The continuous growth of international container trade calls for logistics networks that seamlessly connect cross-border, domestic, and local transport services. In the design of these networks with various hubs and modes of transport, the consideration of both economies of scale for multimodal transport and congestion is essential since they can significantly impact the location of the hubs and their size. Thereby, in this paper, we study these features within a multimodal hub location problem for international trade that considers a hierarchy in the network structure. We develop a mixed-integer linear programming formulation, minimizing infrastructural, operational, and congestion costs. A hybrid adaptive variable neighborhood search algorithm with tailored operators and speed-up strategies is proposed to solve large-scale instances. Numerical experiments are conducted for China's New Western Land-Sea Corridor case and provide new managerial insights for designing hierarchical, multi-modal, cross-border logistics networks.

Very few systematic, quantitative and empirical methods exist which allow an impact assessment of urban freight solutions or policies. Especially the behavioral responses of logistic agents are difficult to include systematically in impact assessments. This is mainly due to limitations on urban freight data and absence of validated and proven simulation tools. We present an urban freight simulator: Multi-Agent Simulation System for Goods Transport (MASS-GT). MASS-GT is an agent-based model consisting of a framework of discrete choice and optimization models to describe the logistic choices of shippers, carriers, producers and consumers. The disaggregate level of detail allows the analysis of a wide variety of logistic developments and policies across all or specific logistic segments. The model was first implemented for a study area in the Netherlands and calibrated and validated using a variety of data sources.

Despite the importance of urban freight transportation for the accessibility and livability of cities, few systematic, quantitative and empirical methods exist which allow an impact assessment of urban freight transportation solutions or policies. There is a lack of transparent literature on the full specification and estimation of these models, which not only hampers continued research, but also the development of evidence-based urban freight transport policies. We present the urban freight simulator Multi-Agent Simulation System for Goods Transport (MASS-GT) with its full specification and empirical implementation for a study area in The Netherlands. It concerns an agent-based model based on a framework of discrete choice and optimization models, which describes logistic choices of shippers, carriers, producers and consumers. The disaggregate level of detail allows the analysis of a wide variety of logistic developments and policies across all or specific logistic segments. The model is estimated and validated using a variety of data sources: truck trip diaries, supply/use statistics, an e-commerce demand survey, traffic counts and other relevant statistics. The article presents the full specifications of the model and their empirical estimation, including the data sources used. Also, the validity of the model is evaluated using road freight traffic counts. Finally, examples of applications of the model to case studies are provided.

The transport sector is the second highest emitting sector in the Netherlands, with 19 % of the total energy consumption. Where other sectors are becoming more sustainable, the transport sector remains one of the most difficult to decarbonize, despite the many studies in this field. This research aims to gather a list of potential measures and combine them to reach an 80% reduction of CO2 emissions in 2040 for the hinterland freight corridor of Rotterdam – Venlo which has a high rail freight intensity. Literature study and expert interviews established a list of potential measures. Using an iterative normative scenario design approach, a list of 8 potential measures with corresponding targets was drafted. After computation, a reduction of 80% is estimated for the corridor. The results show that for this particular corridor, the reduction depends greatly on the availability and adaptability of biodiesels and the electrification of vehicles and vessels. In contrast, measures aiming to improve the logistical
efficiency do reduce the total energy required and could therefore alleviate challenges related to biodiesel availability and electrification, but have a smaller potential in reducing CO2 emissions.

The demand for new offshore wind farms is increasing at a rapid pace, and the installation rate must be quadrupled by 2030 to meet the ambitions of European countries. The installation of the superstructures involves several components and is highly weather-dependent, making this an important bottleneck. In this paper, we evaluate the two main strategies for the installation of superstructures: feedering and shuttling. With feedering, the installation vessel is fed with components by feeder vessels directly from manufacturing ports. With shuttling, the installation vessel retrieves the components itself from a marshalling port. In contrast to existing studies, we include manufacturing ports and their production rate to have a better understanding of their influence on the installation rate and develop a rolling horizon optimization-simulation framework composed of a mixed integer linear programming model and a Markov simulation model for weather forecasting. A heuristic is proposed to solve the model to overcome the limitation of commercial solvers. Results indicate that accurate initial buffer calculations, depending on the production rate at the manufacturing ports and project-dependent characteristics, can increase the installation rate significantly for both strategies. Finally, feedering outperforms shuttling in most scenarios and is less weather dependent.

