as input and jointly optimizes the class (i.e., capacity) and number of liners assigned on each route and the scheme for allocating containers on each leg to maximize the profit (container transportation revenue minus fleet operating costs, voyage costs, and capital costs) of the liner company. The joint chance constraint in the model requires that the transportation demand of the contract shipper be satisfied with a pre-determined probability. We then reformulate the model as a second-order cone programming and design a customized algorithm to explore the global optimal solution based on the outer approximation algorithm framework. This paper can serve as a baseline distribution-free model for solving liner fleet deployment and slot allocation joint optimization problems.
{"title":"Liner fleet deployment and slot allocation problem: A distributionally robust optimization model with joint chance constraints","authors":"Tao Zhang , Shuaian Wang , Xu Xin","doi":"10.1016/j.trb.2025.103236","DOIUrl":"10.1016/j.trb.2025.103236","url":null,"abstract":"<div><div>In this paper, we address the classical liner fleet deployment and slot allocation joint optimization problem in the maritime field with uncertain container transportation demand. We relax the assumption in existing studies that the demand distribution function is known because container transportation demand is deeply affected by the world’s economic and political landscape. With the help of advances in distributionally robust optimization theory, we develop a two-stage data-driven robust chance-constrained model. This distribution-free model requires only limited historical demand data as input and jointly optimizes the class (i.e., capacity) and number of liners assigned on each route and the scheme for allocating containers on each leg to maximize the profit (container transportation revenue minus fleet operating costs, voyage costs, and capital costs) of the liner company. The joint chance constraint in the model requires that the transportation demand of the contract shipper be satisfied with a pre-determined probability. We then reformulate the model as a second-order cone programming and design a customized algorithm to explore the global optimal solution based on the outer approximation algorithm framework. This paper can serve as a baseline distribution-free model for solving liner fleet deployment and slot allocation joint optimization problems.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"197 ","pages":"Article 103236"},"PeriodicalIF":5.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928914","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号