The Experts below are selected from a list of 27699 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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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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the multi depot Vehicle Routing Problem with inter depot routes
European Journal of Operational Research, 2007Co-Authors: Benoit Crevier, Jean François Cordeau, Gilbert LaporteAbstract:This article addresses an extension of the multi-depot Vehicle Routing Problem in which Vehicles may be replenished at intermediate depots along their route. It proposes a heuristic combining the adaptative memory principle, a tabu search method for the solution of subProblems, and integer programming. Tests are conducted on randomly generated instances.
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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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classical and modern heuristics for the Vehicle Routing Problem
International Transactions in Operational Research, 2000Co-Authors: Gilbert Laporte, Michel Gendreau, Jeanyves Potvin, Frederic SemetAbstract:This article is a survey of heuristics for the Vehicle Routing Problem. It is divided into two parts: classical and modern heuristics. The first part contains well-known schemes such as, the savings method, the sweep algorithm and various two-phase approaches. The second part is devoted to tabu search heuristics which have proved to be the most successful metaheuristic approach. Comparative computational results are presented.
Baozhen Yao - One of the best experts on this subject based on the ideXlab platform.
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an improved particle swarm optimization for carton heterogeneous Vehicle Routing Problem with a collection depot
Annals of Operations Research, 2016Co-Authors: Baozhen Yao, Junjie Gao, Mingheng ZhangAbstract:In this paper, a carton heterogeneous Vehicle Routing Problem with a collection depot is presented, which can collaboratively pick the cartons from several carton factories to a collection depot and then from the depot to serve their corresponding customers by using of heterogeneous fleet. Since the carton heterogeneous Vehicle Routing Problem with a collection depot is a very complex Problem, particle swarm optimization (PSO) is used to solve the Problem in this paper. To improve the performance of the PSO, a self-adaptive inertia weight and a local search strategy are used. At last, the model and the algorithm are illustrated with two test examples. The results show that the proposed PSO is an effective method to solve the multi-depot Vehicle Routing Problem, and the carton heterogeneous Vehicle Routing Problem with a collection depot. Moreover, the proposed model is feasible with a saving of about 28 % in total delivery cost and could obviously reduce the required number of Vehicles when comparing to the actual instance.
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improved ant colony optimisation for the dynamic multi depot Vehicle Routing Problem
International Journal of Logistics-research and Applications, 2013Co-Authors: Wanjun Cai, Xiaoting Yuan, Baozhen YaoAbstract:Dynamic Vehicle Routing Problem (DVRP) with single depot has received increasing interest from engineers and scientists. Dynamic multi-depot Vehicle Routing Problem (DMDVRP), an extension of DVRP, however, has not received much attention. In our paper, a distance-based clustering approach is introduced to simplify the DMDVRP by allocating each customer to its nearest depot. Thus, DMDVRP is decomposed to a sequence of DVRPs. An improved ant colony optimisation (IACO) with ant-weight strategy and mutation operation is presented to optimise Vehicle Routing Problem (VRP) in this paper. Moreover, to satisfy the real-time feature of DMDVRP, the nearest addition approach is used to handle the new orders occurring during a time slice on the basis of VRP solution. Finally, the computational results for 17 benchmark Problems are reported to validate that IACO with the distance-based clustering approach is more suitable for solving DMDVRP.
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An improved ant colony optimization for Vehicle Routing Problem
European Journal of Operational Research, 2009Co-Authors: Bin Yu, Zhong Zhen Yang, Baozhen YaoAbstract:The Vehicle Routing Problem (VRP), a well-known combinatorial optimization Problem, holds a central place in logistics management. This paper proposes an improved ant colony optimization (IACO), which possesses a new strategy to update the increased pheromone, called ant-weight strategy, and a mutation operation, to solve VRP. The computational results for fourteen benchmark Problems are reported and compared to those of other metaheuristic approaches.
Jan Mazal - One of the best experts on this subject based on the ideXlab platform.
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Applying the ant colony optimisation algorithm to the capacitated multi-depot Vehicle Routing Problem
International Journal of Bio-inspired Computation, 2016Co-Authors: Petr Stodola, Jan MazalAbstract:The multi-depot Vehicle Routing Problem MDVRP is an extension of a classic Vehicle Routing Problem VRP. There are many heuristic and metaheuristic algorithms e.g., tabu search, simulated annealing, genetic algorithms as this is an NP-hard Problem and, therefore, exact methods are not feasible for more complex Problems. Another possibility is to adapt the ant colony optimisation ACO algorithm to this Problem. This article presents an original solution of authors to the MDVRP Problem via ACO algorithm. The first part deals with the algorithm including its principles and parameters. Then several examples and experiments are shown.
W Y Szeto - One of the best experts on this subject based on the ideXlab platform.
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an artificial bee colony algorithm for the capacitated Vehicle Routing Problem
European Journal of Operational Research, 2011Co-Authors: W Y SzetoAbstract:This paper introduces an artificial bee colony heuristic for solving the capacitated Vehicle Routing Problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms the original one, and can produce good solutions when compared with the existing heuristics. These results seem to indicate that the enhanced heuristic is an alternative to solve the capacitated Vehicle Routing Problem.
Thibaut Vidal - One of the best experts on this subject based on the ideXlab platform.
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a memetic algorithm for the multi trip Vehicle Routing Problem
European Journal of Operational Research, 2014Co-Authors: Diego Cattaruzza, Nabil Absi, Dominique Feillet, Thibaut VidalAbstract:We consider the Multi Trip Vehicle Routing Problem, in which a set of geographically scattered customers have to be served by a fleet of Vehicles. Each Vehicle can perform several trips during the working day. The objective is to minimize the total travel time while respecting temporal and capacity constraints.
