This study focuses on two-echelon synchronized logistics problems in the context of integrated water- and land-based transportation (IWLT) systems. The aim is to meet the increasing demand in city logistics as a result of the growth in transport activities, including parcel delivery, food delivery, and waste collection. We propose two models, a novel mixed integer linear joint model, and a logic-based Benders’ decomposition (LBBD) model, for a two-echelon problem under realistic settings such as multi-trips, time windows, and synchronization at the satellites with no storage and limited resource capacities. The objective is to optimize transfers and satellite assignments, thereby reducing overall logistics costs for street vehicles and vessels. Computational experiments demonstrate that the LBBD model is more robust in terms of solution quality and solution time on average while the added value of the LBBD is more evident when solving large-scale instances with 100 customers, reducing the overall costs by 10.6% on average and significantly reducing the fleet costs on both networks. Furthermore, we assess the effect of changing cost parameters and satellite locations in the proposed IWLT system–analyzing system behavior and suggesting potential improvements–and evaluate several system alternatives in city logistics–consisting of different transportation network designs (single- and two-echelon), vehicle types, and operational constraints. On average, the proposed two-echelon IWLT system reduces the number of kilometers traveled by vehicles at street level by ranging from 20% to 30% compared to a typical single-echelon service design that relies solely on trucks.

@en

Mobility is vital for societal wellbeing, economic growth, social inclusion, and access to essential amenities. However, the current system faces significant challenges, including environmental impact, unequal access, and safety concerns. […]@en

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.

@en

This paper presents a hybrid system dynamics and agent-based model to simulate the dynamic evolution of consumer preferences for parcel delivery. Scenarios provide insight about preference development and capacity growth of delivery services.@en

This review aims to explore the potential for synchromodal transport planning at the operational level. Synchromodal transport planning involves the optimization of the movement of freights across multiple transport modes, with the objective of minimizing cost, improving efficiency, and promoting sustainability. Through this review, we provide a roadmap for methodological developments in the area of operational synchromodal transport planning research. The roadmap provides a comprehensive categorization of different fields and their trends. The fundamentals of synchromodal transport planning are evolved to more flexible planning approaches that take practical considerations and multiple objectives into account. Dynamic planning is evolving to become more adaptive and resilient to changing environments. Finally, collaborative planning will continue to integrate both vertical and horizontal collaboration with distributed optimization approaches. With dynamic and collaborative approaches considering preferences, the full potential of synchromodal transport planning can be unlocked towards efficient and sustainable freight transportation.

@en

This book serves as a comprehensive roadmap for navigating the realm of Operations Research (OR). From laying down fundamental mathematical principles to crafting precise modeling techniques and their solution methods, it culminates in a panoramic view of OR models mirroring real-world operations. Delving into diverse applications-from assignment problems to network problems like graph coloring and minimum spanning trees, and navigating through routing problems that are very common in logistics-the book equips readers with practical insights. Each model is accompanied by meticulously detailed examples, seamlessly integrated with hyperlinked codes accessible via an open repository. Moreover, it introduces an engaging dimension with hyperlinks to three serious games replicating some cornerstone OR models, offering a playful yet educational environment for solo or group experimentation.@en

The development of the global COVID-19 pandemic from 2020 onward has had significant impact on the world and specifically the maritime industry. Striking examples were COVID-19 outbreaks onboard the Diamond Princess cruise vessel and the U.S.S. Theodore Roosevelt aircraft carrier at the start of the pandemic. Contagious disease management onboard large passenger ships remains a complex issue, amplified by the international character of the industry, confined environment and shared facilities. This paper therefore presents an integrated infection and crowd behavior model used to calculate agent-specific infection risk, incorporating guest and crew circulation through a passenger ship layout. The integrated model is used to investigate the effect of ship layout design, capacity reduction and mask wearing on COVID-19 airborne infection risk onboard large passenger vessels.@en

The design and pricing of services are two of the most important decisions faced by any intermodal transport operator. The key success factor lies in the ability to meet the needs of the shippers. Therefore, making full use of the available information about the demand helps to come up with good design and pricing decisions. With this in mind, we propose a Choice-Driven approach, incorporating advanced choice models directly into a Service Network Design and Pricing problem. We evaluate this approach considering both deterministic and stochastic choice models. To reduce the computational time for the stochastic instances, we propose a predetermination heuristic. The proposed models are compared to a benchmark, where shippers are solely cost-minimizers. Results show that the operator's profits can be significantly improved, even with deterministic models. The stochastic versions further increase the realized profits: in particular, considering shippers’ heterogeneity allows a better estimation of the demand.

@en

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.

