XZ
Xiaoning Zhu
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5 records found
1
Journal article
(2026)
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Siqiao Li, Xiaoning Zhu, Pan Shang, Tom Van Woensel, Yu Yao
This study investigates a shared-use metro network in which passengers and freight are transported on different trains, enhancing resource utilization and promoting the sustainable growth of urban areas. From the perspective of public authorities seeking to balance the interests of all stakeholders, we address the tactical problem of determining the optimal number, stop pattern, and schedule of freight train services needed for freight delivery, while maintaining high passenger service quality by minor timetable adjustments. A non-convex mixed-integer quadratic programming model is developed to minimize freight transportation, train operational costs, and deviations in passenger train schedules. A Frank-Wolfe-based heuristic is developed with a novel gradient approximation that accounts for the hierarchy between binary and continuous decisions. The step size is defined as a discrete gap between disjoint subspaces determined by train operations, reflecting that partially shifting flow does not effectively impact the objective due to the indivisibility of train activation. Experiments are conducted based on instances generated from the Beijing Metro network. The results show that under high freight demand, dedicated freight trains cause less passenger delay than utilizing existing passenger trains. Prioritizing passenger schedules shifts freight to lower-priority lines, while incorporating a quadratic consolidation cost helps reduce fragmentation and excessive transshipments.
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This study investigates a shared-use metro network in which passengers and freight are transported on different trains, enhancing resource utilization and promoting the sustainable growth of urban areas. From the perspective of public authorities seeking to balance the interests of all stakeholders, we address the tactical problem of determining the optimal number, stop pattern, and schedule of freight train services needed for freight delivery, while maintaining high passenger service quality by minor timetable adjustments. A non-convex mixed-integer quadratic programming model is developed to minimize freight transportation, train operational costs, and deviations in passenger train schedules. A Frank-Wolfe-based heuristic is developed with a novel gradient approximation that accounts for the hierarchy between binary and continuous decisions. The step size is defined as a discrete gap between disjoint subspaces determined by train operations, reflecting that partially shifting flow does not effectively impact the objective due to the indivisibility of train activation. Experiments are conducted based on instances generated from the Beijing Metro network. The results show that under high freight demand, dedicated freight trains cause less passenger delay than utilizing existing passenger trains. Prioritizing passenger schedules shifts freight to lower-priority lines, while incorporating a quadratic consolidation cost helps reduce fragmentation and excessive transshipments.
Journal article
(2024)
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Siqiao Li, Xiaoning Zhu, Pan Shang, Li Wang, Tianqi Li
With the introduction of freight transportation into the passenger transit network, underground city logistics are regarded as a desirable alternative to address the challenges due to truck movements in urban freight transport. Furthermore, some metro lines suffer from low-capacity utilization because of the unbalanced demand, which provides the potential to expand the extra capacity for freight transportation. Therefore, this study integrates the train unit scheduling problem with combined transportation of passengers and freight during off-peak hours. The objective is to fully utilize the remaining capacity by not only integrating passenger and freight flows but also allowing a flexible composition such that the capacity can better meet the demand. The composition of a trip is defined as its state. A three-dimensional space–time–state network is constructed to capture the composition transitions and passenger/freight trajectories. The problem is formulated as an integer linear programming model to minimize the weighted sum of train unit operational, passenger travel, and freight travel costs. Utilizing the problem-specific characteristics, a constrained-gap-based branch-and-bound approach is developed to efficiently solve the model. The worst bound for each objective is guaranteed by introducing two gaps in the passenger- and freight-related objectives. The nodes with estimated lower bounds exceeding the designated gaps are pruned. A beam search procedure is also included to further reduce the computational complexity. The developed algorithm is tested on real-life instances from the Beijing Metro Network. The results provide insights into the benefits and applicable scenarios for integrating passenger and freight transportation. Moreover, we demonstrate that the number of train units should be carefully determined considering the tradeoff between passenger service quality and freight demand volume.
