EURO Journal on Transportation and Logistics最新文献

This paper presents an air traffic management framework to enable multiple fleets of unmanned aerial vehicles to traverse dense, omni-directional air traffic safely and efficiently. The main challenge addressed here is separation assurance in the absence of full coordination and communication. In this framework, each fleet is independently managed by a routing agent, which progressively plans the non-overlapping move-ahead corridors for vehicles in the fleet by solving a nonlinear optimization model. The model is artfully designed so that agents of different fleets need not engage in complicated multilateral communications or make guesses about external vehicles’ flight intents to maintain effective inter-vehicle separation. For a complex routing problem, the framework is able to support centralized fleet routing, decentralized vehicle self-routing, and any other agent-vehicle configuration in between, allowing for customized trade-off between response time and traffic efficiency. Innovative algorithmic enhancements for solving the agent’s nonconvex routing problem are prescribed with detailed annotation. The effectiveness and noteworthy properties of the framework are demonstrated by several simulation experiments.

In this paper, an algorithm, called ACS-OPHS, is proposed to tackle the Orienteering Problem with Hotel Selection (OPHS). This algorithm is strongly based on the Ant Colony System (ACS); however, it differs from the ACS in the way the paths are constructed, in tuning a parameter of the transition rule and in the pheromone trails updating rules. The ACS-OPHS uses a bi-directional search strategy and employs a novel and fast approach to identify all feasible intermediate hotels in an offline manner. Moreover, in the ACS-OPHS, the relative importance of exploitation versus exploration is determined according to the progress of the algorithm in approaching to the global optima. The ACS-OPHS is a simple and well-performing approach to solve the OPHS. Concerning the standard benchmark instances, it outperforms the state-of-the-art algorithms in several instances and produces competitive solutions in reasonable time. This algorithm also improves the best known results of four instances with unknown optimal solutions.

This article addresses a relocation and recharging problem faced by modern carsharing operators who manage a fleet of electric vehicles. As customers utilize the fleet, batteries are depleted and vehicles are possibly left in low-demand locations. Consequently, carsharing operators need to arrange the charging of depleted batteries and the relocation of poorly positioned vehicles in order to better meet the demand of the customers. Most of these activities require the intervention of dedicated staff. This article provides a framework for planning recharging and relocation activities based on periodically routing and scheduling a number of dedicated staff as a result of updated system information. The periodic planning problem is formulated as a Mixed Integer Linear Program and solved in a rolling-horizon fashion. For the solution of the problem a fast Adaptive Large Neighborhood Search heuristic is proposed. Tests based on data for the city of Oslo show that the heuristic can deliver, in reasonable computational time, high quality solutions for instances compatible with real-life planning problems.

This paper addresses a school bus routing problem, which is classified as a location–allocation–routing problem. The problem consists of selecting pickup locations, allocating students to them, and generating a route that traverses between them. The proposed model is for a single school and a single-route. The objective is to find the subset of pickup stops aiming to minimize the total distance walked by all students from their homes to the respective pickup stops, subject to an upper bound on the route distance of connecting selected stops. We present an exact and heuristic algorithms which are developed based on a layered graph. Computational results are conducted on a series of generated benchmark instances and test data from Norway that demonstrate a good performance of the proposed approach.

Mobility-on-Demand Transit (MoDT) is a suitable solution for linking packed urban centers to low-demand suburban areas. Meanwhile, micromobility services, including dockless bikesharing and electric scooters, are growing exponentially worldwide, providing a low-cost, low-emission travel mode for short home-based trips. We propose an intermodal network in which travelers use micromobility for the first-/last-mile connections to MoDT. The optimal design of the intermodal network is formulated as a two-stage stochastic program with a revenue-maximization objective. The first stage solves the near-optimal transfer hub locations, and the second stage considers the integrated operations of the micromobility and MoDT vehicle fleet. This work contributes to the MoD literature by addressing how to coordinate the intermodal transfers and improve the utilization of vehicles with uncertain demand. The movements of these vehicles are modeled as an interconnected closed queueing network with time lags. A new starter-follower model captures the rearranged ride-pooling behavior at these selected transfer hubs. We implement this network design method to evaluate the benefit of combining a bikesharing and a MoDT network in New York City. This paper provides a systematic method for designing intermodal mobility networks, laying the foundation for multimodal mobility applications.

