Search Results (18)

Background: Disruptions in freight transportation—such as service delays, infrastructure failures, and labor strikes—pose significant challenges to the reliability and efficiency of intermodal networks. To address these challenges, this study introduces Adaptive Intermodal Transportation (AIT), a resilient and flexible planning framework that enhances Synchromodal Freight Transport (SFT) by integrating real-time disruption management. Methods: Building on recent advances, we propose two novel strategies: (1) Reassign with Delay Buffer, which enables dynamic rerouting of shipments within a user-defined delay tolerance, and (2) (De)Consolidation, which allows splitting or merging of shipments across services depending on available capacity. These strategies are incorporated into a re-planning module that complements a baseline optimization model and a continuous disruption-monitoring system. Numerical experiments conducted on a Great Lakes-based case study evaluate the performance of the proposed strategies against a benchmark approach. Results: Results show that under moderate and high-disruption conditions, the proposed strategies reduce delay and disruption-incurred costs while increasing the percentage of matched shipments. The Reassign with Delay Buffer strategy offers controlled flexibility, while (De)Consolidation improves resource utilization in constrained environments. Conclusions: Overall, the AIT framework demonstrates strong potential for improving operational resilience in intermodal freight systems by enabling adaptive, disruption-aware planning decisions. Full article

Intermodal freight transport (IFT) requires a large number of optimisation measures to ensure its attractiveness. This involves numerous control decisions on different time scales, making integrated optimisation with traditional methods almost unfeasible. Recently, a new trend in optimisation science has emerged: the application of Deep Learning (DL) to combinatorial problems. Neural combinatorial optimisation (NCO) enables real-time decision-making under uncertainties by considering rich context information—a crucial factor for seamless synchronisation, optimisation, and, consequently, for the competitiveness of IFT. The objective of this study is twofold. First, we systematically analyse and identify the key actors, operations, and optimisation problems in IFT and categorise them into six major classes. Second, we collect and structure the key methodological components of the NCO framework, including DL models, training algorithms, design strategies, and review the current State of the Art with a focus on NCO and hybrid DL models. Through this synthesis, we integrate the latest research efforts from three closely related fields: optimisation, transport planning, and NCO. Finally, we critically discuss and outline methodological design patterns and derive potential opportunities and obstacles for learning-based frameworks for integrated optimisation problems. Together, these efforts aim to enable a better integration of advanced DL techniques into transport logistics. We hope that this will help researchers and practitioners in related fields to expand their intuition and foster the development of intelligent decision-making systems and algorithms for tomorrow’s transport systems. Full article

Improved navigability can enhance inland waterway transportation efficiency, contributing to synchro-modal logistics and promoting sustainable development in regions that can benefit from the presence of considerable waterways. Modern technological solutions, such as digital twins in corridor management systems, must integrate functions of navigability forecasts that provide timely and reliable information for safe trip planning. This information needs to account for the type of vessel and for the environmental and geomorphological characteristics of each navigation trait. This paper presents a case study, within the EU project CRISTAL, focusing on the Italian Po River, of which the navigability forecast requirements of a digital twin are illustrated. Preliminary results to deliver navigability risk information were obtained. In particular, the statistical correlation of water discharge and water depth, computed from historical data, suggested that efficient forecast models for navigability risk, given some water discharge forecasts, could be built. To this aim, the LSTM (long-short-term-memory) technique was used on the same data to provide models linking water discharge and water depth predictions. Future work involves further testing these models with updated real data and integrating outcomes with climatic and infrastructure management information to enhance the accuracy of the risk information. Full article