@en

The increasing volume of global freight trade, coupled with economic growth, necessitates ongoing innovation in optimizing freight operations. Over the past decade, the concept of synchromodality has been explored to encourage a modal shift from unimodal to multimodal transport. Synchromodality, with its flexibility feature, can create more resilient freight transport systems. Various models employing different techniques have been proposed to establish a resilient synchromodal framework capable of reacting to disruptions. However, there are only few studies addressing the unknown duration of disruptions. This research proposes a learning-based modular framework comprising to capture the dynamics of disruptions in multimodal transport and learn to make more effective decisions, thus addressing the challenge of limited prior knowledge about disruptions and enabling fast responses to disruptions.

@en

The objective of this study is to address the issue of service time uncertainty in synchromodal freight transport, which can cause delays, inefficiencies, and reduced satisfaction for shippers. The proposed solution is an online deep Reinforcement Learning (RL) approach that takes into account the service time uncertainty, assisted by an Adaptive Large Neighborhood Search (ALNS) heuristic that provides state and reward information based on the routing and scheduling. The proposed planning approach re-plans in response to unexpected events and learns from real-time information from various transport modes, including road, railway, and inland waterways. The performance of the proposed planning approach is evaluated in the European Rhine-Alpine corridor under various scenarios with different types and severities of unexpected events. The results demonstrate that the RL approach consistently outperforms other strategies by effectively handling service time uncertainty, leading to reduced costs, emissions, and waiting time, as well as decreased transport delays and improved rewards through accurate decision-making and agile transport re-planning. This study also finds that incorporating event severity information improves the average reward obtained by the RL approach in scenarios involving multiple types of events.

@en

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.

@en

Cooperation between container transport service providers can increase efficiency in the logistics sector significantly. However, cooperation between competitors requires co-planning methods that not only give the cooperating partners an advantage towards external competition but also protect the partners from losing information, clients and autonomy to one another. Furthermore, modern freight transport requires real-time methods that react to new information and situations. We propose a real-time, co-planning method called departure learning based on model predictive control where a barge operator considers the joint cost of themselves and a truck operator when deciding barge departures. At regular time-intervals, the barge operator uses previous information to propose a number of departure schedules for which the truck operator discloses their corresponding expected operational costs. Co-planning thus only requires limited exchange of aggregate data. The impact of using departure learning on the transport system’s performance and the method’s learning quality are thoroughly investigated numerically on an illustrative, simulated, realistic hinterland network. With as little as six schedules being exchanged per timestep, departure learning outperforms decentralized benchmark methods significantly in terms of operational costs. It is found that using knowledge about the performance of related schedules is important for the exploration of opportunities, but if this is relied upon too much, the realized solution becomes more costly. It is also found that departure learning is a reliable and realistic co-planning method that especially performs well when peaks in the demand make departure times highly correlated to the cost of operating the transport system, such as in hinterland areas of ports which receive large container ships.

@en

Container transport via inland waterways currently faces several challenges affecting its competitiveness with other modes. These challenges include the high waiting time experienced by container barges and the low priority given to container barges in deepsea ports. To mitigate these challenges, a new concept known as the Modular Mobile Terminal (MMT) is introduced to create a dedicated floating barge handling and consolidation space for containers in deepsea ports. Based on this, the present study proposes an assessment methodology examining the feasibility of the MMT from a logistical and economic perspective. In doing this, a time optimization model was developed to determine the number of MMTs leading to the most time savings for container barges. It also helps target a market by finding the hinterland flows that can be positively linked to the MMTs. Afterward, an economic evaluation is conducted to determine the cost savings for the actors and determine under which conditions the actors would benefit from using the MMT system. The proposed methodology is then applied in a case study for the ports of Antwerp and Rotterdam to derive insights into the efficiency and profitability of the MMTs. Results revealed that the MMTs would be most suitable for vessels transporting small cargo volumes below 60 Twenty Foot Equivalent Units (TEUs). Furthermore, the analysis suggests that two MMTs would be optimal for the port of Antwerp, and four for the port of Rotterdam, to achieve an overall net benefit for all the actors. Thus, it can be concluded that the MMTs are most suitable for handling and consolidating cargoes from container barges with small call sizes.

