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Abstract

As a critical feature of synchromodal transport (ST), service flexibility plays an important role in improving the utilization of resources to reduce costs, emissions, congestions, and delays. However, none of the existing studies considered flexible services under the framework of synchromodality. This paper develops a Mixed Integer Linear Programming (MILP) model to formulate service flexibility in ST planning. In the MILP model, vehicles with flexible services as well as fixed services are both considered, and vehicle routes and request routes are planned simultaneously. Due to the computational complexity, an Adaptive Large Neighborhood Search heuristic is designed to solve the problem. Several customized operators are designed based on the characteristics of the studied problem. The proposed model is compared with the models developed in a highly-cited paper and a newly published paper that do not consider service flexibility. Case studies on small instances verified that the proposed model with flexibility performs better on all scenarios, including scenarios with different weights for the individual objectives, scenarios under congestion,and dynamic optimization scenarios. On large instances (up to 1600 shipment requests), the proposed model with flexibility reduces the cost by 14% on average compared with the existing models in the literature.

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... At the same time, greenhouse emissions from transportation reached 729 million tons in Europe while road transport occupied 72% thereof (EEA, 2022). Compared with trucks, barges and trains generate lower costs and emissions but have longer travel times and less flexibility (Zhang et al., 2022). As an alternative, intermodal transportation has been proposed to provide efficient and sustainable services by using different transport modes and combining them in a multimodal network (Demir et al., 2016). ...
... Integrating container and vehicle routing decisions is expected to improve the efficiency of transport plans and the utilization rate of the fleet significantly. However, only a few studies considered the problem of routing containers and vehicles integrally in a multimodal network (e.g., Larsen et al., 2021a;Müller et al., 2021;Zhang et al., 2022), and all these studies consider a centralized decision-maker. In practice, most container transport systems are controlled by multiple stakeholders who prefer to share limited information with each other and keep their own decision authority (Lee and Song, 2017). ...
... These existing models can be further divided into two groups: container routing and shipment matching. While container routing models focus on the flow of containers on multimodal networks (e.g., Li et al. (2015), Qu et al. (2019), Yee et al. (2021), Larsen et al. (2021a), Rivera and Mes (2022) and Akyüz et al. (2023)), shipment matching models design binary variables to assign a shipment request (i.e, a bundle of containers with the same attributes) with specific time windows to multimodal services with specific time schedules (e.g., Demir et al. (2016), Guo et al. (2020), Müller et al. (2021) and Zhang et al. (2022)). Besides, most of the above studies consider vehicles having flexible departure times (e.g., Qu et al. (2019) and Guo et al. (2020)), and only a few consider the routing of containers and vehicles integrally in a multimodal network. ...
Article
This paper considers a decentralized container transport system in which two decision-makers are involved in getting a container from its origin to its destination: a logistics service provider (LSP) and a flexible service operator (FSO). While the LSP receives shipment requests from shippers and controls the movement of containers over a multimodal network by booking scheduled (e.g., barges and trains) and flexible services (e.g., trucks) from service operators, the FSO manages a fleet of vehicles (e.g., trucks) that have flexible routes and departure times to fulfill the transport requests proposed by the LSP. In the literature, most of the studies focus on either container routing, by assuming all services have fixed routes and trucks are unlimited, or vehicle routing in a road network. This paper investigates the integrated problems of routing containers and vehicles through a multimodal network from a decentralized perspective considering the decision authorities of the LSP and the FSO. A synchromodal framework is designed to control the decision process which enables to utilize the benefits of real-time mode and route changes. To investigate the impact of communication, we develop a co-planning method under the synchromodal framework to coordinate the transport plans between the LSP and the FSO in real-time. The co-planning method considers a realistic level of information exchange and adheres to no changes in their responsibilities and authorities compared to current practice. The performance of the co-planning method is evaluated under various scenarios. The experimental results show that co-planning, using expected transport request fulfillment as feedback, reduces the total costs of container transportation and decreases the distance traveled by flexible vehicles under most of the scenarios.
... Furthermore, the RRIRP should be studied in a real-world transportation scenario to ensure that the coordination of road and rail can be realized by RRIRP in the actual transportation. In real-world transportation, the operations of rail services (specifically, in this study, container block trains) follow fixed timetables [8], while road services (specifically, in this study, container trucks) are flexible [9]. Coordinating road and rail by RRIRP to enhance intermodality and accomplish transportation orders should fully model the above differences. ...
... Currently, time-dependent intermodal routing studies primarily focus on time-dependent travel times. Zhang et al. [9] modeled the time dependency of truck travel times as a piecewise linear function and investigated a carbon-efficient intermodal routing problem with soft pickup and delivery time windows and road service flexibility. Sun et al. [32] and Guo et al. [33] considered the same modeling of time dependency as Zhang et al. [9] and presented an intermodal routing problem in a dynamic environment and a multimodal routing problem with traffic congestion and capacity uncertainty, respectively. ...
