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.

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 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.

Digitalisation is transforming logistics operations worldwide. However, low-income countries continue to face significant barriers to adoption, including limited infrastructure and resources. In Ethiopia, supply chains remain inefficient due to inadequate technological integration. The technology acceptance model (TAM) has been used by several researchers to explain the usage and adoption of technologies. However, this framework has rarely been applied to digital logistics in the context of low-income countries. This study empirically investigated the intention of stakeholders in Ethiopian supply chains to adopt digital technologies using a modified version of TAM. Data were collected through an extensive survey of logistics professionals. The results indicated that, beyond perceived usefulness and ease of use, external factors such as infrastructure availability, human resource capacity, technological accessibility and supportive policies, significantly influence stakeholders' intention to adopt digital technologies. The study emphasises the importance of awareness-raising initiatives and the development of digital strategies to support successful digital transformation in low-income countries. These findings offer valuable insights for policymakers and practitioners seeking to better understand the relationship between technology adoption, user perceptions and enabling conditions.

This paper presents the first results of a Delphi survey aimed at eliciting experts' opinions on possible scenarios of integration between passenger and freight transport enabled by a Mobility as a Service (MaaS) platform. Combining freight shipments with passenger trips is a promising addition to the MaaS business model that could help to reduce the number of freight vehicles and contribute to a more efficient use of passenger transport services and modes. The research objective of this paper is therefore to explore the feasibility of an extended version of MaaS including freight, called “MaaS for Passenger and Freight” (MaaS4PaF). Passenger and freight transport experts were asked to express their opinions with respect to the potential market penetration, business ecosystem, and implementation of this concept. Results allow to elaborate on opportunities and barriers, especially related to uncertain business models and the multitude of actors involved, and propose a research agenda to further investigate the feasibility and potential of MaaS for passenger and freight transport integration.

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 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 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.

As the rapid growth of urban e-commerce increases the volume of last-mile deliveries, logistics service providers have difficulty in meeting the demand of on-demand consumer requests. This increase in demand challenges traditional delivery, with some parcels becoming disproportionately costly to deliver to their destinations. To address this, we introduce a cost-based outlier parcel selection mechanism that identifies parcels with a high negative impact on the marginal delivery costs. These outlier parcels are then eliminated from their tours and outsourced to a crowdshipping market, where individuals combine the delivery task with their already planned trips. We use unique data on delivery tours of six service providers for the province of South Holland in the Netherlands. The cost-based decision rule for identifying outlier parcels results in a low proportion of outsourcing to the crowdshipping market compared to earlier literature. We identify only about 1 % of the total parcel demand as outliers across all carriers combined. Of these outlier parcels, the proportion selected for crowdshipping based on their cost efficiency ranges from 42.78 % to 3 %, depending on the scenario. While crowdshipping provides a viable solution for handling a small portion of last-mile deliveries, its environmental and economic sustainability is restricted by factors such as compensation rates and the delivery mode used. This study demonstrates that outsourcing high-cost outlier parcels to crowdshipping can be cost-efficient and reduce emissions of last-mile logistics companies; however, the proportion of these parcels is very small, limiting the overall impact on sustainability.

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.

Game-theoretic models are frequently used to analyse the effects of information availability and quality on supply chain decision-making. Although information asymmetry plays a vital role in shaping sustainable supply chains, a comprehensive review of these models within this context remains lacking. This study conducts a systematic literature review and performs an in-depth content-based analysis of 73 peer-reviewed journal articles, categorising them based on their assumptions regarding supply chain structure, information structure, and interaction among supply chain members. We find that researchers are extending traditional supply chain models to address emerging challenges and opportunities driven by sustainable practices under asymmetric information. However, the research remains in a preliminary phase, with most models relying on simplified settings – typically dyadic, single-product, and single-period frameworks – and focusing primarily on demand and cost information asymmetries. Multilateral information structures and non-contractual coordination mechanisms remain largely unexplored. Theoretical advancements have considerably outpaced empirical validation, revealing a critical gap in the integration of real-world practices. Our findings highlight the importance of information sharing and coordination mechanisms in achieving sustainability outcomes and improving supply chain performance. These insights enrich the theoretical discourse on information asymmetry in sustainable supply chains.

This paper investigates the potential improvements in multimodal freight systems through the use of modular vehicles (MVs), aiming to enhance operational adaptability and integrate these vehicles within the mobility-as-a-service (MaaS) concept. The research examines a specialized form of the Pickup and Delivery Problem (PDP) adapted for rail and road logistics, presenting a new mathematical model for the Pickup and Delivery Modular Vehicle Routing Problem (PDMVRP) that includes road and rail. This model tackles significant operational issues such as routing of MVs on road, platooning and the scheduling of MVs on railways and transfers between road and rail MVs. A practical example in a regional road and railway network demonstrates the effectiveness of the proposed model, indicating their ability to decrease costs(energy and emissions), shorten travel times, and boost the efficiency of rail capacity, thereby promoting the advancement of MV usage in future transport systems.

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.

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.

This study examines the power dynamics and their impact on involvement levels and compromise complexities in B2B joint decision-making within Dutch high-tech firms. Using a five-phase framework, it investigates the asymmetrical participation of co-owning decision-makers in dyadic buyer-supplier relationships and presents a conceptual model to explain how different power sources influence business interactions. A qualitative multiple case study involving Dutch high-tech firms was conducted. The research reveals that power bases significantly determine both involvement levels and the complexities of compromises, showing a positive correlation between involvement and compromise complexity. This study provides guidance for B2B stakeholders, offering diagnostic tools for understanding power structures and strategies to enhance collaborative decision-making. Additionally, the paper recommends governance frameworks and role delineations to improve participation levels, especially for companies with less power. This scholarly work enriches the literature by clarifying the relationship between power bases, involvement levels, and compromise complexities, and extends the application of social power theory to high-tech B2B contexts.

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.

The e-commerce growth trend has great potential to influence both the nature and the dimension of the challenges policymakers will face in the near future. In this sense, the purchasing habits of the end consumer have undergone a significant change with the development of the emerging information and communication technologies that impact on the purchase behaviour and the way of receiving the item bought. The goal of the paper is thus to analyse cutting-edge last mile delivery alternatives while looking at the characteristics that determine the preferred alternative for receiving e-purchases. The identified new alternatives besides traditional mail are: crowdshipping, delivery to the car trunk, delivery by autonomous delivery robots, unattended parcel locker, attended pick up point and click-and-collect service. The paper presents the methodology for designing and delivering, as well as the findings of a survey carried out for investigating users' behaviour, after reviewing the key attributes and levels that influence end consumers in choosing how to receive their e-purchases.

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.

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.

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.