EURO Journal on Transportation and Logistics最新文献

We consider a car-sharing problem in a company during business hours. The employees, located at one or several offices, have to travel to one or more appointments each with a fixed location and fixed start and end times and return to one of the offices afterwards. Each employee trip can be carried out with one out of several alternative modes of transport. The considered modes of transport are a company car from the company car pool, walking, public transport, bike, and taxi. The aim is to assign modes of transport to employee trips such that the total costs of covering the trips is minimized.

We first consider that the company is operating a shared fleet of a single type of vehicle and then that the fleet consists of different vehicle types. By relying on minimizing the savings when using a vehicle compared to the cheapest alternative available mode of transport (which is used if no vehicle is assigned to a trip), we do not need to model the alternative modes explicitly. For the case where the vehicle fleet consists of a single type of vehicle, we model the vehicle-sharing problem as a minimum-cost flow problem. Secondly, if multiple types of vehicles are available the problem can be formulated as a multi-commodity flow problem. Since very efficient solution methods are available for these formulations, they are applicable in daily operations.

We provide a comprehensive computational study for both cases on instances based on demographic, spatial, and economic data of Vienna. We show that our formulations for the problem solve these instances in a few seconds, which makes them usable in an online booking system. In the analysis, we discuss different potential settings. We study different sizes and compositions of the shared fleet, restricted sets of modes of transport, and variations of the objective function.

Given a set of transport requests to a transit station and a set of homogeneous vehicle, both geographically dispersed in a business area, the First-Mile Ride-Sharing Problem (FMRSP) consists of finding least cost vehicle routes to transport passengers to the station by shared rides. In this paper we formulate the problem as a mathematical optimization problem and study the effectiveness of preventive movements of idle vehicles (i.e., rebalancing) in order to anticipate future demand. That is, we identify promising rebalancing locations based on historical data and give the model incentives to assign vehicles to such location. We then assess the effectiveness of such movements by simulating online usage of the mathematical model in a rolling-horizon framework. The results show that rebalancing is consistently preferable both in terms of profits and service rate. Particularly, in operating contexts where the station is not centrally located, rebalancing movements increase both profits and service rates by around 30% on average.

In the Multi-Commodity two-echelon Distribution Problem (MC2DP), multiple commodities are distributed in a two-echelon distribution system involving suppliers, distribution centres and customers. Each supplier may provide different commodities and each customer may request several commodities as well. In the first echelon, capacitated vehicles perform direct trips to transport the commodities from the suppliers to the distribution centres for consolidation purposes. In the second echelon, each distribution centre owns a fleet of capacitated vehicles to deliver the commodities to the customers through multi-stop routes. Commodities are compatible, i.e., they can be mixed in the vehicles. Finally, customer requests can be split by commodities, that is, a customer can be visited by several vehicles, but the total amount of each commodity has to be delivered by a single vehicle. The aim of the MC2DP is to minimize the total transportation cost to satisfy customer demands.

We propose a set covering formulation for the MC2DP where the exponential number of variables relates to the routes in the delivery echelon. We develop a Branch-Price-and-Cut algorithm (BPC) to solve the problem. The pricing problem results in solving an Elementary Shortest Path Problem with Resource Constraints (ESPPRC) per distribution centre. We tackle the ESPPRC with a label setting dynamic programming algorithm which incorporates ng-path relaxation and a bidirectional labelling search. Pricing heuristics are invoked to speed up the procedure. In addition, the formulation is strengthened by integrating capacity cuts and two families of valid inequalities specific for the multiple commodities aspect of the problem.

Our approach solves to optimality 439 over the 736 benchmark instances from the literature. The optimality gap of the unsolved instances is 2.1%, on average.