... Zhang et al. [9] modeled the time dependency of truck travel times as a piecewise linear function and investigated a carbon-efficient intermodal routing problem with soft pickup and delivery time windows and road service flexibility. Sun et al. [32] and Guo et al. [33] considered the same modeling of time dependency as Zhang et al. [9] and presented an intermodal routing problem in a dynamic environment and a multimodal routing problem with traffic congestion and capacity uncertainty, respectively. In these studies, the formulation of time-dependent travel times contributes to optimizing the efficiency of intermodal transfer and timeliness regarding time windows or due dates. ...
Article
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This study explores a road–rail intermodal routing problem. To improve the carbon efficiency of transportation, reducing CO2 emissions is considered by the routing. Soft time windows are incorporated into the routing to optimize the timeliness of the first-mile pickup and last-mile delivery services in intermodal transportation. The routing is further modeled in a time-dependent and fuzzy environment where the average truck speeds of the road depend on the truck departure times and are simultaneously considered fuzzy along with rail capacities. The fuzzy truck speed leads to the fuzziness of three aspects, including speed-dependent CO2 emissions of the road, a timetable-constrained transfer process from road to rail, and delivery time window violation. This study formulates the routing problem under the above considerations and carbon tax regulation as a combination of transportation path planning problem and truck departure time and speed matching problem. A fuzzy nonlinear optimization model is then established for the proposed routing problem. Furthermore, chance-constrained programming with general fuzzy measure is used to conduct the defuzzification of the model to make the problem solvable, and linearization techniques are adopted to linearize the model to enhance the efficiency of problem-solving. Finally, this study presents an empirical case to demonstrate the effectiveness of the designed approach. This case study evaluates the performance of carbon tax regulation by comparing it with multi-objective optimization. It also focuses on sensitivity analysis to discuss the influence of the optimistic–pessimistic parameter and confidence level on the optimization results. Several managerial insights are revealed based on the case study.
... Furthermore, the transportation sector is a major contributor to carbon dioxide emissions that cause global warming and harm the sustainable development of human beings. Therefore, European Commission promises to reduce the carbon dioxide emissions in transportation by 60% by 2025, and China implemented the "Carbon Peak and Carbon Neutrality" policy in which freight transportation is treated as an important source of achieving the policy goals [16]. Road-rail intermodal transportation should consider the public goal of reducing carbon dioxide emissions in its routing to further empower the advantage it has of being more eco-friendly. ...
... Zhang et al. [33] and Rosyida et al. [43] formulated hard time windows to ensure that the accomplishment of the delivery services should be within the delivery time windows. Zhang et al. [16], Sun [26] and Fazayeli et al. [44] proposed soft time windows for intermodal routing in which the late and early deliveries resulted in penalties. Sun et al. [9] and Sun and Li [45] took advantage of fuzzy soft time windows to quantify the satisfaction degrees of the intermodal routing and indicated that the improvement on the satisfaction degrees worsened the economic objective of the routing. ...
... As for the road-rail intermodal hub-and-spoke transportation, its timeliness is influenced by both delivery services for receivers and pickup services for shippers [20]. Currently, there are few studies taking the two aspects of timeliness into account, e.g., Sun et al. [9] and Zhang et al. [16], while others are only concerned with deliveries. Furthermore, all these studies refer to a single pickup or delivery time window for each transportation order, while ignore customer flexibility enabled by multiple time windows for pickups and deliveries. ...
Article
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This study investigates a road-rail intermodal routing problem in a hub-and-spoke network. Carbon cap-and-trade policy is accommodated with the routing to reduce carbon dioxide emissions. Multiple time windows are employed to enhance customer flexibility and achieve on-time pickup and delivery services. Road service flexibility and resulting truck operations optimization are explored by combining truck departure time planning under traffic restrictions and speed optimization with the routing. To enhance the feasibility and optimality of the problem optimization, the routing problem is formulated in a fuzzy environment where capacity and carbon trading price rate are trapezoidal fuzzy parameters. Based on the customer-centric objective setting, a fuzzy nonlinear optimization model and its linear reformation are given to formulate the proposed routing problem that combines distribution route design, time window selection and truck operations optimization. A robust possibilistic programming approach is developed to optimize the routing problem by obtaining its robust solutions. A case study is presented to demonstrate the feasibility of the proposed approaches. The results show that the multiple time windows and truck operations optimization can lower the total costs, enhance the optimality robustness and reduce carbon dioxide emissions of the routing optimization. The sensitivity analysis finds that increasing the lower bound of the confidence level in the robust possibilistic programming model improve the robustness and environmental sustainability; however, worsen the economy of the routing optimization.
... For solving large instances of the problem in reasonable time, a heuristic or metaheuristic approach is required since the solution space will be extensive. [14] has implemented a ALNS based metaheuristic to solve large sized instances for synchromodal transportation. In our study, we propose a Genetic Algorithm (GA) to tackle the problem, which can be potentially extended to address multiobjective optimization. ...
... Constraints (12-13) enforce precedence when one service arrives before another departs. Constraints (14) and (15) ensure time window and terminal working hours adherence. Constraints (16) and (17) Since it is practically infeasible to solve large-sized instances in respectable timeframe using exact methods, there is a necessity to introduce metaheuristic approaches. ...
... Consequently, the modeling approach for the Synchromodal Transport Planning Problem with Flexible Services differs from the models applied in the studies of Guo et al. (2020) and Demir et al. (2016) in both synchromodal and freight transport. Zhang et al. (2022) provide an optimization framework for synchromodal transport with flexible services. They formulate a Mixed Integer Linear Programming model to harness the benefits of service flexibility within the context of synchromodal transport planning. ...