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With the introduction of freight transportation into the passenger transit network, underground city logistics are regarded as a desirable alternative to address the challenges due to truck movements in urban freight transport. Furthermore, some metro lines suffer from low-capacity utilization because of the unbalanced demand, which provides the potential to expand the extra capacity for freight transportation. Therefore, this study integrates the train unit scheduling problem with combined transportation of passengers and freight during off-peak hours. The objective is to fully utilize the remaining capacity by not only integrating passenger and freight flows but also allowing a flexible composition such that the capacity can better meet the demand. The composition of a trip is defined as its state. A three-dimensional space–time–state network is constructed to capture the composition transitions and passenger/freight trajectories. The problem is formulated as an integer linear programming model to minimize the weighted sum of train unit operational, passenger travel, and freight travel costs. Utilizing the problem-specific characteristics, a constrained-gap-based branch-and-bound approach is developed to efficiently solve the model. The worst bound for each objective is guaranteed by introducing two gaps in the passenger- and freight-related objectives. The nodes with estimated lower bounds exceeding the designated gaps are pruned. A beam search procedure is also included to further reduce the computational complexity. The developed algorithm is tested on real-life instances from the Beijing Metro Network. The results provide insights into the benefits and applicable scenarios for integrating passenger and freight transportation. Moreover, we demonstrate that the number of train units should be carefully determined considering the tradeoff between passenger service quality and freight demand volume.
The considerable increase in parcel deliveries has negatively impacted the accessibility and livability of cities. One solution strategy is to decouple short-distance from long-distance shipping so that last-mile transport can be performed with low-footprint vehicles. Such solutions are referred to in the literature as multi-echelon distribution systems. This study introduced a new variant of the two-echelon vehicle routing problem that considers multiple alternative transport modes as well as multiple commodities over a multi-period time horizon, where customers can obtain their commodities from any store. We referred to this problem as the two-echelon multi-commodity multimodal vehicle routing problem (MCM-2E-VRP). The objective of service providers is to minimize total generalized costs while satisfying customer requirements. We formulated this as a mathematical model based on a space–time network and introduced a random utility discrete choice model to capture variations in performance and preferences. We developed an adaptive large-neighborhood search (ALNS) algorithm to provide solutions for newly generated MCM-2E-VRP instances based on the Beijing Yizhuang transportation network. Extensive numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. A sensitivity analysis revealed some policy-relevant findings regarding the effects of store distribution and vehicle capacity.
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The considerable increase in parcel deliveries has negatively impacted the accessibility and livability of cities. One solution strategy is to decouple short-distance from long-distance shipping so that last-mile transport can be performed with low-footprint vehicles. Such solutions are referred to in the literature as multi-echelon distribution systems. This study introduced a new variant of the two-echelon vehicle routing problem that considers multiple alternative transport modes as well as multiple commodities over a multi-period time horizon, where customers can obtain their commodities from any store. We referred to this problem as the two-echelon multi-commodity multimodal vehicle routing problem (MCM-2E-VRP). The objective of service providers is to minimize total generalized costs while satisfying customer requirements. We formulated this as a mathematical model based on a space–time network and introduced a random utility discrete choice model to capture variations in performance and preferences. We developed an adaptive large-neighborhood search (ALNS) algorithm to provide solutions for newly generated MCM-2E-VRP instances based on the Beijing Yizhuang transportation network. Extensive numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. A sensitivity analysis revealed some policy-relevant findings regarding the effects of store distribution and vehicle capacity.
Journal article
(2024)
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Shuai Wang, Xiaoning Zhu, Pan Shang, Wenqian Liu, Xiao Lin, Lóránt Tavasszy
The rapid increase in e-commerce and the emergence of combined passenger/freight systems in urban areas have raised the question of how best to integrate public transport services into door-to-door deliveries. This paper develops a variant of the pickup and delivery problem, called the two-echelon pickup and delivery problem using public transport (2E-PDP-PT). In the 2E-PDP-PT, the transportation network is split into two echelons. Different vehicles are utilized across the first and second echelons to ensure distribution efficiency. Parcels are delivered by public transport with free capacity or via trucks between satellites in the first echelon, and logistics vehicles are operated in the second echelon. The satellites are located at the echelon borders to transfer commodities between echelons. The 2E-PDP-PT aims to minimize total delivery costs and improve public transport capacity utilization. We formulate a new mathematical model based on a space-time network and adopt an adaptive large neighborhood search (ALNS) algorithm for the 2E-PDP-PT. The effectiveness of the ALNS algorithm is validated using newly generated small-scale instances. Furthermore, we investigate large-scale instances based on the Beijing Yizhuang transportation network. The computations show that an average total delivery cost savings of 4.5% is feasible. In addition, we analyze the impact of demand distributions and compare the ALNS algorithm and the LNS algorithm. Finally, we conclude that dynamically integrating public transport into freight transport services can benefit both logistics companies and public transport operators.