Dry port construction facilitates intermodal freight transport in the import and export corridors, especially for landlocked countries. Selecting the optimal locations for dry ports is a crucial component of national planning. In this study, multi-criteria decision-making (MCDM) combined with GIS was used to map suitable sites for dry ports. Essential criteria for selecting optimum dry port locations were identified from the literature and a Simple Multi-Attribute Ranking Technique (SMART) was used for expert weighting these criteria. The results revealed that distance from road and distance from railway are the two most important criteria, while distance from a seaport is the least important. Application of the method to identify optimal dry port locations in Ethiopia showed that most of its territory is moderately suitable for dry port location. However, most of the existing dry ports in the region are found to lie within the highly suitable areas. Overall, the suitability map developed in this study provides a rich basis of information for future sustainable dry port investments.

De transitie naar een circulaire economie vraagt om intensieve samenwerking binnen waardeketens. Dit artikel bouwt op concepten voor de analyse van logistieke systemen zoals ge¨ıntroduceerd door prof. Kees Ruijgrok, en verbindt deze aan recent onderzoek naar Value Chain Collaboration en circulaire prestaties. Met behulp van kwalitatieve data, verzameld via semi-gestructureerde interviews, en kwantitatieve data, verkregen uit twee opeenvolgende vragenlijsten, is de relatie tussen samenwerking en prestaties op de Rladder van de circulaire economie nader onderzocht. Samenwerking blijkt sterk te correleren met circulaire prestaties, met name in technologie-intensieve sectoren die profiteren van gedeelde platforms en infrastructuur. Traditionele sectoren lijken te worden belemmerd door een gebrek aan vertrouwen en beperkte middelen. Deze resultaten benadrukken de noodzaak van maatwerkoplossingen om unieke uitdagingen en kansen binnen sectoren te benutten. Het artikel beoogt bij te dragen aan de literatuur over circulaire economie en samenwerking in de logistiek, als basis voor toekomstig onderzoek en praktijktoepassingen

The Internet of Things (IoT) can bring radical advancements in the domain of waste collection, as it enables the organization of demand-responsive schedules which leads to higher efficiency operations. One major challenge in the deployment of demand-responsive schedules, nevertheless, is the uncertainty they bring in the planning of resources as they follow the daily waste demand. This is undesirable in real-life operations as it makes it difficult to reserve resources and ensure the stability of operational processes. Therefore, waste collection scheduling approaches need to be devised that are not only demand-responsive but also supply-friendly. In this paper, we present a solution approach for the waste collection vehicle routing problem in an IoT context (IoT-WCVRP) that focuses on these requirements. We demonstrate its applicability through a case study of Rotterdam in The Netherlands, where real-life household waste data are used and the observed waste collection operations in the city are compared against the optimized outcomes of the model. The application results show that our IoTWCVRP approach achieves the stated demand and supply trade-off, increases the vehicle utilization rates by 5%, and reduces emissions and travelled kilometres by 6% and 8% respectively.

As ports are experiencing heavier traffic, the pressure to improve port call processes is increasing. Port call optimization (PCO) is one of these improvement initiatives, enabling the arrival of vessels to the port just-in-time when the vessel services, like pilotage, towage, and mooring, are all readily available. Otherwise, vessels that sailed at full speed to arrive at the port may have to wait, idling at anchorage, occupying space, burning fuel, and leading to increased congestion. One of the main challenges in the implementation of PCO is determining the time at which availability of these services can be guaranteed. The paper addresses this challenge by presenting a model that jointly schedules vessels and service providers. It extends the current approaches to allow application to larger and busier ports, where repositioning times for pilots and tugboats is highly variable and vessels experience waiting times between services. The problem is formulated as a mixed-integer linear programming one and is modelled in continuous time. We test alternative scheduling strategies using three different objective functions, based on the current ‘first-come-first-serve’ approach, a minimal level of service, and the best capacity utilization. The model is applied on data made available by the Port of Rotterdam, and it provides a full-service schedule for vessels and service providers.

The energy transition is reshaping global trade flows, introducing uncertainty into energy-related liquid bulk logistics, particularly within ports. Despite the significance of liquid bulk in global supply chains, existing port choice models are primarily developed for containerized cargo. This study investigates the key determinants of decision-making in liquid bulk logistics within the context of sustainability transitions. Using a multi-case study approach, including stakeholder interviews and qualitative analysis, we identify factors shaping infrastructure planning, port selection, storage, and transportation modes.