... Their aim is to reduce the overall expenditure, encompassing transit expenses, transfer fees, storage charges, carbon tax, waiting expenses, and penalties for delays. Moreover, due to computational complexity, Zhang et al. (2022) employ several customized operators and integrate performance improvement methods into the Adaptive Large Neighborhood Search algorithm in order to efficiently address this challenge. The characteristics inherent to synchromodal transport, including the utilization of various modes, transshipment processes, the combination of fixed and adaptable vehicles, intricate timetables, and synchronization, are also considered in their model. ...
Article
In recent years, new concepts such as synchromodality have emerged to help carriers better leverage existing capacities and assets to achieve environmental and socio-economic sustainability. Synchromodality is a vast concept. It involves the intelligent utilization of various transport modes. Its main objective is to enhance the freedom and flexibility to switch between transport modes at transport network nodes. The emergence of synchromodality can be facilitated by optimization and simulation models associated with a sharing web service for decision-making. This article studies the concept of synchromodality in the scientific literature and highlights approaches using simulation and optimization techniques. The major challenge of this study lies in the effective implementation of synchromodality concept in practice, while respecting the instructions and constraints set by freight transport stakeholders from a more generic point of view. For that, we present an implementation of the modal shift on the Seine Axis Corridor. A simulation-optimization framework is proposed to generate reliable transport solutions based on the user preferences and environmental considerations. Finally, we resort to sensitivity analyses to assess the impact of variation of service times
... For solving large instances of the problem in reasonable time, a heuristic or metaheuristic approach is required since the solution space will be extensive. [14] has implemented a ALNS based metaheuristic to solve large sized instances for synchromodal transportation. In our study, we propose a Genetic Algorithm (GA) to tackle the problem, which can be potentially extended to address multiobjective optimization. ...
... Constraints (12-13) enforce precedence when one service arrives before another departs. Constraints (14) and (15) ensure time window and terminal working hours adherence. Constraints (16) and (17) Since it is practically infeasible to solve large-sized instances in respectable timeframe using exact methods, there is a necessity to introduce metaheuristic approaches. ...
Conference Paper
This paper studies the concept of synchromodal freight trans- portation. Modeled as a service network planning problem, a generic mathematical model which minimizes the total duration and the CO2 emissions is presented along with a metaheuristic approach using genetic algorithm. The model is validated on the instances based on the Seine axis river in France with a multitude of inland waterways and terminals. Keywords: Mixed integer linear programming, Synchromodality, Inter- modal Transportation, Service network planning, genetic algorithm
... As verified in Zhang et al. (2022c), solving a similar problem to optimality using an exact approach (Gurobi) is more computationally expensive than ALNS and the exact approach is unable to provide the optimal solution for large instances due to the complexity. Zhang et al. (2022b) verify that ALNS produces high-quality solutions with low computation time and performs well on large-scale instances in intermodal transport. Therefore, we use ALNS to solve the ITPP-SP in this study. ...
... The CO 2 e is converted into carbon tax using a price 4 of 8 euros per ton, based on the price of the EU emission allowance (Riessen et al., 2015). As reported in Guo et al. (2020) and Zhang et al. (2022b), the vehicle can wait for containers with a waiting fee, and containers can be stored in the terminal with a storage fee. We use the same storage and waiting unit costs 3 and 5 of 1 euro/(TEU h). ...
Article
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Sustainability is a common concern in intermodal transport. Collaboration among carriers may help in reducing emissions. In this context, this work establishes a collaborative planning model for intermodal transport and uses eco-labels (a series of different levels of emission ranges) to reflect shippers' sustainability preferences. A mathematical model and an Adaptive Large Neighborhood Search heuristic are proposed for intermodal transport planning of carriers and fuzzy set theory is used to model the preferences towards eco-labels. For multiple carriers , centralized, auction-based collaborative, and non-collaborative planning approaches are proposed and compared. Real data from barge, train and truck carriers in the European Rhine-Alpine corridor is used for extensive experiments where both unimodal carrier collaboration and intermodal carrier collaboration are analyzed. Compared with non-collaborative planning without eco-labels, the number of served requests increases and emissions decrease significantly in the collaborative planning with eco-labels as transport capacity is better utilized.
... For example, a pre-processing path generation heuristic for shipment-matching in synchromodal transportation was proposed in [16], focusing on minimizing transportation costs and carbon emissions. Similarly, [17] implemented an Adaptive Large Neighborhood Search (ALNS) to address a synchromodal transportation problem that incorporated vehicle routing, allowing for flexible origins and destinations for vehicles. The objectives included minimizing transportation costs, transfer costs, storage charges, carbon taxes, waiting costs, and delay penalties. ...