@en

Several measures have been developed to prevent emissions from inland water transportation. However, it is challenging to weigh all the aspects to identify the pathway that will ultimately result in zero-emission inland shipping. A data-driven virtual representation of the inland shipping system can be used to evaluate zero-emission strategies, effectiveness of policies and technologies, and consequences of their implementation. This multi-level digital twin can realistically represent the system with all relevant components, which needs to be validated using real-world data. Subsequently, future scenarios can be imposed on the digital twin, and the proposed intervention measures can be applied, based on which their efficiency can be assessed together with the inland shipping sector. This study discusses the essential aspects of designing a digital twin for an IWT. Three aspects are considered essential: individual ships, logistics chains, and infrastructure. As these research topics span various scales, ranging from a single vessel to an entire infrastructure network, an agent-based approach is suitable for forming the basis of the digital twin. Consequently, potential interventions can be considered, ranging from the application of new technologies to individual vessels to policy measures implemented for an entire shipping corridor or various bunker infrastructure strategies in the network. Additionally, the impact of the implemented interventions can be evaluated at any desired scale, ranging from the individual ship level and its emissions to the network level and aggregated emissions in an entire area, or the impact on the logistics chain.@en

Through the vast adoption and application of emerging technologies, the intelligence and autonomy of smart mobility can be substantially elevated to address more diversified demands and supplies. Along with this trend, a systematic collaboration among three essential elements of smart mobility services, namely devices, data and functions, is being studied to comprehensively break down the intrinsic barriers that existed in current solutions, to support the integration of connectable devices, the fusion of heterogeneous data, the composability of reusable functions, and the flexibility in their cooperations. To enable such a collaboration, this paper proposes a federated platform, called Future Mobility Sensing Advisor (FMSA), which can 1) manage the three elements through standardized interfaces separately and uniformly; 2) create a fully connected knowledge graph to orchestrate the three elements efficiently and effectively; 3) support the client-server interaction in centralized and federated modes to handle service requests and edge resources with various availability and accessibilities jointly and adaptively; and 4) accommodate various mobility services to foster harmonious and sustainable mobility tenderly and invisibly. Moreover, the efficiency and effectiveness of the platform are also tested through a performance evaluation, and a pilot supported at the Great Boston Area, respectively. As a result, it shows that FMSA can 1) achieve high performance by using the two interaction modes selectively, and 2) renovate smart mobility towards sustainability through personalized services that can measure user preferences and system objectives mutually.

@en

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.

@en

Global synchromodal transportation involves the movement of container shipments between inland terminals located in different continents using ships, barges, trains, trucks, or any combination among them through integrated planning at a network level. One of the challenges faced by global operators is the matching of accepted shipments with services in an integrated global synchromodal transport network with dynamic and stochastic travel times. The travel times of services are unknown and revealed dynamically during the execution of transport plans, but the stochastic information of travel times are assumed available. Matching decisions can be updated before shipments arrive at their destination terminals. The objective of the problem is to maximize the total profits that are expressed in terms of a combination of revenues, travel costs, transfer costs, storage costs, delay costs, and carbon tax over a given planning horizon. We propose a sequential decision process model to describe the problem. In order to address the curse of dimensionality, we develop a reinforcement learning approach to learn the value of matching a shipment with a service through simulations. Specifically, we adopt the Q-learning algorithm to update value function estimations and use the ϵ-greedy strategy to balance exploitation and exploration. Online decisions are created based on the estimated value functions. The performance of the reinforcement learning approach is evaluated in comparison to a myopic approach that does not consider uncertainties and a stochastic approach that sets chance constraints on feasible transshipment under a rolling horizon framework.

@en

Understanding the modal split in freight transportation is a key factor for the successful implementation of innovations. Mode choice models should then be as representative of reality as possible. The use of disaggregate shipment data can help to achieve it. However, shipment data are often unavailable due to confidentiality issues. As a result, numerous models using only aggregate data have been developed, but their capacity to capture heterogeneity in preferences remains limited. In this paper, we propose a Weighted Logit Mixture model to estimate heterogeneous mode choice preferences of shippers directly from aggregate data. The proposed Weighted Logit Mixture is applied to a case study along the European Rhine-Alpine corridor and allows to estimate the probability distribution of the cost sensitivity among the population. The estimation results show that there exists a substantial variation of the cost sensitivity regarding intermodal transport. The proposed methodology is also compared to a state-of-the-art Weighted Logit model to assess its potential. This reveals that the proposed Weighted Logit Mixture exhibits at least a similar predictive power to the benchmark while achieving a better description of the population’s preferences that enables policy-makers to take better informed decisions and appropriate actions.@en

This paper considers a coordinated global shipment matching problem in which a global operator receives shipment requests from shippers and three local operators provide local transport services in different geographical areas. While local operators make local matching decisions, the global operator combines the matched local services into itineraries to provide integrated transport for shipments. To handle the interconnecting constraints between different operators, a Lagrangian relaxation heuristic approach is developed. Under the proposed approach, the original problem is decomposed into local operator-related subproblems. These subproblems are optimized iteratively under local constraints as well as under the incentives imposed by the global operator to meet interconnecting constraints. The experiment results show that with the proposed approach, global transport planning that requires coordination among different operators to achieve a common goal can be realized.

@en