Synchromodal transport operations frequently require rapid re-planning in response to uncertain terminal service times, yet such decisions are typically made by multiple carriers with heterogeneous objectives and decentralized control. This study investigates collaborative re-planning in synchromodal transport networks under service-time uncertainty from a multi-carrier perspective. We formulate the problem as a decentralized decision-making process in which carriers must coordinate re-planning actions while preserving individual rationality. To address this challenge, we propose a model-assisted multi-agent reinforcement learning (MARL) framework that combines heuristic plan generation with learning-based carrier coordination. An adaptive large neighborhood search (ALNS) heuristic is used to generate feasible candidate re-planning options, while carriers are modeled as learning agents that decide whether to accept or reject these options through a consensus mechanism. Rather than directly optimizing a centralized model, carriers learn acceptance policies based on local observations and realized outcomes, without requiring prior knowledge of service-time distributions. An event-triggered and regret-based learning scheme is introduced to align global delay mitigation with carrier-specific cost preferences under uncertainty. Extensive computational experiments on a realistic synchromodal transport network demonstrate that the proposed framework significantly improves re-planning performance compared with benchmark strategies. The results show that decentralized, heterogeneous multi-agent coordination can outperform centralized and homogeneous approaches, particularly in scenarios with high uncertainty and avoidable delays. Mechanism and ablation analyses further reveal the structural importance of multi-agent decomposition, carrier heterogeneity, and the temporal allocation of decision preferences along the transport chain. Additional analyses examine sensitivity to key reward hyperparameters and negotiation round limits, robustness under alternative disruption distributions and network-graph perturbations, and controlled extension settings with more carriers and transshipments. The proposed approach provides a practical decision-support methodology for collaborative synchromodal transport re-planning under uncertainty.

Inventory capacity constraints represent real-world space scarcity in warehouses. When only full truckloads are permitted, these constraints are typically enforced by blocking deliveries that would violate the capacity. However, when the inventory is simultaneously too high to allow a delivery while also too low to serve all demand, this method leads to unmet demand. This study therefore introduces inventory-driven delays, where vehicles are held at the destination until enough inventory capacity is available, as a method to enforce capacity constraints. To capture these delays, the dispatch problem is formulated as a discrete-time Markov Decision Process (MDP) with inventory-dependent service times. The problem is solved using Approximate Dynamic Programming (ADP), which accounts for opportunity costs associated with waiting vehicles, allowing the policy to handle the state-dependent costs introduced by inventory-driven delays. A case study is developed based on data from an Ethiopian brewery. The proposed method is compared against multiple heuristics across four different replenishment strategies. Results show that one full truckload of unmet demand is recovered per ten cumulative days of inventory-driven delays, suggesting that allowing such delays can serve as a strategic buffer in inventory-capacitated, full-truckload systems. Future work may address dynamic order generation, parameter uncertainty, and extensions toward the general Inventory Routing Problem (IRP).

This study explores how integrating inland waterways into multimodal distribution systems can enhance city logistics, alleviate street-level congestion, and ultimately improve the livability of the urban environment. To capture the operational complexities of such systems under severe space scarcity and regulatory access constraints, we formulate a multi-trip two-echelon vehicle routing problem. The model explicitly accounts for the physical limitations of dense city centers by incorporating storage-free satellites and spatial constraints for vehicle occupancy. While the first requires precise spatiotemporal synchronization between interacting vehicles during the transshipment operation from one mode to the other, the latter bounds the maximum number of transshipments occurring at the same time at a satellite. To evaluate system performance under these conditions, we develop an optimization framework driven by an iterative decomposition-based heuristic. The approach integrates a capacitated assignment model with routing heuristics through an adaptive workload-bounding feedback loop, ensuring that downstream routing constraints actively shape upstream customer-to-satellite assignments to find a feasible solution that attains the target service level while heuristically minimizing resource consumption. The methodology is demonstrated through a large-scale case study of a multimodal distribution system in Amsterdam, serving over 750 HoReCa businesses. To derive strategic insights from this operational model, we conduct a comprehensive scenario analysis of 10560 instances. The proposed framework identifies the minimum operational resources required to guarantee service coverage by systematically evaluating diverse strategic decisions in terms of network design, transshipment modalities, workforce levels, and city access time windows. The results illustrate practical trade-offs for urban logistics planning: relaxing full-coverage service targets (e.g., to 90%) provides substantial infrastructure savings, while denser satellite networks reduce street-level travel distances and increase zero-emission walking deliveries. Furthermore, enabling parallel transshipments with an adaptive workforce allows a significantly smaller network to maintain full service coverage.