Conference Paper
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This study presents a multi-objective optimization approach for synchromodal transportation, incorporating external costs, a critical factor often overlooked in synchromodal literature. A mathematical model is developed to minimize total duration, carbon emissions, and external costs. The proposed multi-objective optimization eliminates the need for objective normalization, a common requirement in prior studies on synchromodality. To solve the problem, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed. The algorithm parameters are calibrated using Taguchi analysis, and the model is validated using instances based on the Seine Axis in France, which features a rich network of inland waterways and terminals. The results demonstrate that NSGA-II achieves optimal solutions for small instances and delivers high-quality solutions with minimal deviation from the bestknown solutions for larger instances. In addition, the impact of external costs on mode selection is analyzed, providing valuable insights into sustainable transport planning.
... Established studies on train schedules [10][11][12][13][14] mainly focus on the operational efficiency and vehicle scheduling of a single transportation mode, and lack the flexibility to adapt to the needs of multi-modal transportation systems. As transportation networks become increasingly complex, there is an urgent need for synergistic timetable optimization across various transportation modes, integrating operational information from railroads, highways, and airways to achieve efficient optimization of the entire travel chainn [15][16][17] . Particularly in the context of air-rail connectivity, there is a pressing need to enhance timetable design to facilitate seamless connections, improve transfer experiences, and reduce waiting time, thereby elevating overall travel efficiency. ...
Article
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The optimization of the train frequency of the Airport express line (AEL) is crucial for improving the efficiency of air-rail intermodal transport. It directly influences passenger transfer convenience and overall service quality, thereby bolstering the competitiveness of the transport system This study focuses on the optimization of “AEL and Flight Succession” in the context of air-rail intermodal transport. By analyzing the departure and landing time of airport flights, we assess the demand from various passenger flows and identify key factors that impact the connection between the AEL and flights. Based on these factors, we develop a demand-driven optimization model for AEL frequency, aimed at minimizing total travel time and the number of unserved passengers. A simulated annealing algorithm is employed to solve this model. The Lanzhou-Zhongchuan AEL serves as a case study for validation. The results demonstrate that the optimized schedule reduces total passenger travel time costs by 0.93% and 3.82%, respectively, while accounting for passenger time sensitivity and fairness principles, with a difference of 2.89% between these scenarios. In addition, the optimization scheme decreases the number of unserved passengers by 14.7% and reduces the percentage of flights and trains failing to meet occupancy constraints by 17%. This study illustrates that the schedule optimization strategy not only effectively increases the number of served passengers but also significantly reduces total intermodal and commuter travel time. Such findings provide a solid scientific foundation for AEL operations and management to develop a more efficient and rational train schedule in the context of air-rail intermodal transport.
... Under In future work, our model may be extended to a flow-based or routing model for intermodal and synchromodal transportation planning (Hrušovský et al. 2021, Zhang et al. 2022. When determining route options, we now only consider those where the expected travel time is within the order's provided delivery window. ...
Article
Accepted by: Aris Syntetos Intermodal transportation planning combines road with more sustainable transportation modes to encourage a modal shift. To evaluate the impact of a modal shift on transportation cost and emissions, we propose an intermodal transportation planning model to provide transparency in the cost-emissions trade-off. The model incorporates minimum load requirements, time windows, freight consolidation and stochastic travel times to generate alternative transportation options. It also includes order consolidation to facilitate the utilization of transportation modes that would otherwise be infeasible due to, for instance, minimum load requirements. We also propose a synchromodal planning tool to evaluate re-planning and re-consolidation options in response to disruptions. We numerically illustrate the working of our model using a representative network setting and quantify the trade-offs concerning costs and emissions by evaluating different transportation route options.
... The recent developments in information technologies and data analytics have facilitated the utilization of dynamic and stochastic information in decision-making processes (Ritzinger et al., 2015). In the literature, the most considered dynamic event is the arrival of new shipment requests (Pillac et al., 2013;SteadieSeifi et al., 2014;Zhang et al., 2022a). Rolling horizon framework, as a periodic reoptimization approach, has been well applied in vehicle routing problems (Arslan et al., 2019;Yıldız, 2021) and synchromodal transport planning (Li et al., 2015;Guo et al., 2020). ...
Article
Global synchromodal transportation is a promising strategy for providing efficient, reliable, flexible, and sustainable container shipping services across continents. It involves integrating multiple modes and routes owned by various operators to create a comprehensive transport plan. However, these operators often have their own local networks and are hesitant to cede control to a centralized platform. Instead, they prefer to share limited information in a coordinated manner to achieve a common goal without sacrificing their own benefits. This paper proposes a coordinated mechanism for global synchromodal transport planning, in which a global operator proposes incentives to local operators to select the most efficient modes and routes for shipping containers from one continent to another. An augmented Lagrangian relaxation approach is developed for the global operator to generate incentives, and a heuristic algorithm is designed to address the computational complexity of the optimization problems faced by local operators. We incorporate the proposed approaches with a rolling horizon framework to handle dynamic shipment requests received from spot markets and with a buffer strategy to address travel time uncertainties. The coordinated mechanism is tested on a real network between Asia and Europe, and results show that it can significantly increase total profits, reduce request rejections, and reduce infeasible transshipments compared to decentralized global transportation plans currently in use, particularly under scenarios with higher degrees of dynamism and uncertainty.
... However, the majority of relevant studies focus more on the delivery than the pickup and assume that containers are released at a fixed time [13,16] or there is the earliest release time [17]. Currently, quite a few works simultaneously model the two types of time windows, among which Sun et al. [2] and Zhang et al. [18] use the soft time window, while Sun [8] adopts the hard time window. Although these studies discuss the such time windows, their modeling still has the disadvantages of the hard and soft time windows indicated above. ...
