EURO Journal on Transportation and Logistics最新文献_第10页

The vehicle routing problem with relaxed priority rules 放宽优先规则下的车辆路径问题

IF 2.4

EURO Journal on Transportation and Logistics Pub Date : 2021-01-01 DOI: 10.1016/j.ejtl.2021.100039

Thanh Tan Doan , Nathalie Bostel , Minh Hoàng Hà

引用次数: 6

Hybridizing large neighborhood search and exact methods for generalized vehicle routing problems with time windows 带时间窗的广义车辆路径问题的大邻域搜索与精确方法

IF 2.4

EURO Journal on Transportation and Logistics Pub Date : 2021-01-01 DOI: 10.1016/j.ejtl.2021.100040

Dorian Dumez , Christian Tilk , Stefan Irnich , Fabien Lehuédé , Olivier Péton

{"title":"Hybridizing large neighborhood search and exact methods for generalized vehicle routing problems with time windows","authors":"Dorian Dumez , Christian Tilk , Stefan Irnich , Fabien Lehuédé , Olivier Péton","doi":"10.1016/j.ejtl.2021.100040","DOIUrl":"10.1016/j.ejtl.2021.100040","url":null,"abstract":"<div><p>Delivery options are at the heart of the generalized vehicle routing problem with time windows (GVRPTW) allowing that customer requests are shipped to alternative delivery locations which can also have different time windows. Recently, the vehicle routing problem with delivery options was introduced into the scientific literature. It extends the GVRPTW by capacities of shared locations and by specifying service-level constraints defined by the customers’ preferences for delivery options. The vehicle routing problem with delivery options also generalizes the vehicle routing problem with home roaming delivery locations and the vehicle routing problem with multiple time windows. For all these GVRPTW variants, we present a widely applicable matheuristic that relies on a large neighborhood search (LNS) employing several problem-tailored destruction operators. Most of the time, the LNS performs relatively small and fast moves, but when the solution has not been improved for many iterations, a larger destruction move is applied to arrive in a different region of the search space. Moreover, an adaptive layer of the LNS embeds two exact components: First, a set-partitioning formulation is used to combine previously found routes to new solutions. Second, the Balas-Simonetti neighborhood is adapted to further improve already good solutions. These new components are in the focus of our work and we perform an exhaustive computational study to evaluate four configurations of the new matheuristic on several benchmark instances of the above-mentioned variants. On all the benchmark sets, our matheuristic is competitive with the previous state-of-the-art methods. In summary, the four configurations provide 81 new best-known solutions.</p></div>","PeriodicalId":45871,"journal":{"name":"EURO Journal on Transportation and Logistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ejtl.2021.100040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115047021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Erratum regarding missing Declaration of competing interest statements in previously published articles 关于先前发表的文章中缺少竞争利益声明的勘误表

IF 2.4

EURO Journal on Transportation and Logistics Pub Date : 2021-01-01 DOI: 10.1016/j.ejtl.2021.100031

引用次数: 0

Learning to handle parameter perturbations in Combinatorial Optimization: An application to facility location 学习处理组合优化中的参数扰动:在设施选址中的应用

IF 2.4

EURO Journal on Transportation and Logistics Pub Date : 2020-12-01 DOI: 10.1016/j.ejtl.2020.100023

Andrea Lodi , Luca Mossina , Emmanuel Rachelson

{"title":"Learning to handle parameter perturbations in Combinatorial Optimization: An application to facility location","authors":"Andrea Lodi , Luca Mossina , Emmanuel Rachelson","doi":"10.1016/j.ejtl.2020.100023","DOIUrl":"10.1016/j.ejtl.2020.100023","url":null,"abstract":"<div><p>We present an approach to couple the resolution of Combinatorial Optimization problems with methods from Machine Learning. Specifically, our study is framed in the context where a reference discrete optimization problem is given and there exist data for many variations of such reference problem (historical or simulated) along with their optimal solution. Those variations can be originated by disruption but this is not necessarily the case. We study how one can exploit these to make predictions about an unseen new variation of the reference instance.</p><p>The methodology is composed by two steps. We demonstrate how a classifier can be built from these data to determine whether the solution to the reference problem still applies to a perturbed instance. In case the reference solution is only partially applicable, we build a regressor indicating the magnitude of the expected change, and conversely how much of it can be kept for the perturbed instance. This insight, derived from <em>a priori</em> information, is expressed via an additional constraint in the original mathematical programming formulation.</p><p>We present the methodology through an application to the classical facility location problem and we provide an empirical evaluation and discuss the benefits, drawbacks and perspectives of such an approach.</p><p>Although it cannot be used in a black-box manner, i.e., it has to be adapted to the specific application at hand, we believe that the approach developed here is general and explores a new perspective on the exploitation of past experience in Combinatorial Optimization.</p></div>","PeriodicalId":45871,"journal":{"name":"EURO Journal on Transportation and Logistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ejtl.2020.100023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130631552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation 航空机组配对中的机器学习构造初始聚类进行动态约束聚合

