The Experts below are selected from a list of 36621 Experts worldwide ranked by ideXlab platform
Gilbert Laporte - One of the best experts on this subject based on the ideXlab platform.
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a concise guide to existing and emerging Vehicle Routing problem variants
European Journal of Operational Research, 2020Co-Authors: Thibaut Vidal, Gilbert Laporte, Piotr MatlAbstract:Abstract Vehicle Routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating Vehicle Routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges.
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an adaptive large neighborhood search heuristic for the cumulative capacitated Vehicle Routing problem
Computers & Operations Research, 2012Co-Authors: Glaydston Mattos Ribeiro, Gilbert LaporteAbstract:The cumulative capacitated Vehicle Routing problem (CCVRP) is a variation of the classical capacitated Vehicle Routing problem in which the objective is the minimization of the sum of arrival times at customers, instead of the total Routing cost. This paper presents an adaptive large neighborhood search heuristic for the CCVRP. This algorithm is applied to a set of benchmark instances and compared with two recently published memetic algorithms.
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The capacitated Vehicle Routing problem with stochastic demands and time windows
Computers & Operations Research, 2011Co-Authors: Hongtao Lei, Gilbert Laporte, Bo GuoAbstract:The capacitated Vehicle Routing problem with stochastic demands and time windows is an extension of the capacitated Vehicle Routing problem with stochastic demands, in which demands are stochastic and a time window is imposed on each vertex. A vertex failure occurring when the realized demand exceeds the Vehicle capacity may trigger a chain reaction of failures on the remaining vertices in the same route, as a result of time windows. This paper models this problem as a stochastic program with recourse, and proposes an adaptive large neighborhood search heuristic for its solution. Modified Solomon benchmark instances are used in the experiments. Computational results clearly show the superiority of the proposed heuristic over an alternative solution approach.
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New heuristics for the Vehicle Routing problem
Logistics Systems: Design and Optimization, 2005Co-Authors: Jean François Cordeau, Alain Hertz, Michel Gendreau, Gilbert Laporte, Jean Sylvain SormanyAbstract:This chapter reviews some of the best metaheuristics proposed in recent years for the Vehicle Routing Problem. These are based on local search, on population search and on learning mechanisms. Comparative computational results are provided on a set of 34 benchmark instances.
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a unified tabu search heuristic for Vehicle Routing problems with time windows
Journal of the Operational Research Society, 2001Co-Authors: Jean François Cordeau, Gilbert Laporte, A MercierAbstract:This paper presents a unified tabu search heuristic for the Vehicle Routing problem with time windows and for two important generalizations: the periodic and the multi-depot Vehicle Routing problems with time windows. The major benefits of the approach are its speed, simplicity and flexibility. The performance of the heuristic is assessed by comparing it to alternative methods on benchmark instances of the Vehicle Routing problem with time windows. Computational experiments are also reported on new randomly generated instances for each of the two generalizations.
Martin W P Savelsbergh - One of the best experts on this subject based on the ideXlab platform.
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an optimization based heuristic for the split delivery Vehicle Routing problem
Transportation Science, 2008Co-Authors: Claudia Archetti, Grazia M Speranza, Martin W P SavelsberghAbstract:The split delivery Vehicle Routing problem is concerned with serving the demand of a set of customers with a fleet of capacitated Vehicles at minimum cost. Contrary to what is assumed in the classical Vehicle Routing problem, a customer can be served by more than one Vehicle, if convenient. We present a solution approach that integrates heuristic search with optimization by using an integer program to explore promising parts of the search space identified by a tabu search heuristic. Computational results show that the method improves the solution of the tabu search in all but one instance of a large test set.
Ivan Kristianto Singgih - One of the best experts on this subject based on the ideXlab platform.
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Multi-Depot Split-Delivery Vehicle Routing Problem
IEEE Access, 2021Co-Authors: Hyunpae Lim, Gyu M. Lee, Ivan Kristianto SinggihAbstract:The rapid advancements in information technologies and globalization change the way of distributing goods to customers. Many enterprises have multiple factories, warehouses, and distribution centers and strive for competitive efficiency in the distribution operations to minimize transportation costs. This study proposed the mixed-integer programming (MIP) model for the multi-depot split-delivery Vehicle Routing problems (MDSDVRPs) with hetero Vehicles, allowing multiple visits to a customer. A genetic algorithm (GA) with a novel two-dimensional chromosome representation has been proposed with dynamic mutation policies. The process parameters of the proposed GA are optimized using the Taguchi method. The proposed algorithms showed the benefits of split-delivery in MDSDVRPs and showed the competitive performance even for the classical single-depot Vehicle Routing problems with no split-delivery.
Lixin Miao - One of the best experts on this subject based on the ideXlab platform.
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a spatiotemporal partitioning approach for large scale Vehicle Routing problems with time windows
Transportation Research Part E-logistics and Transportation Review, 2012Co-Authors: Weihua Lin, Lixin MiaoAbstract:For Vehicle Routing problems (VRP) with time windows (VRPTW) solved by conventional cluster-first and route-second approach, temporal information is usually considered with Vehicle Routing but ignored in the process of clustering. The authors propose an alternative approach based on spatiotemporal partitioning to solving a large-scale VRPTW, considering jointly the temporal and spatial information for Vehicle Routing. A spatiotemporal representation for the VRPTW is presented that measures the spatiotemporal distance between two customers. The resulting formulation is then solved by a genetic algorithm developed for k-medoid clustering of large-scale customers based on the spatiotemporal distance. The proposed approach showed promise in handling large scale networks.
Fariborz Jolai - One of the best experts on this subject based on the ideXlab platform.
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efficient stochastic hybrid heuristics for the multi depot Vehicle Routing problem
Robotics and Computer-integrated Manufacturing, 2010Co-Authors: Mohammad Mirabi, S Fatemi M T Ghomi, Fariborz JolaiAbstract:The paper addresses the problem of multi-depot Vehicle Routing in order to minimize the delivery time of Vehicle objective. Three hybrid heuristics are presented to solve the multi-depot Vehicle Routing problem. Each hybrid heuristic combines elements from both constructive heuristic search and improvement techniques. The improvement techniques are deterministic, stochastic and simulated annealing (SA) methods. Experiments are run on a number of randomly generated test problems of varying depots and customer sizes. Our heuristics are shown to outperform one of the best-known existing heuristic. Statistical tests of significance are performed to substantiate the claims of improvement.
