The Physical Internet (PI) paradigm, which has gained attention in research and academia in recent years, leverages advanced logistics and interconnected networks to revolutionise the way goods are transported and delivered, thereby enhancing efficiency, reducing costs and delays, and minimising environmental impact. Within this system, PI-hubs function similarly to cross-docks, enabling the splitting of PI-containers into smaller modules for delivery through a network of interconnected hubs. This allows dynamic routing optimisation and efficient consolidation of PI-containers. However, the impact of system parameters and relevant uncertainties on the performance of this innovative logistics framework is still unclear. For this reason, this work proposes a robustness analysis to understand how the PI logistics framework is affected by the handling, consolidation, and processing of PI-containers at PI-hubs. To this end, the considered PI logistics system is represented via a mathematical programming model that determines the best allocation of PI-containers in an intermodal setting with different transportation modes. In doing so, four Key Performance Indicators (KPIs) are separately considered to investigate different aspects of the PI system's performance, and the relevant robustness is assessed with respect to the PI-hub processing times and the number of modules per PI-container. In particular, a Global Sensitivity Analysis (GSA) is performed to evaluate, through a case study, the individual relevance of each input parameter on the resulting performance.

The railway scheduling problem concerns the determination of trains' scheduled departure and arrival times at stops, and the allocation of capacity in the network. The timetable must be both conflict-free given infrastructure constraints, and stable enough for trains to recover from delays that could occur in normal operations. Existing methods for tactical scheduling contain a tradeoff between having an accurate (microscopic) representation of signalling constraints, and having a simple-enough (macroscopic) infrastructure representation to scale to real-world problem instances. This creates issues for infrastructure managers looking to run more trains on their infrastructure by migrating to Distance-To-Go (DTG) signalling systems (e.g. ETCS Level 2), and to exploit the capabilities of Connected Driver Advisory Systems (C-DAS) and Automatic Train Operation (ATO) to control trains more precisely. In this paper, we present a methodology for incorporating the capabilities of DTG signalling in conjunction with C-DAS and ATO systems into a disjunctive scheduling model for both periodic and nonperiodic instances. We show that the resulting model has both a microscopic infrastructure representation, and a macroscopic computational complexity, allowing railways to quickly compute conflict-free and stable timetables for large problem instances. The resulting model also accurately represents the computation of the brake indication point for both conventional and DTG signalling as a function of the trains' current speed. Tests on a large-scale periodic scheduling instance in the UK show that the model produces timetables with reasonable computation time.

Amid the growing need for more sustainable freight transport, the rail sector faces challenges in competing with road transport due to lower flexibility and reliability. To address these challenges, intermodal rail freight systems must evolve to enhance flexibility and integrate more seamlessly with broader mobility services. A key factor in the success of such innovations is the integration with existing railway infrastructure. This study examines the concept of modular vehicles (MVs) in rail systems, focusing on the innovative 'Pod' system developed in the Pods4Rail project. These Pods consist of detachable flat wagons (carriers) and capsules or containers (transport units) capable of moving both goods and passengers. A fundamental requirement for MVs is the assignment of carriers to transport units, allowing them to operate on the rail network. This involves , ensuring the availability of carriers at pickup points in terms of assignments of carriers to transport units as well as relocating empty carriers across the network. This may lead to empty runs and potential capacity challenges. Once assigned, complete Pod formation (combinations of carriers and transport units) form swarms or platoons through virtual coupling, which could significantly improve capacity utilization. This paper presents a heuristic framework that incorporates the assignment of carriers to transport units, and pod dispatching for pickup and delivery, including empty carrier circulation, considering parameters for the operation in virtual coupling. Results indicate that platooning reduces makespan for larger problem sizes, but requires a sufficient number of carriers.

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.

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.

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.

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.

This paper explores the enhancement of rail-based, intermodal freight transport systems through the integration of modular vehicles (MVs), aiming to boost the flexibility of this form of transport and its integration into the mobility-as-a-service framework. This study specifically addresses a specific variant of the Pickup and Delivery Problem (PDP) tailored to railway logistics, by developing mathematical models for the Pickup and Delivery Modular Vehicle Routing Problem (PDMVRP) with platooning. This model focuses on the operational challenges associated with routing and platooning of MVs on the railways. A case study conducted within a railway context assesses the efficacy of our model, demonstrating its potential to reduce transportation costs and improve railway capacity utilization, thereby advancing modular vehicle operations in railway environments.

