EURO Journal on Transportation and Logistics最新文献

Collaborative delivery employing drones in last-mile delivery has been an extensively studied topic in recent years. In this paper, it is studied Truck–Drone Delivery Problems (TDDPs) in which a traditional delivery truck is gathered with a drone to cut delivery times and costs. The vehicles work together in a hybrid operation involving one drone launching from a larger vehicle that operates as a mobile depot and a recharging platform. The drone launches from the truck with a single package to deliver to a customer. Each drone must return to the truck to recharge batteries, pick up another package, and launch again to a new customer location. This work proposes a novel Mixed Integer Programming (MIP) formulation and a heuristic approach to address the problem. The proposed MIP formulation yields better linear relaxation bounds than previously proposed formulations for all instances, and was capable of optimally solving several unsolved instances from the literature. A hybrid heuristic based on the General Variable Neighborhood Search metaheuristic combining Tabu Search concepts is employed to obtain high-quality solutions for large-size instances. The efficiency of the algorithm was evaluated on 1415 benchmark instances from the literature, and over 80% of the best known solutions were improved.

This investigation combines the aircraft maintenance routing problem (AMRP) with the aircraft maintenance facility location problem as they are interdependent. Unlike the classical fixed charge location model, aircraft maintenance facility location is not based on static customer demands but on how aircraft move around the airline network while undergoing periodic maintenance checks. Two important factors relevant to this analysis are: (a) aircraft overnight stays at maintenance facilities and airports away from their home bases and (b) the location and cost of maintenance facility construction. There are two possible scenarios concerning the status of maintenance facilities. The first is a tabula rasa case – for a start-up airline with no existing maintenance facilities (AMFLP). The second is an operating airline case with prepositioned maintenance facilities, which we denote as a maintenance facility location update problem (AMFUP). We formulate both as binary integer multicommodity flow problems whose aim is to find the cost-minimizing number, size, and location of the maintenance facilities. Experiments with flight schedules from two regional airlines indicate that total annualized costs are convex in the number of maintenance airports. Though new facility costs tend to dominate, costs associated with noncyclic routes become increasingly important as the number of maintenance airports decreases. Key contributions of this paper are that it (a) demonstrates the superiority of the integration of the aircraft maintenance routing and aircraft maintenance facility location problems, (b) provides a formulation and solution methodology dealing with this significant but under-researched problem, and (c) presents extensive computational experiments, cost and sensitivity analyses for two real life aircraft flight schedules.

In the context of on-demand ridehailing, we propose a heuristic matching algorithm where a passenger can share their ride with one more passenger while experiencing a high-quality service with a minimal increase in travel time. To evaluate the performance, we implemented the algorithm in a traffic microsimulator and compared it with a ride-matching algorithm developed by Simonetto et al. (2019) at IBM. Moreover, to enhance efficiency and reduce computational time, we proposed a decentralized version that is based on vehicle-to-infrastructure (V2I) and infrastructure-to-infrastructure (I2I) communication. Application on the downtown Toronto road network demonstrated that the service rate in the centralized version improved by 24%, compared to the IBM algorithm. The decentralized version demonstrated a 25.53 times speedup and the service rate improved by 19%, compared to the IBM algorithm. Furthermore, a sensitivity analysis was conducted over both centralized and decentralized versions to address how different variables and parameters can affect the system’s performance.

In the challenging environment of attended home deliveries, pricing delivery options can play a crucial role to ensure profitability and service quality of retailers. To differentiate between standard and premium delivery options, many retailers include time windows of various lengths and fees within their offer sets. Even though customers prefer short delivery time windows, longer time windows can help maintaining flexibility and profitability for the retailer. We classify pricing strategies along two dimensions: static versus dynamic price setting and whether an offer set can include one or multiple price points. For static pricing, we implement price configurations that reflect current business practice. For the dynamic pricing, we adapt routing mechanisms that consider the flexibility within the underlying route plan during the booking process and set delivery fees accordingly. To evaluate the pricing strategies under plausibly realistic conditions, we model customer behavior through a nested logit model. This model represents customer choice as sequential decisions between premium and standard time windows. We perform a computational study considering realistic travel and demand data to investigate the effectiveness of static and dynamic time window pricing. Finally, we offer managerial insights and an outlook into applying strategic analysis to decide on price setting strategies.

Mobility-on-demand services continue to grow in popularity and could provide a cheap and resource-saving alternative to private vehicles. However, to be truly attractive to the general public, these services must be thoroughly optimized. In this paper, we consider a ride-hailing problem where available vehicles have to be assigned to dynamically arising customer requests and, furthermore, vacant vehicles have to be repositioned to other parts of the service area to balance supply and demand. We propose a novel repositioning strategy based on dynamically created, overlapping zones that addresses identified weaknesses of previous repositioning approaches. While most other ride-hailing studies only consider one specific setting for which a suitable ride-hailing strategy is developed, we further analyze which design decisions in the context of assignment and repositioning work best under different given problem characteristics. Our results show that the proposed repositioning approach outperforms the benchmark approaches in most of the relevant settings, independent of the underlying objective function. Additionally, we show that, especially for low-utilized fleets, the simple nearest-vehicle assignment strategy outperforms matching-based assignment approaches in many settings. The insights gained are analyzed and thoroughly discussed.