Article
Full-text available
This study models a container routing problem using multimodal transportation to improve its economy, timeliness, and reliability. Pickup and delivery time windows are simultaneously formulated in optimization to provide the shipper and the receiver with time-efficient services, in which early pickup and delayed delivery can be avoided, and nonlinear storage periods at the origin and the destination can be minimized. Furthermore, the capacity uncertainty of the multimodal network is incorporated into the advanced routing to enhance its reliability in practical transportation. The LR triangular fuzzy number is adopted to model the capacity uncertainty, in which its spread ratio is defined to measure the uncertainty level of the fuzzy capacity. Due to the nonlinearity introduced by the time windows and the fuzziness from the network capacity, this study establishes a fuzzy nonlinear optimization model for optimization problem. A chance-constrained linear reformulation equivalent to the proposed model is then generated based on the credibility measure, which makes the global optimum solution attainable by using Lingo software. A numerical case verification demonstrates that the proposed model can effectively solve the problem. The case analysis points out that the formulation of pickup and delivery time windows can improve the timeliness of the entire transportation process and help to achieve on-time transportation. Furthermore, improving the confidence level and the uncertainty level increases the total costs of the optimal route. Therefore, the shipper and the receiver must prepare more transportation budget to improve reliability and address the increasing uncertainty level. Further analysis draws some insights to help the shipper, receiver, and multimodal transport operator to organize a reliable and cost-efficient multimodal transportation under capacity uncertainty through confidence level balance and transportation service and transfer service selection.
... In synchromodal transportation, modal choice and routing are not previously determined long in advance, while these decisions should be taken based on real-time information. In essence, synchromodality models mainly focus on the operational decision level due to the nature of the concept (Zhang et al., 2022). In line with this focus, scholarly research has extensively examined the challenges of shipment planning from various perspectives, principally organized according to the resultant decision type: shipment acceptance decision (based on the service configurations), shipment matching, and shipment routing optimization. ...
Article
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This paper offers an empirical study to explore the relationship between transportation modalities and environmental concerns, promoting the adoption of synchromodality as a strategic pathway to achieving sustainable freight transport. The study uses a synchromodal freight transportation platform to analyze the impact of carbon tax policy on modal shift and environmental sustainability. The synchromodal platform is based on an optimization model using Mixed Integer Linear Programming (MILP), incorporating carbon tax as a surrogate measure for environmental costs. A sensitivity analysis is conducted across four distinct scenarios in a case study in the Great Lakes region, focusing on the Canada-US transborder trade. The results of this study illustrate the considerable potential for increasing the utilization of more environmentally sustainable transportation modes in this region. While the addition of carbon tax entails increased total transportation costs for each unit of cargo, the synchromodal-enabled modal shift promises to mitigate transportation’s negative externalities, including congestion, environmental impacts, and noise pollution. The results also highlight the role of synchromodality as a catalyst for sustainable freight transport decisions in the context of a carbon-conscious world.
... In synchromodal transportation, modal choice and routing are not previously determined long in advance, while these decisions should be taken based on real-time information. In essence, synchromodality models mainly focus on the operational decision level due to the nature of the concept (Zhang et al., 2022). In line with this focus, scholarly research has extensively examined the challenges of shipment planning from various perspectives, principally organized according to the resultant decision type: shipment acceptance decision (based on the service configurations), shipment matching, and shipment routing optimization. ...
Article
Full-text available
This paper offers an empirical study to explore the relationship between transportation modalities and environmental concerns, promoting the adoption of synchromodality as a strategic pathway to achieving sustainable freight transport. The study uses a synchromodal freight transportation platform to analyze the impact of carbon tax policy on modal shift and environmental sustainability. The synchromodal platform is based on an optimization model using Mixed Integer Linear Programming (MILP), incorporating carbon tax as a surrogate measure for environmental costs. A sensitivity analysis is conducted across four distinct scenarios in a case study in the Great Lakes region, focusing on the Canada-US transborder trade. The results of this study illustrate the considerable potential for increasing the utilization of more environmentally sustainable transportation modes in this region. While the addition of carbon tax entails increased total transportation costs for each unit of cargo, the synchromodal-enabled modal shift promises to mitigate transportation's negative externalities, including congestion, environmental impacts, and noise pollution. The results also highlight the role of synchromodality as a catalyst for sustainable freight transport decisions in the context of a carbon-conscious world.
... Furthermore, the exact approach fails to deliver optimal solutions for large instances due to their inherent complexity. Zhang et al. (2022c) further substantiated that ALNS is capable of generating high-quality solutions within a short computation time and excels in handling large-scale instances in intermodal transport. Consequently, we have opted to employ ALNS to solve the problem in our current study. ...
... In this paper we propose an ALNS algorithm to solve VRPCD and its uncertainty variant. ALNS has been successfully implemented to handle many VRP extensions such as pickup and delivery problems (Ropke and Pisinger, 2006;Masson et al., 2013), routing with synchromodal constraint problems (Grangier et al., 2016;Zhang et al., 2022), and two echelon vehicle routing problems (2E-VRP) (Hemmelmayr et al., 2012). A thorough review of the literature about ALNS algorithms can be found in Pisinger and Ropke (2019). ...
