Disruptions and uncertainties can significantly reduce the efficiency of conventional intermodal transport, often leading to severe economic losses and deterioration in service levels. To mitigate the negative impacts of disruptions on the shipments, our research leverages the flexibility of synchromodality and develops a learning-based modular framework for disruption management. By utilizing a hybrid simulation-optimization modeling approach, the framework effectively captures disruptions and generates dynamic response strategies. Through the integration of Reinforcement Learning (RL), the proposed approach re-plans under disruptions, accounting for their stochastic characteristics, enabling swift and effective decision-making in real-time scenarios. Results are compared against two policies, always wait and always reassign, highlighting the superior performance of the RL approach, when exposed to a certain disruption profile, with comparable or better decisions compared to other policies in response to disruptions. Additionally, results are compared against a benchmark policy to test an alternative reward mechanism, demonstrating that integrating a cost-based reward mechanism increases its resilience and results in lower costs, especially in the case of more frequent and low to moderately severe disruptions.

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.

Dynamic multimodal transport planning is vital for enhancing flexibility and responsiveness in emergency logistics. We propose a dynamic planning approach that integrates drones into the multimodal system with trains, trucks, and aircraft, introducing dual-role drones that can be transported as cargo and later operate as carriers. A Mixed Integer Programming (MIP) model, optimized via a rolling horizon approach, supports real-time route planning. Given the problem’s complexity, we develop an Adaptive Large Neighborhood Search (ALNS) algorithm with problem-specific operators. The model accounts for mode coordination, routing constraints, and cargo heterogeneity. It dynamically replans routes under disruptions such as road damage, considering mode availability and delivery requirements. Numerical experiments are conducted based on a real disaster scenario. A comparison with an exact method shows improved computational efficiency and solution quality. Further comparisons with a drone-free and static approach highlight gains in service rate and disruption resilience. We also examine the impact of cargo heterogeneity. These results, across instances from 5 to 400 orders, provide practical insights for optimizing drone deployment, transport mode selection, and cargo management in disaster response.

The shift from private vehicles to public and shared transport is crucial to reducing emissions and meeting climate targets. Consequently, there is an urgent need to develop a multi-modal transport trip planning approach that integrates public transport and shared mobility solutions, offering viable alternatives to private vehicle use. To this end, we propose a preference-based optimization framework for multi-modal trip planning with public transport, ride-pooling services, and shared micro-mobility fleets. We introduce a mixed-integer programming model that incorporates preferences into the objective function of the mathematical model. We present a meta-heuristic framework that incorporates a customized Adaptive Large Neighborhood Search algorithm and other tailored algorithms, to effectively manage dynamic requests through a rolling horizon approach. Numerical experiments are conducted using real transport network data in a suburban area of Rotterdam The Netherlands Model application results demonstrate that the proposed algorithm can efficiently obtain near-optimal solutions. Managerial insights are gained from comprehensive experiments that consider various passenger segments, costs of micro-mobility vehicles, and availability fluctuation of shared mobility.

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.

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.

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.