IF 2.4

EURO Journal on Transportation and Logistics Pub Date : 2020-12-01 DOI: 10.1016/j.ejtl.2020.100020

Yassine Yaakoubi , François Soumis , Simon Lacoste-Julien

{"title":"Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation","authors":"Yassine Yaakoubi , François Soumis , Simon Lacoste-Julien","doi":"10.1016/j.ejtl.2020.100020","DOIUrl":"10.1016/j.ejtl.2020.100020","url":null,"abstract":"<div><p>The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and ends at the same base. Because of the extensive list of complex rules and regulations, determining whether a sequence of flights constitutes a feasible pairing can be quite difficult by itself, making CPP one of the hardest of the airline planning problems. In this paper, we first propose to improve the prototype <em>Baseline</em> solver of <span>Desaulniers et al. (2020)2020</span>) by adding dynamic control strategies to obtain an efficient solver for large-scale CPPs: Commercial-GENCOL-DCA. These solvers are designed to aggregate the flights covering constraints to reduce the size of the problem. Then, we use machine learning (ML) to produce clusters of flights having a high probability of being performed consecutively by the same crew. The solver combines several advanced Operations Research techniques to assemble and modify these clusters, when necessary, to produce a good solution. We show, on monthly CPPs with up to 50 000 flights, that Commercial-GENCOL-DCA with clusters produced by ML-based heuristics outperforms Baseline fed by initial clusters that are pairings of a solution obtained by rolling horizon with GENCOL. The reduction of solution cost averages between 6.8% and 8.52%, which is mainly due to the reduction in the cost of global constraints between 69.79% and 78.11%.</p></div>","PeriodicalId":45871,"journal":{"name":"EURO Journal on Transportation and Logistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ejtl.2020.100020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81747277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Introduction to the special issue on combining optimization and machine learning: Application in vehicle routing, network design and crew scheduling “优化与机器学习结合”专刊简介:在车辆路线、网络设计和乘员调度中的应用

IF 2.4

EURO Journal on Transportation and Logistics Pub Date : 2020-12-01 DOI: 10.1016/j.ejtl.2020.100024

Claudia Archetti , Jean-François Cordeau , Guy Desaulniers

引用次数: 3

Travel speed prediction based on learning methods for home delivery 基于学习方法的送货速度预测

IF 2.4

EURO Journal on Transportation and Logistics Pub Date : 2020-12-01 DOI: 10.1016/j.ejtl.2020.100006

Maha Gmira , Michel Gendreau , Andrea Lodi , Jean-Yves Potvin

{"title":"Travel speed prediction based on learning methods for home delivery","authors":"Maha Gmira , Michel Gendreau , Andrea Lodi , Jean-Yves Potvin","doi":"10.1016/j.ejtl.2020.100006","DOIUrl":"10.1016/j.ejtl.2020.100006","url":null,"abstract":"<div><p>The travel time to proceed from one location to another in a network is an important consideration in many urban transportation settings ranging from the planning of delivery routes in freight transportation to the determination of shortest itineraries in advanced traveler information systems. Accordingly, accurate travel time predictions are of foremost importance. In an urban environment, vehicle speeds, and consequently travel times, can be highly variable due to congestion caused, for instance, by accidents or bad weather conditions. At another level, one also observes daily patterns (e.g., rush hours), weekly patterns (e.g., weekdays versus weekend), and seasonal patterns. Capturing these time-varying patterns when modeling travel speeds can provide an immediate benefit to commercial transportation companies that distribute goods, since it allows them to better optimize their routes and reduce their environmental footprint.</p><p>This paper presents the first part of a project aimed at optimizing time-dependent delivery routes in an urban setting. It focuses on the prediction of travel speeds using as input GPS traces of commercial vehicles collected over a significant period of time. The proposed algorithmic framework is made of a number of macro-steps where different machine learning and data mining methods are applied. Computational results are reported on real data to empirically demonstrate the accuracy of the obtained predictions.</p></div>","PeriodicalId":45871,"journal":{"name":"EURO Journal on Transportation and Logistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ejtl.2020.100006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129960375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Two-stage stochastic programming model to locate capacitated EV-charging stations in urban areas under demand uncertainty 需求不确定性下城市电动汽车充电站选址的两阶段随机规划模型