... The results in Aksen et al. (2014), de Sá et al. (2015, and Dayarian et al. (2016) also show that exact algorithms are unable to provide the optimal solution for the large instances due to the complexity increase, while the ALNS produces high-quality solutions with low computation time. Zhang et al. (2022b) verify that ALNS can obtain the (near) optimal solution for synchromodal transport planning and it performs well on large-scale instances. Therefore, a customized ALNS is developed to solve the STPP-HVP. ...
Article
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In synchromodal transport, a freight forwarder usually serves multiple shippers with heterogeneous and vague preferences, such as low-cost, fast, or reliable transport. Ignoring shippers’ preferences will negatively impact the satisfaction of shippers and lead to the loss of them in the longer run. In order to incorporate these preferences, a Synchromodal Transport Planning Problem with Heterogeneous and Vague Preferences (STPP-HVP) is proposed and formulated as a mathematical model. Heterogeneous and Vague Preferences (HVP) are modeled through Multiple Attribute Decision Making approaches that integrate fuzzy set theory. The proposed model has two objectives, i.e., maximizing the number of served requests and minimizing the transportation cost. Preferences of shippers are set as constraints such that the freight forwarder needs to satisfy the preferred levels for each attribute. A heuristic algorithm (Adaptive Large Neighborhood Search) is proposed to find (near) optimal solutions. The case study in the European Rhine–Alpine corridor demonstrates that the proposed model can provide more attractive solutions to shippers compared with optimization which ignores preferences. Under various scenarios, the attributes, such as cost, time, emissions, reliability, and risk of damage, are analyzed and the (near) optimal modes and routes are suggested according to HVP. Moreover, the results show that the conflicts among attributes, conflicts among shippers, and conflicts between the freight forwarder and shippers are resolved by making one actor more satisfied without compromising any other actor’s preferences.
Article
A comprehensive understanding of shippers’ preferences can help transport freight forwarders create targeted transport services and enhance long-term business relationships. This research proposes an integrated approach to learn shippers’ preferences in synchromodal transport operations and optimize transport services accordingly. A preference learning method was developed to capture shippers’ preferences through pairwise comparisons of transport plans. To model the underlying complex nonlinear relationships and detect heterogeneity in preferences, artificial neural networks (NNs) were employed to approximate shippers’ utility for a specific plan. Leveraging the learned preferences, a synchromodal transport planning model with shippers’ preferences (STPM-SP) was proposed, with the objectives of minimizing the total transportation cost and maximizing shippers’ satisfaction. A case study based on the European Rhine-Alpine corridor was conducted to demonstrate the feasibility and effectiveness of the proposed approach. The results demonstrated that artificial NNs have the capacity to identify complex (i.e., nonlinear and heterogeneous) relationships in shippers’ preferences. The planning results showed that the STPM-SP effectively found solutions with a significant satisfaction improvement of 37%. This research contributes to learning shippers’ preferences in the transport operation process and highlights the importance of incorporating these preferences into the decision-making process of synchromodal transport planning.
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The optimization of airport express train schedules is crucial for air-rail intermodal transport, which directly affects the efficiency of passenger transfer and service level. It thus enhances the competitiveness of the transport system. In the context of air-rail intermodal transportation, this study explores the “Airport Express and Flight Succession” issue. Given the take-off and landing time of airport flights, we analyze the demand of different passenger flows and the key factors influencing the connection between airport express lines and flights, and construct a demand-driven optimization model of airport express train schedules based on air-rail intermodal transport conditions with the goal of minimizing the total travel time and the number of unserved passengers, and use a simulated annealing algorithm to solve the model and verify the model and algorithm by taking the case of the Lanzhou-Zhong Chuan Airport Express Line. The model and algorithm are validated by taking Lanzhou Zhong Chuan Airport Express as an example. The results show that the optimized timetable reduces the total travel time cost of passengers by 0.93% and 3.82%, the number of unserved passengers by 14.7%, and the proportion of flights and trains that do not satisfy the constraints of passenger occupancy by 17%, under the premise of considering the time sensitivity of different types of passengers and the principle of fairness, respectively. Therefore, the timetable optimization strategy of this study significantly reduces the total travel time of intermodal and commuter traffic while increasing the number of served passengers, which provides a solid scientific basis for the operation and management of the Airport Express to develop a more efficient and reasonable train timetable under the conditions of air-rail intermodal transportation.
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This paper presents a synchronized transfer strategy in which operators can schedule logistics vehicles among employees and arrange the transfer of commodities. The transfer time consists of the vehicle waiting time and commodity transition time. By adopting the strategy, logistics vehicles can visit fewer communities, resulting in savings in transportation cost under ensuring punctual delivery. An integer programming model based on a space-time-state network is proposed to describe the complex transfer process. To simplify the model, time window and vehicle capacity constraints are embedded into the network. The alternating direction method of multipliers (ADMM) is designed to solve the model. In the ADMM-based solution framework, the original model can be converted into a series of shortest-path searching subproblems and iteratively solved using a dynamic programming (DP) algorithm. Our computational results show that the proposed model and algorithm are efficient and competitive.