The logistics landscape in e-commerce is undergoing a profound transformation toward sustainability and autonomy. This paper explores the implementation of autonomous maritime and last-mile transportation solutions to optimize the entire logistics chain from factory to customer. Building on the lessons learned from the maritime industry’s digital transformation, the study identifies key features and proposes a forward-looking autonomous maritime and last-mile transportation system. Emphasizing the role of geospatial technologies, the proposed system employs GIS-based electronic route optimization for efficient goods delivery, integrating onboard and ashore GIS-based sensors for enhanced location precision. A case study was built to analyze the implementation of autonomous means of transport along the route of a product from factory to customer. The integration of autonomous systems shows substantial improvements in logistics performance. Synchromodal logistics and smart steaming techniques can be utilized to optimize transportation routes, resulting in reduced fuel consumption and emissions. The findings reveal that autonomous maritime and last-mile transport systems can significantly enhance the efficiency, flexibility and sustainability of e-commerce logistics. The study emphasizes the need for advanced technological integration and provides a comprehensive framework for future research and practical applications in the logistics industry. Full article

The transport sector accounts for approximately a quarter of the EU’s total greenhouse gas emissions, with freight transport alone accounting for about one-third of the overall transport-related emissions. Mitigating the sector’s environmental impact is crucial for tackling climate change and achieving sustainable development goals. Modal shift is one of the main solutions to address this challenge; however, many companies have yet to realize its full potential. This paper presents a survey conducted in the Flanders region of Belgium, aiming to identify the challenges and barriers faced by industry players in this key geographical area and to explore the reasons behind the limited implementation of synchromodal transport among them. The survey evaluates the current state of synchromodal transport adoption and offers valuable insights for policymakers and industry stakeholders aiming to enhance sustainability in the logistics sector. The findings emphasize that to overcome the identified challenges, both policy support and the companies’ commitment are required. Policy support includes establishing consistent regulations and promoting greener transport modes through providing incentives and technological advancements. This research contributes to the field by examining barriers to the adoption of synchromodality and exploring its application within the context of Flanders. By focusing on this strategic logistics hub, the study provides insights and recommendations tailored to the specific challenges of the region’s logistics sector. The challenges faced by industry players in Flanders offer a deeper understanding of modal shift dynamics, facilitating informed decision-making for policymakers and industry stakeholders. Implementing these strategies paves the way for more environmentally friendly, efficient, and integrated transport, benefiting both the industry and the planet. Full article

Logistics is significantly impacted by quality/quantity issues associated with data collection and data sharing restrictions. Nonetheless, public data from national entities and internet-of-things (IoT) solutions enable the development of integrated tools for performance analysis and real-time optimization of logistics networks. This study proposes a three-module data-driven system architecture that covers (a) logistics data collection tools, (b) logistics services performance evaluation, and (c) the transition to synchromodal systems. Module 1 integrates multisource data from national logistics platforms and embedded devices placed within intermodal containers. A multigraph representation of the problem is conceived. Environmental, economic, and operational data are generated and injected into a digital twin. Thus, key performance indicators (KPIs) are computed by simulation or direct transformation of the collected data. Module 2 uses Multi-directional Efficiency Analysis, an optimization algorithm that benchmarks multimodal transportation routes of containers using prior KPIs. Outputs are a technical performance index relevant to logistics clients and improvement measures for logistics service providers. A real case study application of the solution proposed for Module 2 is presented. Module 3 provides real-time scheduling and assignment models using CP-sat solvers, accommodating varying system dynamics and resource availability, minimizing makespan and operational costs. Full article

The search for more sustainable freight transport has been the focus in the last decades. In this way, the concept of synchromodality was built considering the collaboration of shippers and logistic service providers to enable real-time switching between transport modes and mode-free transport bookings, encouraging more flexible and sustainable freight transportation. However, there are several challenges to its implementation in real life, which is the focus of this paper. To achieve this, in addition to a literature study, a case study was conducted in Flanders, using a combination of qualitative methods, i.e., focus groups (FG) and expert interviews, aiming to bridge the gap between theory and practice. Challenges such as real-time decision making, limited infrastructure capacity, and the need for stakeholder collaboration were emphasized. Expert insights highlight the need for a forecast-based approach to facilitate mode shift decisions, particularly from roadway to inland waterway transport (IWT). The analysis underscores the potential benefits of the proposed synchromodal technology while acknowledging the requirements needed to make it real. Full article

