A new hybrid genetic algorithm for the capacitated vehicle routing problem

Berger, Jean; Barkaoui, Mohamed

doi:10.1057/palgrave.jors.2601635

Cited by 73 publications

(28 citation statements)

References 25 publications

(39 reference statements)

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“…Starting from the solutions provided by Fu et al [91], for instance C5 with 17 vehicles LSA found a solution of cost 868. 44 32 and ttime avg (0.7) = 460.0. As expected, the average CPU time consistently increases with the number of extracted customers, while the best solution costs are obtained with the default setting of p (i.e., p = 0.5), thus indicating that extracting too many customers leads in general to worse solutions (i.e., z avg (0.7) > z avg (0.5)).…”

Section: Computational Resultsmentioning

confidence: 99%

Enhanced mixed integer programming techniques and routing problems

Tramontani

2010

4OR-Q J Oper Res

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Section: Computational Resultsmentioning

confidence: 99%

Enhanced mixed integer programming techniques and routing problems

Tramontani

2010

4OR-Q J Oper Res

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“…The search space can also be reduced by means of representations that do not allow certain solutions [60,135] or by using a heuristic approach that removes some unreasonable solutions [3]. Many other examples can be found in the literature [7,11,43].…”

Section: Model Restriction and Reducing The Search Spacementioning

confidence: 99%

Scaling up data mining algorithms: review and taxonomy

García-Pedrajas

Haro-García

2012

Prog Artif Intell

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The overwhelming amount of data that are now available in any field of research poses new problems for data mining and knowledge discovery methods. Due to this huge amount of data, most of the current data mining algorithms are inapplicable to many real-world problems. Data mining algorithms become ineffective when the problem size becomes very large. In many cases, the demands of the algorithm in terms of the running time are very large, and mining methods cannot be applied when the problem grows. This aspect is closely related to the time complexity of the method. A second problem is linked with performance; although the method might be applicable, the size of the search space prevents an efficient execution, and the resulting solutions are unsatisfactory. Two approaches have been used to deal with this problem: scaling up data mining algorithms and data reduction. However, because data reduction is a data mining task itself, this technique also suffers from scalability problems. Thus, for many problems, especially when dealing with very large datasets, the only way to deal with the aforementioned problems is to scale up the data mining algorithm. Many efforts have been made to obtain methods that can be used to scale up existing data mining algorithms. In this paper, we review the methods that have been used to address the problem of scalability. We focus on general ideas, rather than specific implementations, that can be used to provide a general view of the current approaches for scaling up data mining methods. A taxonomy of the algorithms is proposed, and many examples of different tasks are presented. Among the different techniques used for data mining, we will pay special attention to evolutionary methods, because these methods have been used very successfully in many data mining tasks.

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“…Table 6 provides a comparison on the results obtained on the fourteen classical instances by some recent metaheuristics for the VRP (the reader is referred to Gendreau et al (2002), and for more results and comparisons). These are Cordeau et al (2002), Toth and Vigo (2003), Berger and Barkaoui (2003), Prins (2004), Tarantilis (2004) and TS + PR (columns marked CGLPS, TV, BB, P, T and HG). The results are expressed in terms of total distance traveled.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…While many heuristic approaches have been used to successfully tackle the VRP during the past five decades (Alfa et al 1991, Kawamura et al 1998, Bullnheimer et al 1998, Bullnheimer et al 1999, Reimann et al 2004, Berger and Barkaoui 2003, Prins 2004, Tarantilis 2004, Tabu Search (TS) has been the most widely applied technique to provide good approximate solutions for the problem. This method was originally proposed by Glover (1986) as a local search technique that does not terminate when it encounters local optima, but rather proceeds with moves that degrade the objective function.…”

Section: Introductionmentioning

confidence: 99%

Path relinking for the vehicle routing problem

Gendreau

2006

J Heuristics

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This paper describes a tabu search heuristic with path relinking for the vehicle routing problem. Tabu search is a local search method that explores the solution space more thoroughly than other local search based methods by overcoming local optima. Path relinking is a method to integrate intensification and diversification in the search. It explores paths that connect previously found elite solutions. Computational results show that tabu search with path relinking is superior to pure tabu search on the vehicle routing problem.

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A new hybrid genetic algorithm for the capacitated vehicle routing problem

Cited by 73 publications

References 25 publications

Enhanced mixed integer programming techniques and routing problems

Enhanced mixed integer programming techniques and routing problems

Scaling up data mining algorithms: review and taxonomy

Path relinking for the vehicle routing problem

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