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The scheduling of ship traffic in the port is an important operation that aims to improve the navigation efficiency of the port, which is greatly affected by a variety of unpredictable events. In line with this research direction, this paper explores the ship traffic scheduling problem (STSP) with uncertain arrival and departure times (STSP-UN). STSP-UN treats arrival and departure times as random variables, with the objective of generating a schedule plan that minimizes the total waiting time of ships in the port while accommodating all possible variations in arrival and departure times within predefined uncertainty sets. A robust counterpart of the deterministic STSP formulation is derived where the uncertain arrival and departure times are confined within budget uncertainty sets. To obtain solutions for the proposed model, a hybrid optimization method (MAVNS) is used, combining a memetic algorithm (MA) for global exploration with a variable neighborhood search algorithm (VNS) that utilizes problem-specific neighborhood operators for local search. Numerical experiments are carried out at the Comprehensive port in Huanghua based on various instance sizes. The results validate the rationality and effectiveness of both the proposed robust optimization model and the algorithm. Specifically, the solution produced by MAVNS is 44.40% and 27.65% superior to that of the genetic algorithm (GA) and MA, respectively, for small-scale instances. For large-scale instances, the improvement rate is 41.25% and 30.02% compared to GA and MA, respectively. The MAVNS algorithm is a superior approach compared to an adaptive heuristic algorithm based on reinforcement learning (GSAA-RL), with consistently better performance across all instances and an impressive average gap of 31.20%. Furthermore, compared to the First-Come-First-Served (FCFS) strategy, MAVNS consistently exhibits superior performance in all instances, highlighting a remarkable average gap of 98.67%. The research enables ports to better respond to unexpected situations and reduce the negative impacts caused by uncertainty.
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In this chapter, we consider synchromodal planning of transport orders with the objective to minimize costs, delays, and CO2 emissions. Synchromodal planning is a form of multimodal planning in which the best possible combination of transport modes is selected for every transport order. The underlying problem is known as the multi-objective k-shortest path problem, in which we search for the k-shortest paths through a multimodal network, taking into account time-windows of orders, schedules for trains and barges, and closing times of hubs. We present a synchromodal planning algorithm that is implemented at a 4PL service provider located in the Netherlands. We illustrate our approach using simulation with order and network data from this logistics service provider. On the corridor from the Netherlands to Italy, an average cost reduction of 10.1 % and a CO2 reduction of 14.2 % can be achieved with synchromodal planning. © Springer International Publishing Switzerland 2016. All rights reserved.
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The integrated intermodal logistics network design problem consists of determining terminal locations and selecting regular routes and transportation modes for loads. This problem was formulated using a path-based formulation and a decomposition-based search algorithm has been proposed for its solution. Computational results show that this approach is able to obtain optimal solutions for non-trivial problem instances of up to 150 nodes in reasonable computational times. Previous studies have only been able to obtain approximate solutions for network problems of this size. A few general insights about the effects of design parameters on solution characteristics were also obtained.
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The recent development of Intelligent Transportation Systems offers the possibility of cooperative planning of multi-actor systems in a distributed framework, by enabling prompt exchange of information among actors. This paper proposes a modeling framework for cooperation in intermodal freight transport chains as multi-actor systems. In this framework, the problem of optimizing freight transportation is decomposed into a suitable set of sub-problems, each representing the operations of an actor which are connected using a negotiation scheme. A Discrete Event model is developed which optimizes the system on a rolling horizon basis to account for the dynamics of intermodal freight transport operations. This framework allows for an event driven short/medium term planning of intermodal freight transport chains. The proposed methodology is evaluated using a realistic case study, and the results are compared against the First-Come-First-Served strategy, highlighting the significance of cooperation in systems operating close to capacity.
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Pickup and delivery problems with time windows are frequently encountered by courier companies. These companies serve customers that require transportation of a package from a pickup location to a delivery location. This paper presents an empirical study on usefulness of transshipment points in such a context. Transshipment allows for a request to be served by two vehicles: one vehicle collects the load at the pickup location, drops it at a transshipment point, and another vehicle carries the load to the delivery location. The motivation for this work came from the practice observed in a large San Francisco based courier company that allows transshipment of loads between vehicles. The company serves large geographic area covering several neighboring cities. The main reasoning behind allowing transshipment lies in the idea of keeping drivers in their home area. We have investigated a generalization of this transshipment practice, allowing vehicles to move through entire service area in order to evaluate the usefulness of transshipment. We identify circumstances under which transshipment may be beneficial.
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This paper investigates intermodal freight transport planning problems among deep-sea terminals and inland terminals in hinterland haulage for a horizontally fully integrated intermodal freight transport operator at the tactical container flow level. An intermodal freight transport network (IFTN) model is first developed to capture the key characteristics of intermodal freight transport such as the modality change phenomena at intermodal terminals, physical capacity constraints of the network, time-dependent transport times on freeways, and time schedules for trains and barges. After that, the intermodal freight transport planning problem is formulated as an optimal intermodal container flow control problem from a system and control perspective with the use of the proposed IFTN model. To deal with the dynamic transport demands and dynamic traffic conditions in the IFTN, a receding horizon intermodal container flow control (RIFC) approach is proposed to control and to reassign intermodal container flows in a receding horizon way. This container flow control approach involves solving linear programming problems and is suited for transport planning on large-sized networks. Both an all-or-nothing approach and the proposed RIFC approach are evaluated through simulation studies. Simulation results show the potential of the proposed RIFC approach.