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The rapid increase in e-commerce and the emergence of combined passenger/freight systems in urban areas have raised the question of how best to integrate public transport services into door-to-door deliveries. This paper develops a variant of the pickup and delivery problem, called the two-echelon pickup and delivery problem using public transport (2E-PDP-PT). In the 2E-PDP-PT, the transportation network is split into two echelons. Different vehicles are utilized across the first and second echelons to ensure distribution efficiency. Parcels are delivered by public transport with free capacity or via trucks between satellites in the first echelon, and logistics vehicles are operated in the second echelon. The satellites are located at the echelon borders to transfer commodities between echelons. The 2E-PDP-PT aims to minimize total delivery costs and improve public transport capacity utilization. We formulate a new mathematical model based on a space-time network and adopt an adaptive large neighborhood search (ALNS) algorithm for the 2E-PDP-PT. The effectiveness of the ALNS algorithm is validated using newly generated small-scale instances. Furthermore, we investigate large-scale instances based on the Beijing Yizhuang transportation network. The computations show that an average total delivery cost savings of 4.5% is feasible. In addition, we analyze the impact of demand distributions and compare the ALNS algorithm and the LNS algorithm. Finally, we conclude that dynamically integrating public transport into freight transport services can benefit both logistics companies and public transport operators.
Journal article
(2023)
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Siqiao Li, Xiaoning Zhu, Pan Shang, Tianqi Li, Wenqian Liu
This study investigates the potential of a shared freight and passenger high-speed railway system in which different transportation resources are allowed to be shared under different sharing modes. A sharing-carriage mode is proposed and combined with a sharing-train mode to fully exploit the remaining capacity of the existing railway schedule. The manner in which these two sharing modes are jointly involved to utilize the available capacity is investigated and optimized. First, a space–time network is constructed to analyze the distribution of flow for a given train schedule. Subsequently, the influence of integrated transportation upon passenger satisfaction is included by introducing a load-factor-based penalty cost for each train. The model is first formulated as a mixed-integer program that minimizes service and routing costs and then reformulated into a path-based model. A Benders decomposition approach is proposed to decompose the problem into two subproblems. Instead of exploring every possible path for each commodity when solving the Benders subproblem, a column-pool-based approximation approach is proposed to generate feasible solutions. Finally, the proposed approach is tested on two small-scale examples and 12 scenarios from a real-world high-speed railway network. Different train load factors, penalty costs, sharing modes, and commodity volumes are investigated to demonstrate the applicability of integrated transportation. The performance of the algorithm and acceleration techniques is also analyzed.
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This study investigates the potential of a shared freight and passenger high-speed railway system in which different transportation resources are allowed to be shared under different sharing modes. A sharing-carriage mode is proposed and combined with a sharing-train mode to fully exploit the remaining capacity of the existing railway schedule. The manner in which these two sharing modes are jointly involved to utilize the available capacity is investigated and optimized. First, a space–time network is constructed to analyze the distribution of flow for a given train schedule. Subsequently, the influence of integrated transportation upon passenger satisfaction is included by introducing a load-factor-based penalty cost for each train. The model is first formulated as a mixed-integer program that minimizes service and routing costs and then reformulated into a path-based model. A Benders decomposition approach is proposed to decompose the problem into two subproblems. Instead of exploring every possible path for each commodity when solving the Benders subproblem, a column-pool-based approximation approach is proposed to generate feasible solutions. Finally, the proposed approach is tested on two small-scale examples and 12 scenarios from a real-world high-speed railway network. Different train load factors, penalty costs, sharing modes, and commodity volumes are investigated to demonstrate the applicability of integrated transportation. The performance of the algorithm and acceleration techniques is also analyzed.