EURO Journal on Transportation and Logistics最新文献

Due to high congestion in cities and growing demand for last-mile delivery services, several companies have been implementing two-echelon distribution strategies over the past few years. Notably, the installation of urban transshipment points has gained increasing attention, used by logistics operators to transfer goods from large freight trucks to smaller and more agile vehicles for last-mile delivery. Nevertheless, the main challenge is how to decide the number and location of these facilities under the presence of demand uncertainty. In this paper, we develop a two-stage stochastic program to design two-echelon last-mile delivery networks under demand uncertainty. This approach decomposes the problem into strategic decisions (facility location) and operational decisions (daily distribution of goods). To address large-scale instances, we solve the model through the sample average approximation (SAA) technique and estimate the optimal routing costs (of the SAA counterpart) using a continuous approximation method. Using a real-world case study with more than 1300 customers from New York City, our results provide several managerial insights regarding the mix of transportation modes, facility location, and the impact of allowing the outsourcing of customer demand. We provide extensive validation of the two-stage stochastic program results through a simulation-based approach and the calculation of the value of the stochastic solutions.

Coordinating the schedules of feeder and collector public transit lines can reduce the passenger travel times. With advances in smart mobility, mobility-as-a-service (MaaS) schemes allow passengers to book a combined ticket for all their trip legs. This detailed information about the origins and destinations of door-to-door trips offers the opportunity to coordinate the schedules of public transport lines to reduce the passenger travel times. In this study, we model the coordination problem of feeder and collector lines by explicitly considering the regularity of the feeder lines and the transfer times of passengers. The coordination problem is modeled as a nonlinear non-convex problem and reformulated to an easy-to-solve convex optimization problem. Because of the travel times uncertainty, we also introduce a stochastic optimization formulation based on the sample average approximation approach. We test the performance of our approach in a case study with feeder and collector lines in Singapore showing an improvement potential of 5%–10% in passenger travel times.

In this paper, we examine a vehicle routing problem with a makespan objective incorporating both stochastic and correlated travel times, which is usually not considered in routing problems. As an alternative to simulation, we develop an approach based on extreme-value theory to estimate the expected makespan (and standard deviation) and show how this approach can be embedded within an existing routing heuristic. We present results that demonstrate the impact of different correlation patterns and levels of correlation on route planning using real-world motivated instances. Depending on the particular objective, cost savings of up to 13.76% can be obtained by considering correlation.

Although different vehicle sharing systems (VSSs) use different vehicle types, the management challenges and optimization problems to be solved are similar or even the same. This observation led us to create a generalized and holistic VSS management framework, which aims to be applicable to the system using any vehicle type. The framework components, their mutual relationships, and framework tasks have been identified through a thorough and systematic literature review. Furthermore, the literature is positioned in the line with the framework. Finally, the framework and systematic literature review allowed us to identify gaps in the literature, and interesting research avenues.

The Traveling Salesperson Problem with Hotel Selection (TSPHS) corresponds to a variant of the classic Traveling Salesman Problem (TSP) where the salesperson must establish a route in order to visit and attend all customers and return to the point of origin. At the end of each working day, if they had not attended all customers, the salesperson must go to a hotel (and stay there overnight). The objective of TSPHS is to first minimize the number of trips and then to minimize the total time spent. The present work proposes a new heuristic algorithm called BRKGA-LS, that combines characteristics and procedures of well-established metaheuristics and methods of vehicle routing problems. Based on computational experiments, carried out with 131 instances of the literature, the global optimum was achieved in about 90% of the cases where it is known (86 out of 95 instances), and the algorithm was able to improve the results of the literature in 11 of all 36 instances. Additionally, a hypothesis test was performed to validate the results and the good performance of the BRKGA-LS in comparison to other algorithms in the literature. Therefore, the proposed algorithm is a promising alternative for solving the problem addressed.

We present a Variable Neighborhood Search heuristic for the rolling stock rescheduling problem. Rolling stock rescheduling is needed when a disruption leads to cancellations in the timetable. In rolling stock rescheduling, one must then assign duties, i.e., sequences of trips, to the available train units in such a way that both passenger comfort and operational performance are taken into account. For our heuristic, we introduce three neighborhoods, which focus on swapping duties between train units, on improving the individual duties and on changing the shunting that occurs between trips, respectively. These neighborhoods are used for both a Variable Neighborhood Descent local search procedure and for perturbing the current solution in order to escape from local optima. Moreover, we show that the heuristic can be extended to the setting of flexible rolling stock turnings at ending stations by introducing a fourth neighborhood. We apply our heuristic to instances of Netherlands Railways (NS). The results show that the heuristic is able to find high-quality solutions within 1 ​min of solving time. This allows rolling stock dispatchers to use our heuristic in real-time rescheduling.

The transition from traditional fuel-based bus transportation towards electric bus systems is regarded as a beacon of hope for emission-free public transport. In this study, we focus on battery electric bus systems, in which charging is possible at a variety of locations distributed at terminal stations over the entire bus network. In such systems, two intertwined planning problems to be considered are charging location planning and electric vehicle scheduling. We account for the interdependent nature of both planning problems by adopting a simultaneous optimization perspective. Acknowledging the existence of parameter uncertainty in such complex planning situations, which is rooted in potential changes of values of several environmental factors, we analyze the solution sensitivity to several of these factors in order to derive methodological guidance for decision makers in public transportation organizations. Based on the formulation of a new mathematical model and the application of a variable neighborhood search metaheuristic, we conduct sensitivity analysis by means of numerical experiments drawing on real-world data. The experiments reveal that it is not possible to identify persistent structures for charging locations by an a priori analysis of the problem instances, so that rather a simultaneous optimization is necessary. Furthermore, the experiments show that the configuration of electric bus systems reacts sensitively to parameter changes.

An energy-efficient train trajectory corresponds to the speed profile of a train between two stations that minimizes energy consumption while respecting the scheduled arrival time and operational constraints such as speed limits. Determining this trajectory is a well-known problem in the operations research and transport literature, but has so far been studied without accounting for stochastic variables like weather conditions or train load that in reality vary in each journey. These variables have an impact on the train resistance, which in turn affects the energy consumption. In this paper, we focus on wind variability and propose a train resistance equation that accounts for the impact of wind speed and direction explicitly on the train motion. Based on this equation, we compute the energy-efficient speed profile that exploits the knowledge of wind available before train departure, i.e., wind measurements and forecasts. Specifically, we: (i) construct a distance-speed network that relies on a new non-linear discretization of speed values and embeds the physical train motion relations updated with the wind data, and (ii) compute the energy-efficient trajectory by combining a line-search framework with a dynamic programming shortest path algorithm. Extensive numerical experiments reveal that our “wind-aware” train trajectories present different shape and reduce energy consumption compared to traditional speed profiles computed regardless of any wind information.