Along with the rapid development of the economy and the increasing demand for transport quality, renewed attention to multimodal transportation has emerged. However, due to the unpredictable transport environment in the process of multimodal transportation and the uncertainties caused by the change of transport market demand, transport decision-makers face many difficulties in transport planning and routing, which has become an obstacle to the development of multimodal transportation. As an advanced form of multimodal transportation, synchromodal transportation has received extensive attention in recent years. Due to its flexibility and sustainability, synchromodal transportation can effectively deal with the uncertainty in multimodal transportation. Based on the problem of multimodal transportation networks with uncertainties, this paper proposed a mixed time-window-constrained path optimization model with the goal of minimizing the total transportation costs, and proposed corresponding assumptions considering three types of uncertainty. The model could be solved by a genetic algorithm using MATLAB software. Using this model, the best transportation path and the optimal scheme considering synchronization were obtained. The results of the case study showed that synchromodal transportation can adjust the transportation plan in time to respond to uncertainties, thus, effectively reducing transportation costs. This paper favorably supported the introduction of synchromodal transportation, which is of significance to the development of multimodal transportation in the future. Full article

Synchromodality is an emerging concept in supply chain management. A synchromodal supply chain can be defined as a multimodal transportation planning system, wherein the different agents work in an integrated and flexible way that enables them to dynamically adapt the transport mode based on real-time information from stakeholders, customers, and the logistic network. The potential of synchromodality for the fast-moving consumer goods (FMCG) industry is related to the nature of business. The FMCG market is characterized by relatively low margins and high turnover, which is especially important in export supply chains. However, for a company, it may be challenging to objectively evaluate the costs and benefits, not to mention the design of a synchronized supply chain. In order to facilitate the adoption of the concept and guide the practitioners, our study put forward the following research questions: What should be considered in incorporating synchromodality in the export supply chain for FMCG? How should companies approach tradeoffs among factors affecting the supply chain? To answer these questions, we propose an adaptable framework, which should be considered a primary contribution of our study. The framework incorporates the center of gravity model, mixed integer linear programming, and sensitivity analysis. The framework is validated using a real-world problem from a multinational FMCG company. The problem involves the optimal volume allocation and the selection of the most efficient transportation mode for inland freight. Our study demonstrates that incorporating synchromodality in the export supply chain could reduce the overall cost by 9% and enhance flexibility by allowing multiple modes of transportation. Full article

Synchromodal transportation is a novel multimodal transportation concept. It builds on a collaboration of shippers and logistic service providers to enable real-time switching between transport modes and mode-free transport bookings, enabling more flexible and sustainable freight transportation. This paper summarizes the current state of research since 2010 by means of a systematic literature review. A comprehensive taxonomy consisting of five dimensions and 13 categories for both qualitative and quantitative papers is developed. The results reveal a mixed picture, with high consistency in geographical areas of synchromodal transportation implementation and suitable modeling of operational disruptions and uncertainties. However, compared to multimodal or road transportation, there is little alignment in the forms of collaboration, network organization, or the advantages of synchromodal transportation. Finally, the main fields for future research are identified, namely business, legal, technological, modeling, and awareness. Full article

Synchromodality is the key to finding sustainable solutions for logistics, especially across larger networks. The era of the COVID-19 pandemic has brought special attention to the disruptions in demand and supply across the world and has accentuated the need for sustainable transportation networks to handle such anomalies in supply chains. The proposed research develops a mathematical model for an intermodal transportation network and investigates the model on one of the largest and most widely discussed supply chain projects of the One-Belt-One-Road (OBOR) initiative. The proposed bi-objective model focuses on time and cost functions with rail, roads, and ships as modes of transportation. A detailed analysis was performed on various mode alternatives and links to evaluate their performance. The study provides an insightful understanding of the network with several suggestions. In contrast to roads and trains, container ships depict a fourfold increase in fuel consumption for an average ship weighing 4500 TEUs with the increase in shipping speed. It was concluded that increasing port capacity and reducing custom clearance time can have a major impact on lead times, and this is directly influenced by a country’s ease of doing business. Moreover, with its several branches, the OBOR initiative can provide a robust supply chain with increased logistical capacity. Full article