The findings reveal that logistical choices in liquid bulk are highly interdependent, shaped by supply chain structures, stakeholder dynamics, and regulatory frameworks. As ports might evolve into multienergy hubs, new actors and uncertainties emerge, particularly regarding the role of hydrogen and biofuels. Understanding these evolving dynamics is crucial for optimizing logistics strategies and ensuring efficient, sustainable energy supply chains.

Logistics plays a crucial role in modern society, particularly in densely populated urban areas, facilitating the transportation of goods. Last-mile e-commerce deliveries are emissions-intensive, contributing significantly to CO2 levels and traffic congestion. Addressing this challenge requires systemic changes in last-mile delivery ecosystems. Based on this observation, in alignment with the EU decarbonisation goals, the URBANE project (GA101069782) aims to promote the adoption of sustainable and environmentally friendly last-mile delivery solutions by introducing a collaborative layered “Platform as a Service” (PaaS) paradigm. The initiative focuses on establishing Physical Internet (PI) inspired interventions combined with the implementation of innovative tools, such as agent-based and AI models, employing a Digital Twin platform addressing the operational and strategic planning challenges of city logistics networks. A multi-factorial impact assessment radar further enhances the evaluation of the PI interventions’ effectiveness. The platform fosters collaboration among urban logistics stakeholders governed through “green” smart contracts, addressing security and privacy concerns by using a blockchain infrastructure and digital IDs, creating a trustworthy system for collaboration. The paper showcases the applicability of the URBANE Innovation Transferability Platform in designing, measuring, testing, and validating targeted logistics interventions in Lighthouse Living Labs. Cities and logistic operators receive suggestions for informed data-driven decision-making coupled with integrated and transferable applications that can be standardised and structured, aligned with the targets set in a citie’s Sustainable Urban Logistics Plan (SULP).

The emergence of Intelligent Transportation Systems (ITS) for freight transport in recent times has created interest among practitioners and researchers to extend freight ITS to support broader logistics processes, including dynamic tour scheduling, loading and unloading, warehousing, and even production. However, connecting transport data, ITS and logistics information systems require collaboration between different organizations and new business models to create business value for logistics actors. It is critical for these stakeholders to consider how their business models connect to create meaningful new data-to-information value chains. This study develops a conceptual framework to identify opportunities for logistics value creation with freight transport data. Building on the literature we construct a framework that reconciles multi-firm and firm-level business modelling. The main component is a generalized framework for Data-to-Value (DtV) chains for applications in information and communications technology. In order to support its business validity, we extend this framework with Business Model Canvases (BMCs) of the actors in the value chain. Three real-life use cases from a freight ITS community in the Netherlands are used to evaluate and illustrate the framework.

Freight transport decarbonization is challenging due to the slow implementation of policies to meet climate goals. This paper analyzes the dynamics of the implementation of freight decarbonization measures. A System Dynamics model was developed and applied to the Brazilian freight system to simulate the use of more sustainable modes and means of transport, including electrification, increased use of biofuels, acceleration of fleet renewal, and modal shift. Significant emission reductions are found in the scenarios combining a shift to alternative modes and a rapid phase-out of diesel vehicles. Even so, the Brazilian freight sector's emission budgets towards limiting global warming to 1.5 °C and 2 °C will be depleted during the current and next decade, respectively. An absolute reduction of carbon emissions before 2050 seems unlikely. Besides confirming the need to study the dynamics of the freight system, the findings corroborate the urgency for stronger actions on freight decarbonization.

The rapid increase in e-commerce and the emergence of combined passenger/freight systems in urban areas have raised the question of how best to integrate public transport services into door-to-door deliveries. This paper develops a variant of the pickup and delivery problem, called the two-echelon pickup and delivery problem using public transport (2E-PDP-PT). In the 2E-PDP-PT, the transportation network is split into two echelons. Different vehicles are utilized across the first and second echelons to ensure distribution efficiency. Parcels are delivered by public transport with free capacity or via trucks between satellites in the first echelon, and logistics vehicles are operated in the second echelon. The satellites are located at the echelon borders to transfer commodities between echelons. The 2E-PDP-PT aims to minimize total delivery costs and improve public transport capacity utilization. We formulate a new mathematical model based on a space-time network and adopt an adaptive large neighborhood search (ALNS) algorithm for the 2E-PDP-PT. The effectiveness of the ALNS algorithm is validated using newly generated small-scale instances. Furthermore, we investigate large-scale instances based on the Beijing Yizhuang transportation network. The computations show that an average total delivery cost savings of 4.5% is feasible. In addition, we analyze the impact of demand distributions and compare the ALNS algorithm and the LNS algorithm. Finally, we conclude that dynamically integrating public transport into freight transport services can benefit both logistics companies and public transport operators.