The paper considers a special class of routing problems with the objective of minimizing the passengers risk. A major area of application of the low risk models is helicopter transportation widely used in the petroleum industry. In general, the total risk is considered to be proportional to the number of passengers exposed to landings and takeoffs during several multi-leg flights. We give a review of most of the studied models and demonstrate their links with the known problems of combinatorial optimization, such as the minimum latency problem, the multiple deliverymen problem, single machine and parallel machine scheduling, etc. In this paper, we focus on the problem of minimizing total risk, provided that the pickup from a number of locations is performed by two flights, and it is allowed that a location is visited by both flights, splitting the pickup demand. We show that the problem in NP-hard and admits a pseudopolynomial-time dynamic programming algorithm. We also develop a fully polynomial-time approximation scheme and a fast 5/4-approximation algorithm. The results of computational experiments with our algorithms are reported.

Providing punctual, reliable and performant services to customers is one main goal of railway network operators. The railway scheduling problem is to determine, ahead of time (timetabling), a plan describing the timing of the operations in a railway network, or updating such plan during operations (rescheduling). By optimization and automation, it is possible to operate more trains on the network, closer to the infrastructure capacity. Especially when the scale and complexity of the scheduling problem is increasing, for large-scale networks and multiple interconnected problems, this is of great value for network operators. When planning or adjusting railway operations becomes increasingly complex, modern scheduling algorithms can bring significant performance and economic benefits. In this survey we review approaches in the state of the art for the problems of railway scheduling. We show how the many different approaches of decomposition proposed in the literature of railway scheduling can be categorized into two general principles. We study different solution methods and identify a list of open topics for dealing with large-scale problems for future research.

Machine learning has shown great potential in various domains, but its appearance in inventory control optimization settings remains rather limited. We propose a novel inventory cost minimization framework that exploits advanced decision-tree based models to approximate inventory performance at an item level, considering demand patterns and key replenishment policy parameters as input. The suggested approach enables data-driven approximations that are faster to perform compared to standard inventory simulations, while being flexible in terms of the methods used for forecasting demand or estimating inventory level, lost sales, and number of orders, among others. Moreover, such approximations can be based on knowledge extracted from different sets of items than the ones being optimized, thus providing more accurate proposals in cases where historical data are scarce or highly affected by stock-outs. The framework was evaluated using part of the M5 competition’s data. Our results suggest that the proposed framework, and especially its transfer learning variant, can result in significant improvements, both in terms of total inventory cost and realized service level.

We study a setting in which a company not only has a fleet of capacitated vehicles and drivers available to make deliveries but may also use the services of occasional drivers (ODs) willing to make deliveries using their own vehicles in return for a small fee. Under such a business model, a.k.a crowdshipping, the company seeks to make all the deliveries at the minimum total cost, i.e., the cost associated with their vehicles plus the compensation paid to the ODs.

We consider a stochastic and dynamic last-mile delivery environment in which customer delivery orders, as well as ODs available for deliveries, arrive randomly throughout the day, within fixed time windows.

We present a novel deep reinforcement learning (DRL) approach to the problem that can deal with large problem instances. We formulate the action selection problem as a mixed-integer optimization program.

The DRL approach is compared against other optimization under uncertainty approaches, namely, sample-average approximation (SAA) and distributionally robust optimization (DRO). The results show the effectiveness of the DRL approach by examining out-of-sample performance.

Events that cause disruptions to air company flight schedules transporting passengers and cargo are commonplace in practice, and their operational and financial consequences are often relevant. The aircraft recovery problem (ARP) consists of recovering the flight schedules lost due to such events, determining new flight departure times and possible flight cancellations, as well as revising routes for different aircraft. In this study, the main characteristics considered in ARP papers to make the problem as realistic as possible are identified and a systematic literature review is carried out based on seminal studies of the problem in the 1980s. The literature papers are reviewed in terms of the ARP variants studied, the objectives chosen to be optimized and the practical constraints most often considered in real applications, as well as the networks and mathematical formulations to represent the problem and the solution approaches used to deal with it. The aim is to present an up-to-date review of the state-of-the-art of the ARP and to identify possible literature gaps and interesting opportunities for future research on this problem. It is noteworthy that, compared to the simplicity of the first ARP studies, the studies of the following decades tended to involve more complex variants of the problem as a way to become more adherent to current practical environments, requiring more elaborate formulations and solution methods to be properly addressed.

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.

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.