This paper proposes different algorithms to tackle the Generalized Train Unit Shunting Problem (G-TUSP). This is the pre-operational problem of managing rolling stock in a station, between arrivals and departures. It includes four sub-problems: the Train Matching Problem, the Track Assignment Problem, the Shunting Routing Problem, and the Shunting Maintenance Problem. In our algorithms, we consider different combinations for the integrated or sequential solutions of these sub-problems, typically considered independently in the literature. We assess the performance of the algorithms proposed in real-life and fictive instances representing traffic in Metz-Ville station, which includes four shunting yards. It is a main junction between two dense traffic lines in the east of France. In a thorough experimental analysis, we study the contribution of each sub-problem to the difficulty of the G-TUSP, and we identify the best algorithms. The outcomes of our algorithms are superior to solutions manually designed by experienced railway practitioners.

Traffic Assignment (TA) models route choices of users of a road transport network assuming a known relationship between traffic flow and travel cost, and fixed demand between origin and destination points in the network. In this paper, we consider the case where the users’ travel cost function is a weighted sum of travel time, fuel consumption, and tolls. We show the existence of speed limits and tolls whose combination induces a traffic pattern with minimum total fuel consumption as a user equilibrium. This result holds both in the case where all tolls are required to be non-negative as well as in the case where negative tolls (subsidies) are allowed but network-wide revenue neutrality of the toll scheme is required. With respect to the speed limits, arbitrary non-negative values are allowed in our model, but we additionally analyze the more realistic scenario in which only a discrete set of speed limits (e.g., integer multiples of ten) is available. While this restriction of the available speed limits may lead to an increase in the total fuel consumption achievable in a user equilibrium, we prove upper bounds showing that the increase is very small for real-world TA instances.

The possible unavailability of urban rail-based transport services due to planned maintenance activities may have significant consequences on the perceived quality of service, thus affecting railway attractiveness.

To cope with the mitigation of planned service interruptions and to guarantee a seamless journey and a good travel experience for passengers, it is possible to exploit the existing services differently and/or provide additional on-demand services, such as temporary supplemental bus lines.

In this context, this paper aims to develop a mathematical programming model for planning service interruptions due to maintenance considering passenger transport demand dynamics. In particular, the proposed approach deals with service interruptions characterized by a long duration for which timetable adaption strategies are not applicable, suggesting mitigation actions that exploit the already existing services and/or the activation of additional ones, with the aim of minimizing users’ inconvenience. In doing so, the planned infrastructure status (i.e., available or under maintenance), as well as the forecasted transport demand, are taken into account to adapt the service accordingly by offering a multimodal transport solution to passengers.

To find the best solution, a decomposition solution approach is proposed in combination with a multistage cooperative framework with feedback that models the negotiation process between the involved actors.

Finally, the applicability of the proposed approach to real case studies is discussed based on some performance indicators.

Automated vehicles are becoming a reality. Expectations are that AVs will ultimately transform personal mobility from privately owned assets to on-demand services. This transformation will enhance the possibility of sharing trips, leading to shared AVs (SAVs). The preeminent aim of this paper is to lay foundations for fast and efficient algorithms to be used in such new driving conditions. These algorithms must be able to solve Dial-a-Ride problems with transfers (DARPT). Hence, they should efficiently assign passengers to vehicles and routes while also: administering vehicles dispatch, determining convenient parking for idling vehicles and managing vehicle routing in real-time. In this paper, we develop two integer linear programming models (one in continuous time and one in discrete time) and their extensions to solve the DARPT. Our models take into account routing, service times, constraints on maximum route time-span, unserved requests, preferred arrival and departure time, nonconstant travel times, convenient parking while optimizing routing costs and quality of the service. The models are tested on instances based on Google Maps data by solving them with a commercial solver. The results of these tests are the starting point for validating the performance of forthcoming, ad hoc metaheuristics to be used in real-life sized scenarios.

Liner shipping repositioning is the costly process of moving container ships between services in a liner shipping network to adjust the network to the changing demands of customers. Existing deterministic models for the liner shipping fleet repositioning problem (LSFRP) ignore the inherent uncertainty present in the input parameters. Assuming these parameters are deterministic could lead to extra costs when plans computed by a deterministic model are realized. We introduce an optimization model for the stochastic LSFRP that handles uncertainty regarding container demands and ship travel times. We extend existing LSFRP instances with uncertain parameters and use this new dataset to evaluate our model. We demonstrate the influence of uncertain demand and travel times on the resulting repositioning plans. Furthermore, we show that stochastic optimization generates solutions yielding up to ten times higher expected values and more robust solutions, measured against the CVaR90 objective, for decision-makers in the liner shipping industry compared to the application of deterministic optimization in the literature.