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The vehicle routing problem with trailers and transshipments (VRPTT) is a recent and challenging extension of the well-known vehicle routing problem. The VRPTT constitutes an archetypal representative of the class of vehicle routing problems with multiple synchronization constraints (VRPMSs). In addition to the usual task covering constraints, VRPMSs require further synchronization between vehicles, concerning spatial, temporal, and load aspects. VRPMSs possess considerable practical relevance, but limited coverage in the scientific literature. The purpose of the present paper is to describe how several important types of VRPMSs, such as multi-echelon location-routing problems and simultaneous vehicle and crew routing problems, can be modelled as VRPTTs.
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In recent years, many important real-world applications are studied as “rich” vehicle routing problems that are variants and generalizations of the well-known vehicle routing problem. In this paper we address the pickup-and-delivery version of this problem and consider further generalization by allowing transshipment in the network. Moreover, we allow heterogenous vehicles and flexible fleet size. We describe mixed integer-programming formulations for the problem with and without time windows for services. The number of constraints and variables in the models are bounded by polynomial size of the problem. We discuss several problem variants that are either captured by our models or can be easily captured through simple modifications. Computational work gave promising results and confirms that transshipment in network can indeed enhance optimization.
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This paper presents a survey of vehicle routing problems with multiple synchronization constraints. These problems exhibit, in addition to the usual task covering constraints, further synchronization requirements between the vehicles, concerning spatial, temporal, and load aspects. They constitute an emerging field in vehicle routing research and are becoming a "hot" topic. The contribution of the paper is threefold: (i) It presents a classification of different types of synchronization. (ii) It discusses the central issues related to the exact and heuristic solution of such problems. (iii) It comprehensively reviews pertinent literature with respect to applications as well as successful solution approaches, and it identifies promising algorithmic avenues.
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This article studies the vehicle routing problem with trailers and transshipments (VRPTT), a practically relevant, but challenging, generalization of the classical vehicle routing problem. The article makes three contributions: (i) Building on a nontrivial network representation, two mixed-integer programming formulations for the VRPTT are proposed. (ii) Based on these formulations, five different branch-and-cut algorithms are developed and implemented. (iii) The computational behavior of the algorithms is analyzed in an extensive computational study, using a large number of test instances designed to resemble real-world VRPTTs.Copyright © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 63(1), 119–133 2014
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Multimodal transportation offers an advanced platform for more efficient, reliable, flexible, and sustainable freight transportation. Planning such a complicated system provides interesting areas in Operations Research. This paper presents a structured overview of the multimodal transportation literature from 2005 onward. We focus on the traditional strategic, tactical, and operational levels of planning, where we present the relevant models and their developed solution techniques. We conclude our review paper with an outlook to future research directions.
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In this research, we develop a multiobjective vehicle routing and scheduling heuristic for a pickup and delivery problem. The problem contains time window, advanced request, multi-vehicle and many-to-many transport. In addition, the fleet size is not predetermined, and customers are allowed to transfer between vehicles. The objectives of scheduling are to minimize vehicle expense, tardiness and travel time. We propose a concurrent scheduling approach, which allocates customers to more than one vehicle and assigns more than one customer to a vehicle at a time. It differs from the usual concurrent approach in three aspects: (i) it uses the look-ahead strategy to construct miniroute; (ii) it adopts the head/tail, head, and tail integration techniques; and (iii) it allows interactivity. The procedure takes full advantage of due time and travel time information and is implemented through a computer program. It is a one-phase heuristic that can be reiterated when necessary. We provide detailed programming procedures and present the computational results of the proposed algorithm through the real data.
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In this survey, a classification of 24 asymmetric traveling salesman problem (ATSP) formulations is presented. The strength of their LP relaxations is discussed and known relationships from the literature are reviewed. Some new relationships are also introduced, and computational results are reported.
Transport 2050: Comission Outlines Ambitious Plan to Increase Mobility and Reduce Emmisions
  • S Kallas
Kallas, S., 2011. Transport 2050: Comission Outlines Ambitious Plan to Increase Mobility and Reduce Emmisions. Technical report, Technical Report March.
A green intermodal service network design problem with travel time uncertainty
  • E Demir
  • W Burgholzer
  • M Hrušovskỳ
  • E Arıkan
  • W Jammernegg
  • T Van Woensel
Demir, E., Burgholzer, W., Hrušovskỳ, M., Arıkan, E., Jammernegg, W., Van Woensel, T., 2016. A green intermodal service network design problem with travel time uncertainty. Transp. Res. B 93, 789-807.
European Environment Agency (EEA): Greenhouse gas emissions from transport in Europe
EEA, 2020. European Environment Agency (EEA): Greenhouse gas emissions from transport in Europe. https://www.eea.europa.eu/data-and-maps/indicators/ transport-emissions-of-greenhouse-gases-7/assessment [Online; accessed 14-September-2021].