Due to the uncertain nature of the traffic system, it is not trivial for delivery companies to reliably satisfy customers’ time windows. To guarantee the reliability of the pickup and delivery service under stochastic and time-dependent travel times, we consider a pickup and delivery problem with hard time windows considering stochastic and time-dependent travel times. We propose a chance-constrained model where the operational cost and the service’s reliability are considered. To quantify the service reliability, every node is associated with a desired node service level, and there exists a global service level, both measured by success probabilities. We present an estimation method for arrival times and success probabilities under stochastic travel and service times. We propose an exact solution approach based on a branch-price-and-cut framework, where a labeling algorithm generates columns. Computational experiments are conducted to assess the effectiveness of the solution framework, and Monte Carlo simulations are used to show that the proposed method can generate routes that satisfy both node and global service levels.

Free-floating and instant-access carsharing systems are two features of the most flexible carsharing systems. In the former, users are allowed to freely park the car in any legal parking spot within the boundaries defined by the service operator. In the latter, users are not requested to make any reservation in advance before picking up the car. This paper aims at evaluating the importance of complete trip information in free-floating instant-access carsharing systems. To this aim, we consider a system, referred to as the look-ahead system, where users can reserve a car in advance and are also allowed to directly pick up a car without any reservation. In both cases, the user is requested to specify complete trip details, including the estimated usage duration and the location where the car will be returned. Taking advantage of a complete knowledge of the trip details, the service operator can assign a reservation request to a car that is in use at the time of reservation, provided it will become available at the time and location the user will need it. In its nature, the operational setting we consider is dynamic, as trip information is revealed to the service operator at the time the user requests a car. We also investigate the possibility of suggesting to the user that makes a reservation a pickup location different from the desired one, provided it is not too distant from the latter. We compare the performance of the look-ahead system with the case no information is anticipated, which resembles the service provided by the main carsharing operators currently active in Milan (Italy). Additionally, we use, as a benchmark, the static case where all the information about the users requests (pickup and return locations, as well as delivery time) is known before the start of the planning horizon. We consider a system with no car relocations performed by ad-hoc operators. This enables us to measure the pure benefit of anticipated information, i.e., the benefit coming only from knowing in advance where and when the vehicles will be returned, purged from potential vehicles availability related to relocation operations. The matching between user requests and cars is obtained, for the look-ahead system, iteratively at fixed time intervals through the solution of a Binary Linear Program (BLP) and, for the benchmark case, through the solution of a single BLP. No optimization model is needed for the case where no information is anticipated. A simulation study, based on real-world data from the city of Milan, shows that the look-ahead system can satisfy a number of requests much greater than the case without anticipated information and close to the benchmark case. Moreover, we perform a sensitivity analysis on different parameters, including the maximum distance a user is available to walk and the minimum amount of anticipation requested to users for booking requests, showing their impact on the performance of the systems.

In this paper we study the Traveling Salesman Problem with release dates (TSP-rd) and completion time minimization. The TSP-rd considers a single vehicle and a set of customers that must be served exactly once with goods that arrive to the depot over time, during the planning horizon. The time at which each requested good arrives is called release date and it is known in advance. The vehicle can perform multiple routes, however, it cannot depart to serve a customer before the associated release date. Thus, the release date of the customers in each route must not be greater than the starting time of the route. The objective is to determine a set of routes for the vehicle, starting and ending at the depot, where the completion time needed to serve all customers is minimized. We propose a new Integer Linear Programming model and develop a branch and cut algorithm with tailored enhancements to improve its performance. The algorithm proved to be able to significantly reduce the computation times when compared to a compact formulation tackled using a commercial mathematical programming solver, obtaining 24 new optimal solutions on benchmark instances with up to 30 customers within one hour. We further extend the benchmark to instances with up to 50 customers where the algorithm proved to be efficient. Building upon these results, the proposed model is adapted to new TSP-rd variants (Capacitated and Prize-Collecting TSP), with different objectives: completion time minimization and traveling distance minimization. To the best of our knowledge, our work is the first in-depth study to report extensive results for the TSP-rd through a branch and cut, establishing a baseline and providing insights for future approaches. Overall, the approach proved to be very effective and gives a flexible framework for several variants, opening the discussion about formulations, algorithms and new benchmark instances.

Europe strengthens its policies on climate change, green transition, and sustainable energy by addressing the high greenhouse-gas emissions in the transportation sector. Europe aims to reduce such emissions and reach a state of carbon neutrality by 2030 and 2050, respectively. This is feasible only if electric vehicles dominate the transportation sector. Paving the way for electric vehicle deployment on roads is subject to the provision of electric-vehicle-charging stations on the roads such that sufficiently good driving experience without any obstacles can be achieved. To address this timely societal challenge, we proposed a novel methodology by using the well-known facility-location-allocation methodology named set-covering location models with statistical machine learning and developed it for the problem settings of identifying electric-vehicle-charging station locations. Statistical machine learning was employed in the proposed model to more precisely identify and determine feasible coverage sets. We demonstrated the efficiency of the proposed model for the Capital Region of Denmark, where the green transition is part of the political agenda and is of severe societal concern, by using the newly collected main road transportation dataset.

In this paper, we study the warehouse reshuffling problem, where pallets in a storage have to be rearranged in an efficient way. A high-bay warehouse with an automated storage and retrieval system is considered, which is equipped with a twin shuttle stacker crane. This twin shuttle is designed to perform swap moves, where the pallet at a storage location is swapped with the pallet currently loaded on the stacker crane. We study a new problem variant, where the time for reshuffling is limited, and the desired assignment of pallets to storage locations is not given as input. The objective is to store the frequently accessed pallets closely to the input/output-point to keep the warehouse operations efficient. After proposing necessary and sufficient optimality conditions for assignments of pallets to storage locations, we present algorithms to deal with the time limit. Moreover, we prove NP-hardness of two special cases, introduce lower bound procedures, several construction heuristics and a simulated annealing algorithm. Finally, we present computational results on randomly generated instances based on a real-world company setting.