City logistics simulation can help to provide empirical proof of potential benefits of new solutions in city logistics but decision support tools for such analyses are scarce because of a lack of empirical data and resources. The Tactical Freight Simulator (TFS) is a multi-agent simulator that represents the decision-making of freight agents and individual freight shipments. In this study it is applied to four distinct use cases in city logistics: micro hubs, introduction of zero-emission zones, crowd-shipping and the land use planning of logistic facilities. The simulations show impacts of each development and provide learnings: the type of open or single carrier operation of micro hubs have big local impacts. The impact of freight traffic avoiding zero emission zones can have substantial local impacts. Depending on the configuration of the service, crowd-shipping can lead to more vehicle kilometres. A common finding is the impact of the chosen scenario parameters on the outputs: regulation is important to shape city logistics operations. This also illustrates that, although the technology seems to be ready for innovative solutions, the logistical organisation or business models and policies are not yet well developed.

The considerable increase in parcel deliveries has negatively impacted the accessibility and livability of cities. One solution strategy is to decouple short-distance from long-distance shipping so that last-mile transport can be performed with low-footprint vehicles. Such solutions are referred to in the literature as multi-echelon distribution systems. This study introduced a new variant of the two-echelon vehicle routing problem that considers multiple alternative transport modes as well as multiple commodities over a multi-period time horizon, where customers can obtain their commodities from any store. We referred to this problem as the two-echelon multi-commodity multimodal vehicle routing problem (MCM-2E-VRP). The objective of service providers is to minimize total generalized costs while satisfying customer requirements. We formulated this as a mathematical model based on a space–time network and introduced a random utility discrete choice model to capture variations in performance and preferences. We developed an adaptive large-neighborhood search (ALNS) algorithm to provide solutions for newly generated MCM-2E-VRP instances based on the Beijing Yizhuang transportation network. Extensive numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. A sensitivity analysis revealed some policy-relevant findings regarding the effects of store distribution and vehicle capacity.

Micro-hubs are considered to be a potential solution to increase the consolidation of inner-city deliveries: in the City of Rotterdam it is a potential measure to increase the logistic efficiency in and around the planned zero-emission zone in the city center. When designing the configuration of micro-hubs in an urban setting multiple aspects should be considered, such as their location, the type of vehicles to operate them, and the business model to be adopted for their operation. And although the topic is much studied it remains difficult to predict how different micro-hub configurations affect the transportation system in terms of transport movements, number of travelled kilometers, etc. This paper describes the use of the Tactical Freight Simulator (TFS) to investigate the impact of micro-hubs on the transportation system in case they would be implemented at a wider scale across the city center, and make a comparison with the current state of last-mile delivery. The case study explores three different design aspects: location, type of vehicles (delivery robots, cargo bike, LEV), and the business model (individual/full collaboration). Results show that the largest reduction of vehicle kilometers can be achieved in the scenarios with full collaboration between the CEPs.

In the contemporary fast-evolving urban freight landscape, policymakers, planners and freight operators are confronted with increasing complexity and reduced time to act in an informed way. Traditionally, models and ex-post data analytics were the key to provide information, but demand has grown for interactive and flexible models. Digital Twins (DTs) are widely seen as an important part of the solution. The chapter describes the recent evolution of information provision in urban freight transport, paints an outlook for the future of DTs and discusses necessary conditions for their realisation. DTs provide value by visualising the current urban system and predicting future states in function of relevant external influences and actions from stakeholders. As a relatively recent requirement, they should be able to reflect the different interactions between stakeholders including the temporal scales of decision making in logistics, and its impacts. However, to be able to forecast future states, such tools need to be built with the participation of their potential users that is, all stakeholders involved in urban logistics. In the case of complex tools like DTs, the process of development is as important as prediction capabilities, to make sure they accurately reflect the trade-offs present in decision-making processes.

Electric Road Systems offer dynamic charging infrastructure for electric trucks, by means of overhead lines or rails that conduct electricity, or through induction loops in the pavement. Charging trucks while they are moving has important advantages over stationary charging, as batteries can be smaller and drivers and cargo do not have to wait while recharging. There are significant knowledge gaps about optimal network sizes, impacts on different stakeholders and cost/benefits of these technologies. This paper summarizes 4 MSc thesis projects on these topics by students of TU Delft. Their findings support the growing insight that ERS could make the electric truck landscape more efficient than if based on stationary chargers alone.