Under the pressing need for increasing sustainability of freight transport, the rail sector faces challenges in competing with its road counterpart which can be contributed to lower flexibility and reliability. To overcome these challenges, intermodal rail freight systems should undergo a transformative evolution to enhance flexibility and seamlessly integrate with broader mobility services. Crucial to the success of any such innovative solution is integration with the existing railway infrastructure. This study explores the concept of modular vehicles (MV s) in rail systems, focusing particularly on the innovative 'Pod' system introduced in the Pods4Rail project. It introduces a framework for Pods scheduling on the railway network, incorporating an overlap-level-based platooning. Experiments show that implementing this system can significantly reduce the makespan and optimize railway capacity utilization, especially when dealing with larger problem sizes.

Climate change stresses the need for research and development of innovative sustainable mobility solutions that provide reliable and convenient door-to-door services for both passengers and freight. The increase in urban population and the popularity of e-commerce further highlights the need for action. In this regard, crowd-shipping is often perceived as an efficient, cost-effective, and sustainable alternative (or complement) to the management of urban freight mobility through efficient utilization of current transportation capacities. In this framework, inspired by the concept of MaaS (Mobility as a Service) in integrating various forms of transport and transport-related services into a single on-demand mobility service, this paper proposes a car-sharing-based service for the combined mobility of passengers and freight. In doing so, one-way car-sharing and crowd-shipping concepts are integrated in order to serve part of the existing freight demand in a sustainable and cost-efficient way for users, societies, and the environment. An optimization model is proposed to optimally plan the activation of one-way car-sharing and crowd-shipping services and to determine the optimal number of vehicles to assign to them. Such decisions are aimed at minimizing the total imbalance by serving passenger and freight demand during different time periods. In doing so, the willingness of users to carry freight in their vehicles is also taken into consideration. The capability of the proposed approach is evaluated through representative numerical examples aimed at showing the impact of the model parameters on the solution.

This paper addresses the problem of optimizing the transport of goods in the Physical Internet (PI) framework in a multi-modal setting using a multi-objective mixed-integer linear programming (MILP) approach. The model is specifically designed to meet the requirements related to modular shipments and PI-hubs, and in particular, determines the allocation of modular shipments to each transport mode in an intermodal setting. In doing so, parallel direct connection via road, the delivery times and the transportation costs are minimized. The model is applied to a numerical case study, to test its effectiveness to enhance freight transport efficiency within the PI framework, by exploiting, in particular, all the capacities of the available vehicles. In addition, a sensitivity analysis is conducted on some model parameters, to test its reaction to changes in the supply system and in the objective priorities. Results show that all the shipments are effectively transported between the origin and the destination terminals, they are divided into modules when necessary, and the selected transport modes, allocation strategy, and delivery times vary accordingly to the objective priorities.

This special issue of the Journal of Supply Chain Management Science (JSCMS) presents four papers that were discussed at the 9th International Physical Internet Conference (IPIC) in 2023. The vision of Physical Internet (PI) has gone through a strong maturing process since its conception (Montreuil, 2011), both in terms of its practical articulation and scientific grounding. Recent reviews of the PI-literature (Treiblmaier et al., 2020; Pan et al., 2021; Chen et al., 2022; Nguyen et al., 2022; Cortés-Murcia et al., 2022) explicitly link the PI system concepts to its founding disciplines: industrial engineering (IE) and supply chain management (SCM). Together these PI innovation agendas have helped to involve IE and SCM researchers outside the PI community and have promoted the bundling of global R&D efforts into converging streams of work. In the light of these developments, JSCMS has offered to consider papers presented at IPIC 2023. The four selected and peer-reviewed contributions included in this special issue all revolve around the organization of high levels of connectivity between network services of individual carriers, leading to collaborative deployment of services in an integrated network. In the PI-jargon, this high-level connectivity is termed “hyperconnectivity”.