IF 2.4

EURO Journal on Transportation and Logistics Pub Date : 2020-12-01 DOI: 10.1016/j.ejtl.2020.100025

S.A. MirHassani, A. Khaleghi, F. Hooshmand

{"title":"Two-stage stochastic programming model to locate capacitated EV-charging stations in urban areas under demand uncertainty","authors":"S.A. MirHassani, A. Khaleghi, F. Hooshmand","doi":"10.1016/j.ejtl.2020.100025","DOIUrl":"10.1016/j.ejtl.2020.100025","url":null,"abstract":"<div><p>Due to the dangerous effects of fossil fuels, policymakers tend to substitute fossil-fuel-based vehicles with electric ones. Thus, the optimal design of a charging station network providing convenient access for the users is of great importance. This paper presents a two-stage stochastic programming model for the problem of locating charging stations in urban areas. Parking lots around the buildings which may be visited by people during the day are considered as potential locations for charger installation. The model determines the parking lots that should be equipped with chargers and the number as well as the type of chargers that must be placed in each parking lot considering the demand as an uncertain parameter. The proposed model is examined on the dataset of a midtown area, taken from the literature, and an efficient heuristic algorithm based on Benders decomposition is utilized to solve the model. The results indicate that the heuristic method can find a near-optimal solution (with the optimality gap of at most 0.05%) in a short time.</p></div>","PeriodicalId":45871,"journal":{"name":"EURO Journal on Transportation and Logistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ejtl.2020.100025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129758583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Dynamic evaluation of airline Crew’s flight requests using a neural network 基于神经网络的机组人员飞行请求动态评估

IF 2.4

EURO Journal on Transportation and Logistics Pub Date : 2020-12-01 DOI: 10.1016/j.ejtl.2020.100018

M. Beulen, L. Scherp, B.F. Santos

{"title":"Dynamic evaluation of airline Crew’s flight requests using a neural network","authors":"M. Beulen, L. Scherp, B.F. Santos","doi":"10.1016/j.ejtl.2020.100018","DOIUrl":"10.1016/j.ejtl.2020.100018","url":null,"abstract":"<div><p>In airline crew rostering, pilots’ requests to operate specific flights need to be evaluated efficiently to avoid inefficient schedules. Despite the relevance of correctly assessing and granting crew requests, this topic has received very little attention in the literature. In this paper, we address the case this process is a dynamic problem, in which flight requests are submitted while others have already been granted and pre-assigned. This is the first work to dynamically model flight requests during the crew rostering process. We propose a simulation-trained neural-network algorithm to evaluate flight requests, providing a systematic way of assessing flight requests and supporting the definition of a cost-efficient request granting policy. To train and test this algorithm, we developed an innovative rolling rostering framework that captures the dynamic process in practice. The framework relies on an integer linear programming crew rostering model solved with the help of a column-generation algorithm. The neural-network algorithm is trained and tested in a case study with a major European airline. The results show that the algorithm is more effective than the current practice at the airline, granting 22% more requests while using the same workforce to operate the flight schedule.</p></div>","PeriodicalId":45871,"journal":{"name":"EURO Journal on Transportation and Logistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ejtl.2020.100018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132570138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Interday routing and scheduling of multi-skilled teams with consistency consideration and intraday rescheduling 多技能团队的日间路由和调度，考虑一致性和日间重新调度

IF 2.4

EURO Journal on Transportation and Logistics Pub Date : 2020-09-01 DOI: 10.1016/j.ejtl.2020.100012

Yulia Anoshkina, Frank Meisel

引用次数: 8