Slow steaming, i.e., the possibility to ship vessels at a significantly slower speed than their nominal one, has been widely studied and implemented to improve the sustainability of long-haul supply chains. However, to create an efficient symbiosis with the paradigm of synchromodality, an evolution of slow steaming called smart steaming is introduced. Smart steaming is about defining a medium speed execution of shipping movements and the real-time adjustment (acceleration and deceleration) of traveling speeds to pursue the entire logistic system’s overall efficiency and sustainability. For instance, congestion in handling facilities (intermodal hubs, ports, and rail stations) is often caused by the common wish to arrive as soon as possible. Therefore, smart steaming would help avoid bottlenecks, allowing better synchronization and decreasing waiting time at ports or handling facilities. This work aims to discuss the strict relationships between smart steaming and synchromodality and show the potential impact of moving from slow steaming to smart steaming in terms of sustainability and efficiency. Moreover, we will propose an analysis considering the pros, cons, opportunities, and risks of managing operations under this new policy. Full article

Long-haul continental freight flows still heavily rely on unimodal road transport. Intermodal transport, combining road transport with other transport modes, has the potential to have lower operating costs and to be more environmentally sustainable. However, road transport benefits from its better flexibility and adaptability to sudden disruptions and uncertainties. To facilitate a modal shift towards intermodal transport, it is crucial to improve its resilience (i.e., capability to resist and recover from sudden disruptions). Synchromodality is an extension of intermodality in which decisions on modal choice and routing are not predefined long in advance but are taken based on real-time information and may provide a step in that direction. The conducted literature review investigates how uncertainty can be handled in intermodal and synchromodal freight transport networks. The literature is classified based on the planning level, which is either strategic, tactical or operational. The main focus is on the studied types of uncertainty and the proposed solution approaches. This work contributes to the research field by reviewing the literature on intermodal and synchromodal transport with uncertainty, presenting measures to mitigate the effects of uncertainty and proposing future research directions. Full article

Synchromodality is a freight transport process in which information is exchanged expeditiously in order to maximize the benefits of different modes of transport and transport nodes in terms of efficiency and environmental impact. The aim of the study is to analyze the problems of synchronized intermodal traffic management between the main port and inland transport nodes in European transport corridors and to find reliable solutions to these problems. Therefore, the main purpose of this article is to investigate the problem of the distribution of containers transported by rail between two transport terminals in a synchronous transport network. A specific optimization model is presented in this article. This optimization task is formulated as a stochastic integer programming model between the terminals located in Vilnius and Klaipeda Seaport, the essence of which is as follows: (a) to minimize the waiting time for container cargo at the location—terminal No. 1; (b) to minimize the total journey time of the train; (c) to minimize the waiting time for containerized cargo at the point of arrival—terminal No. 2. Full article

Synchromodal transport incorporates real-time events in a dynamic manner in order to facilitate the most suitable selection of modes, routes and handling points. Up until now, current assessments rely on analytical models. Most of these models average distances for barges and trains via route mapping platforms that provide realistic distances for road only. To reflect on real-world developments more accurately, new thinking and modelling approaches are necessary to bridge academic models with physical transport processes. This paper introduces a computational model which computes movements of agents in geographically referenced space. The model captures stochastic parallel processes for each mode, and simulates decentralized delivery performance of each order in terms of cost, time and emissions at an operational level. Furthermore, we study the routing of individual orders and their responsiveness to disruptions. Computational experiments are performed within a case study which concerns imports of retail goods by unimodal truck transport from France to Belgium. Our findings show that dynamic synchromodal solutions cope with disturbances better, but unnecessary deviations and pro-activeness can also lead to negative effects when compared to static intermodal solutions Full article