Container logistics is under increasing pressure to deliver efficient and sustainable hinterland transport. Achieving this requires improving the performance of environmentally friendly modes, such as inland waterway transport. This study examines the potential of cargo consolidation as a strategy to tackle suboptimal filling rates of containers, one of the most persistent inefficiencies in the Rhine-Alpine Corridor. We develop an integrated optimization model that holistically accounts for the operational and spatial requirements of consolidation, assessing the sensitivity of the strategy to labor costs, fuel prices, value of time, and vessel costs. The results show that, despite additional handling and coordination costs, consolidation can reduce overall transport costs by up to 4 % and attract as much as 42 % more container volumes to IWT on specific origin-destination connections. Vessel occupation rates emerge as a decisive factor in determining consolidation benefits, while cost parameters such as labor and fuel prices strongly influence outcomes. The research illustrates how optimizing this strategy can contribute to the sustainability of port-hinterland container transport and discusses the conditions required for its realization.

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.

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.

Airports and airlines are examining and committing to the electrification of Ground Support Equipment (GSE). In line with this trend, in this paper, we develop a model to simulate and optimize the GSE operations at airports. The aim is to estimate the required quantity of eGSE, the charging requirements of eGSE, the change in airport electricity requirements, and the scheduling possibilities of eGSE charging for the existing turnaround procedures. This is done by means of a Task Scheduling Problem (TSP), that is optimized using Mixed-Integer Linear Programming (MILP). A case study is performed on KLM's GSE fleet at Amsterdam Airport Schiphol. Based on this, it is concluded that daily operations can be sustained without increasing fleet size for GSE types capable of lasting a full day on a single charge, assuming vehicles can recharge overnight. This is the case at many airports due to nighttime curfews. The operational procedures used by the handler play a key role in achieving this outcome. The results confirm that the model is suitable for strategic decision-making and it is effective at the operational level. The model has the potential to lead to a more efficient use of resources in the operation.

The growing demand for parcel delivery contributes to traffic congestion, high emissions, and rising costs of freight logistics, particularly in urban areas. To address these issues, new and sustainable last-mile delivery methods must be implemented. However, estimating the impact of different logistics systems is complex, as it depends heavily on consumer adoption of these new delivery methods. This paper presents a simulation model that captures and explores the interconnections between multiple last-mile delivery methods and corresponding consumer preferences. Two key factors affecting consumer preferences are simulated: (1) consumers’ response to the performance and availability of delivery methods, and (2) the sharing of knowledge through word of mouth and familiarisation. System dynamics is applied at the aggregate level to simulate the evolution of consumer preferences for last-mile delivery across multiple methods. At the disaggregate level, an agent-based model simulates the operational performance of these delivery methods, which in turn influences consumer preferences in the system dynamics model. This integrated approach allows for the observation of the evolving interaction between urban logistics supply and demand, providing key performance indicators on consumer preferences and the delivery method operations at consecutive time points. The developed simulation model is applied to a case study in the Rotterdam-The Hague region, a highly urbanised region in The Netherlands. Results show that consumer preferences strongly depend on the carriers’ ability to fulfil the demand. The dynamic interaction between supply and demand creates a reinforcing feedback loop, where the adaptability of carriers is crucial for the long-term success of a delivery method. Additionally, the spatial results reveal that there are zonal differences in the performance of the delivery methods. Further findings indicate that, while total vehicle kilometres and CO₂ emissions will rise due to increasing parcel demand in all scenarios, the average number of van kilometres and CO₂ emissions per parcel will decrease as demand grows.

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.

In inverse optimization (IO), an expert agent solves an optimization problem parametric in an exogenous signal. From a learning perspective, the goal is to learn the expert’s cost function given a data set of signals and corresponding optimal actions. Motivated by the geometry of the IO set of consistent cost vectors, we introduce the “incenter” concept, a new notion akin to the recently proposed circumcenter concept. Discussing the geometric and robustness interpretation of the incenter cost vector, we develop corresponding tractable convex reformulations that are in contrast with the circumcenter, which we show is equivalent to an intractable optimization program. We further propose a novel loss function called augmented suboptimality loss (ASL), a relaxation of the incenter concept for problems with inconsistent data. Exploiting the structure of the ASL, we propose a novel first-order algorithm, which we name stochastic approximate mirror descent. This algorithm combines stochastic and approximate subgradient evaluations, together with mirror descent update steps, which are provably efficient for the IO problems with discrete feasible sets with high cardinality. We implement the IO approaches developed in this paper as a Python package called InvOpt. Our numerical experiments are reproducible, and the underlying source code is available as examples in the InvOpt package.

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.

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.

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.

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.

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.