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.@en

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.@en

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.

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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.@en

Large-scale electrification of heavy-duty road freight faces challenges including scarcity of charging infrastructure and high battery costs. Dynamic charging could help overcome these challenges by enabling trucks to charge while driving. Important additional benefits for carriers related to lower required sizes and longer lifetimes of batteries could justify the required investments. The study investigates the optimal configuration of network sections to be electrified so that the balance between costs and benefits turns out positive. A case study for a highway network spanning 4 countries in Europe suggests that dynamic charging can lead to a significant reduction in overall transport system costs, up to very large network sizes. The study supports the decision-making of policymakers and road authorities by providing new insights into the costs and benefits of dynamic charging networks, and simultaneously considering the perspectives of investors and users.

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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.

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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.

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Digital technologies are essential tools that enable traceability in supply chains. In low-income countries, traceability represents a challenge due to the complicated structure of supply chains and the involvement of multiple stakeholders. This research developed a framework for a digital traceability system (TS), using the Ethiopian coffee supply chain as a case study. A literature review was conducted to gain an in-depth understanding of state-of-the-art digital traceability technologies. A logistics audit was conducted to map the coffee supply chain in Ethiopia and evaluate the implementation level of traceability technologies. Although the implementation of traceability technologies in the Ethiopian coffee supply chain is low, the results revealed that the usage of traceability technologies improves downstream of the supply chain. The traceability framework developed in this study ranges from a paper-based TS to a fully digitalised TS. The implementation of a TS in the coffee supply chain of Ethiopia is met with several challenges such as affordability, limited awareness, resistance of certain stakeholders, infrastructure limitations, restricted accessibility of some technologies and insufficient policy frameworks. Stakeholders from low-income countries can use the framework developed in this study to adopt a TS for their supply chains in line with their needs and current digitalisation levels.

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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.@en

This chapter discusses the potential of spatial and urban planning interventions to help decarbonize freight transport activities. Against a background of continuing growth and individualization of demand, decarbonization of freight transport services is a daunting challenge. The pursuit of decarbonization can take place along different strategic lines including reduction of transport demand, a shift between modes of transport, increase of efficiency of vehicle movements, reduction of energy use of vehicles and reduction of the carbon content of energy. Instruments of collective or public freight planning that can support these strategies include land use planning for logistics activities, freight transport demand management, investments in freight transport infrastructure including ITS, and regulatory and financial policies. Based on the literature we explore the above relationships, leading to a simple conceptual framework that links the decarbonization strategies to the instruments of freight transport planning. Urgent areas of future work include the design of multi-strategy roadmaps to achieve decarbonization targets, and the assessment of the effectiveness of combined freight planning measures.

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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.

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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.

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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.

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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.@en

The Physical Internet (PI) is a paradigm-changing and technology-driven vision, which is expected to significantly impact the development of the freight transport and logistics (FTL) system of today. However, the development of the FTL system towards the PI creates much uncertainty for its current stakeholders. Ports are one of those stakeholders that are expected to be profoundly affected by these developments. However, research that focuses on port policy, under the uncertain developments towards the PI, is still lacking. By providing port authorities with insights and recommendations on robust policy areas, we address this void in literature. We conduct a scenario analysis in combination with multi-criteria decision analysis (MCDA) to determine the importance of port performance indicators and policy areas in different scenarios. The most significant, uncertain, and orthogonal factors for the development of the PI are technological development and institutional development. We find that for a proper alignment with the PI vision, in three out of four scenarios, ports should prioritize the implementation of digital solutions and standards, as opposed to an infrastructure focused policy.@en

This chapter introduces the main innovations in computing technology that affect logistics systems. It provides an exploration of how these technologies individually or collectively seep into system innovations, creating increased logistics value. The chapter presents an identification of the three concurrent development pathways for future urban freight systems, that build on these innovations. To understand the future functioning of applications of computing technologies, in relation to each other and within the entire system, they are positioned in light of the vision of the Physical Internet. Digital twins are expected to have a profound impact on decision-making about urban freight systems, both from the public and the private side. Currently, conventional decision-making processes on both sides rely on rather slow cycles of information gathering, generation of alternative solutions, evaluation of solutions, decision taking, implementation and learning. The number of cycles per unit of time, or frequency of decision-making, is determined by the duration of each stage and the quality of connections.@en

Joint decision-making is one of the coordination mechanisms to address the inherent complexity of business-to-business (B2B) processes within a supply chain. Joint decision-making can be helpful to define shared goals and objectives, identify supply chain failures and opportunities, and consolidate supply chain success. Parties may benefit directly from a partnership's potential and synergies by collaboratively making decisions. However, specific business conditions need to be in place to enable joint decision-making. This paper investigates how companies in a dyadic relationship arrive at joint and individual supply chain decision-making structure. We examine the drivers, facilitators, and barriers of making joint as well as individual decisions within the supplier-buyer dyad and frame our arguments borrowing perspectives from resource dependency theory, transaction cost economics, collaboration theory, and social exchange theory. The paper presents a case study of Dutch high-tech companies, analysing experiences of supply chain managers via semi-structured interviews. High-tech firms often collaborate and share supply chain decisions due to the high-value capital equipment as well as a shared dependency on highly specific scarce resources. Our study provides new empirical insight into how firms cope with conflicting drivers, facilitators, and barriers in collaborations, controlling their decision-making structure. From the case study, we identify the combinations of facilitators and drivers that tend to promote the existence of joint decisions. We conclude with providing a list of suggestions for decision-makers and future research.

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The choice of shipment size is a vital decision in logistics and has a strong indirect influence on freight transport demand, via the choice of mode and truck type choice. Through time, shipment sizes can change as a result of new decisions in the logistics process or due to conditions external to the supply chain. This study investigates the temporal stability of shipment size choices, relating these to the choice of truck types. It uses repeated cross-sectional data for the years 2015, 2017, and 2019 collected from cordon and business establishment surveys in Addis Ababa city, Ethiopia. The integrated choice and latent variables (ICLV) and latent growth (LG) models were used to assess the time-dependent patterns of choosing shipment sizes, both at the level of the entire freight system as well as the specific truck types. The model results reveal that shipment size decisions are temporally unstable where, in our case, shipment sizes exhibited a declining trend.@en

The fast growth of e-commerce in urban areas has led to a surge in last-mile transportation demand and an associated increase of external effects: congestion, noise and visual pollution. This paper analyses a new urban freight transport service that has a potential to reduce this footprint: crowdshipping. Crowdshipping is a service where a package is delivered via a traveller who is already making a personal trip for other purposes. The decision of whether or not to use crowdshipping is known to be subject to various service, time and price conditions, including trust in a correct delivery. The effect of trust has not been investigated explicitly, however. We conduct a stated choice experiment and estimate a hybrid choice model with trust as a situation-specific latent variable. The research design allows us to explore how the relevant attributes influence service adoption via trust. We find a significant influence of established choice attributes on service adoption, except for the delivery company’s reputation and the possibility of damage. In addition, all attributes except delivery time have a significant influence on trust. We conclude that trust has a partially mediating effect on the adoption of the service except delivery time, and a fully mediating effect on adoption via reputation and damage.@en