Freight transport faces a threefold challenge of limited resources, increasing demand for efficient goods movement, and the pressing need to meet ambitious emissions reduction targets in ever shorter timelines. To address these challenges, the industry requires urgent innovation and the adoption of new technologies and logistics systems to change the way goods are transported. The use of intermodal transport has been developed due to the need for efficient, cost-effective, and sustainable freight transport. However, the current state of intermodal transport still faces various barriers to its utilization, such as a lack of flexibility, delays caused by uncertainty, and a lack of cooperation among transport actors. The proposal of synchromodal transport aims to address these barriers. Synchromodal transport represents an advanced form of intermodal transport that dynamically adapts routes and modes while optimizing resource utilization through synchronization and collaboration. Despite the recognition of synchromodal transport as a promising solution, there are still unaddressed gaps in the transport planning field, including the need for flexible, dynamic, preference-based, and collaborative planning. This thesis aims to fill these gaps through the development and evaluation of a series of innovative approaches, which are tested and validated using real-world transport networks. The goal is to advance the field of synchromodal transport planning, enabling the provision of flexible, reliable, and sustainable services that meet the needs of stakeholders. In order to investigate the potential of flexibility, this thesis presents a mathematical model and a heuristic algorithm (Adaptive Large Neighborhood Search, ALNS) for the simultaneous routing of shipments and vehicles. The proposed approach enables flexible routing and scheduling of vehicles, improving the overall efficiency of the transport system in a static setting as a proof of concept. The results of numerical experiments demonstrate that implementing the proposed approach with flexible services can result in 14% reduction in costs compared to existing methods that do not consider flexibility. In dynamic planning, this thesis tackles the issue of service time uncertainty in synchromodal transport by using an online Reinforcement Learning (RL) approach, assisted by the ALNS algorithm. The proposed model-assisted RL integrates RL and ALNS to leverage the data-driven strengths of RL and the domain knowledge of ALNS. In this way, the model-assisted RL addresses the "curse of dimensionality" caused by the large state space and complex actions in synchromodal transport. The RL approach dynamically adapts to unexpected events that cause uncertainty by learning from real-time data collected from transport operators, terminal operators, and sensors, without requiring any prior information. The proposed approach was tested in various scenarios that included disturbances, disruptions, and a combination of different types of events, and was found to perform better than traditional waiting and average duration strategies in reducing delay, waiting time, cost, and emissions. When it comes to preference-based planning, this thesis addresses the challenge of incorporating the heterogeneous and vague preferences of shippers and carriers. To account for carriers' preferences, a multi-objective optimization model that incorporates weight intervals is proposed to handle vague preferences. The model generates a Pareto frontier of solutions that best reflects the carriers' preferences, allowing them to make informed decisions. For shippers' preferences, the thesis employs multiple attribute decision-making and fuzzy set theory to address the heterogeneity and vagueness of preferences, respectively. The results demonstrate that incorporating preferences results in improved satisfaction among shippers by providing solutions with preferred attributes on cost, time, emissions, risk, and delay. By improving shipper satisfaction, carriers can benefit from increased customer loyalty and retention, leading to a competitive advantage in the market. Moreover, by considering various attributes, such as cost, time, emissions, risk, and delay, the model can help carriers make more informed and sustainable decisions, leading to improved environmental performance and compliance with regulations. Overall, incorporating preferences in planning can result in a win-win situation for both shippers and carriers, leading to improved operational performance and a sustainable competitive advantage. In collaborative planning, this thesis examines the benefits of horizontal collaboration among carriers through the sharing of requests and the consideration of eco-labels. The thesis presents an auction-based mechanism to facilitate collaboration and enable distributed planning. Results indicate that this approach leads to increased request fulfillment, improved sustainability, and reduced costs compared to centralized and non-collaborative planning approaches. On the tested instances, the collaboration between carriers can result in significant increases in the proportion of served requests, with gains of 48% and 11% for synchromodal and unimodal carriers, respectively. Additionally, by taking into account eco-label preferences, the use of the highest or mixed eco-labels can lead to emissions reductions of up to 70% and 15%, respectively, compared to ignoring preferences. Compared to synchromodal carriers, unimodal carriers, especially truck carriers, need to share more requests in collaborative planning to reduce the overall cost. From a policy-making perspective, policymakers can take steps to promote the development of synchromodal transport by implementing incentives for collaborative planning and utilizing eco-labels to achieve sustainable synchromodal transport solutions. In summary, this thesis provides solutions to address the gaps in synchromodal transport planning by proposing innovative mathematical models and algorithms. These methodologies aim to increase the flexibility, reliability, and sustainability of transport services while also reducing cost, time, emissions, and delay. Additionally, the proposed methodologies consider the preferences of both shippers and carriers, promoting a collaborative and eco-friendly approach to transport planning. The numerical experiments and case studies demonstrate the effectiveness and superiority of the proposed approaches compared to existing methodologies.

Decision-makers in synchromodal transport (ST) have different preferences toward different objectives, such as cost, time, and emissions. To solve the conflicts among objectives and obtain preferred solutions, a preference-based multi-objective optimization model is developed. In ST, containers need to be transferred across modes, therefore the optimization problem is formulated as a pickup and delivery problem with transshipment. The preferences of decision-makers are usually expressed in linguistic terms, so weight intervals, that is, minimum and maximum weights, are assigned to objectives to represent such vague preferences. An adaptive large neighborhood search is developed and used to obtain non-dominated solutions to construct the Pareto frontier. Moreover, synchronization is an important feature of ST and it makes available resources fully utilized. Therefore, four synchronization cases are identified and studied to make outgoing vehicles cooperate with changes of incoming vehicles’ schedules at transshipment terminals. Case studies in the Rhine-Alpine corridor are designed and the results show that the proposed approach provides non-dominated solutions which are in line with preferences. Moreover, the mode share under different preferences is analyzed, which signals that different sustainability policies in transportation will influence the mode share.

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.

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.

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.

In intermodal transport, a freight forwarder usually serves multiple shippers with heterogeneous preferences, such as faster transport, more reliable transport, and cheaper transport. Ignoring shippers' preferences will negatively impact the satisfaction of shippers and lead to the loss of them in a longer run. Therefore, an optimization model considering shippers' heterogeneous preferences is proposed for improving the service level of freight forwarders.

Inland waterway transport is becoming attractive due to its minimum environmental impact in comparison with other transportation modes. Fixed timetables and routes are adopted by most barge operators, avoiding the full utilization of the available resources. Therefore a flexible model is adopted to reduce the transportation cost and environmental impacts. This paper regards the route optimization of barges as a pickup and delivery problem (PDP). A Mixed Integer Programming (MIP) model is proposed to formulate the PDP with transshipment of barges, and an Adaptive Large Neighborhood Search (ALNS) is developed to solve the problem efficiently. The approach is evaluated based on a case study in the Rhine Alpine corridor and it is shown that ALNS is able to find good solutions in reasonable computation times. The results show that the cost is lower when there is more flexibility. Moreover, the cost comparison shows that transshipment terminals can reduce the cost for barge companies.