EURO Journal on Transportation and Logistics最新文献

Cities around the globe face major last mile delivery (LMD) challenges as a result of surging online commerce activity, increased parcel delivery demands, lack of parking capacity, and severe traffic congestion particularly in inner city areas. Although a number of innovative solutions and pathways have been proposed to address these challenges, their full impacts are still not well understood due to the lack of comprehensive field studies and real-world data on their performance. Traffic simulation techniques, which are widely used to model and evaluate the impacts of a wide range of urban mobility interventions, offer opportunities to investigate the impacts of city logistics interventions. However, research evidence on this topic remains fragmented, hindering constructive analysis of the role of traffic simulation and modelling in shaping sustainable urban delivery interventions. Given the growing research interest in the nexus of traffic simulation and city logistics, this paper aims to identify and evaluate primary studies related to the simulation forms, evolution, effectiveness, and their feasibility in evaluating the impacts of urban delivery interventions. The paper achieves this through a holistic systematic review that consolidates diverse perspectives on the topic. The paper analysed 79 articles, published by December 2023, that have used traffic simulation to model and evaluate a wide range of city logistics interventions. The review identified five main themes in the literature surrounding development and applications of traffic simulation, namely their use in evaluating the role of information and communication technologies, evaluations of advances in types and forms of delivery vehicles, their application in supporting policy interventions, evaluations of innovations in city logistics facilities and impacts of the collaborative economy. The review also helped in gaining insights about urban delivery interventions and their impacts as stand-alone or in combination with other solutions. The analysis confirms the validity and versatility of traffic simulation as an approach to model the impacts of urban delivery interventions on traffic flow, delivery service and customers, but also the sustainability implications of interventions. The paper also provides a future research agenda to guide future studies in developing and evaluating applications of various interventions with the leverage of traffic simulation.

Crowdshipping is a new delivery paradigm that exploits the capacity of ordinary people who offer their own vehicles and free time to perform deliveries against compensation. In this work, we consider a peer-to-peer logistic platform where a company receives orders from its customers and assigns them to occasional drivers (ODs), or crowdshippers, who perform the delivery operations. We first investigate the problem of deciding how the orders should be partitioned into bundles, where a bundle is a set of orders assigned to the same OD. Then, we focus on the problem of determining the compensation associated with each bundle, with the purpose of minimizing the total delivery costs. The pricing scheme is based on the assumption that each OD is associated with a willingness-to-serve function, which is modeled as a random variable that gives the probability that the OD accepts to deliver the bundle given the compensation value. This random variable captures the estimation of the willingness-to-serve function that the company has elaborated, for example on the basis of historical data. If the compensation offered by the company is greater than or equal to the willingness-to-serve value, the OD performs the delivery, otherwise she/he refuses. In case no OD is available to deliver a bundle, then all packages in the bundle are offered to a third-party delivery company. We simulate two auction systems for the assignment of bundles to ODs: a static and a dynamic auction. In exhaustive simulation tests, we compare different pricing schemes as well as the two auction systems, and outline several managerial insights.

Autonomous trucks are expected to fundamentally transform the freight transportation industry. In particular, Autonomous Transfer Hub Networks (ATHNs), which combine autonomous trucks on middle miles with human-driven trucks on the first and last miles, are seen as the most likely deployment pathway for this technology. This paper presents a framework to optimize ATHN operations and evaluate the benefits of autonomous trucking. By exploiting the problem structure, this paper introduces a flow-based optimization model for this purpose that can be solved by blackbox solvers in a matter of hours. The resulting framework is easy to apply and enables the data-driven analysis of large-scale systems. The power of this approach is demonstrated on a system that spans all of the United States over a four-week horizon. The case study quantifies the potential impact of autonomous trucking and shows that ATHNs can have significant benefits over traditional transportation networks.

Historically, Operations Research (OR) discipline has mainly been focusing on economic concerns. Since the early 2000s, human considerations are gaining increasing attention, pushed by the growing societal concerns of sustainable development on the same terms as the economic and ecological ones. This paper is the first part of a work that aims at reviewing the efforts dedicated by the OR community to the integration of human factors into logistics and manufacturing systems. A focus is put on the modeling and solution approaches used to consider human characteristics, their practical relevance, and the complexity induced by their integration within optimization models. The material presented in this work has been retrieved through a semi-systematic search of the literature. Then, a comprehensive analysis of the retrieved corpus is carried out to map the related literature by class of problems encountered in logistics and manufacturing. These include warehousing, vehicle routing, scheduling, production planning, and workforce scheduling and management. We investigate the mathematical programming techniques used to integrate human factors into optimization models. Finally, a number of gaps in the literature are identified, and new suggestions on how to suitably integrate human factors in OR problems encountered in logistics and manufacturing systems are discussed.

The paper investigates the Electric Vehicle Routing Problem with a non-linear concave and strictly monotonic increasing charging function. In the literature, the non-linear charging function is typically approximated by a piecewise linear charging function which does not overestimate the real charging function in any point. As the piecewise linear charging function underestimates the real state-of-charge in some points, such an approximation excludes feasible solutions from the solution space. To overcome this drawback we introduce a new method to determine a piecewise linear charging function overestimating the real charging function in a way that the area between both functions is minimized as well as an adaptation of a known linearization to include the piecewise linear charging function in a branch-and-cut approach. Thereby, we include infeasible solutions in the solution space. To declare them infeasible again we check every integer solution obtained in the branch-and-cut procedure and add an infeasible path cut if the solution is infeasible for the real charging function such that the procedure terminates with an optimal solution for the real charging function. Our approach is evaluated in a computational study in which instances with up to 100 customers were solved to optimality. Moreover, we evaluate the trade-off between a more complex model formulation due to more binary variables if the number of supporting points for the piecewise linear approximation is increased and the higher approximation error if fewer supporting points are used.

The last mile delivery is particularly challenging for stochastic deliveries with narrow time windows. This topic is timely due to the rise of e-commerce and courier type services and the impacts of fleet size and vehicle type on delivery costs. A novel contribution of this research is to provide an optimization approach, extending the newsvendor model, to provide an optimal drone fleet sizing solution with stochastic demand in terms of number of deliveries and deliveries weight or payload from one central depot. The solutions obtained are robust, as shown in a comprehensive sensitivity analysis.