A new geometric shape-based genetic clustering algorithm for the multi-depot vehicle routing problem

Yücenur, G. Nilay; Demirel, Nihan Çetin

doi:10.1016/j.eswa.2011.03.077

Cited by 66 publications

(19 citation statements)

References 43 publications

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“…The introduction of clustering technology [25][26][27][28][29][30] can effectively solve the problem of the diversity of population. The individuals in the initial population can be divided into several classes, so that the individuals in the same class have the greatest degree of similarity, while the individuals in different classes share the least degree of similarity.…”

Section: Improvement Strategymentioning

confidence: 99%

Wavelet neural network with improved genetic algorithm for traffic flow time series prediction

Yang

2016

Optik

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Section: Improvement Strategymentioning

confidence: 99%

Wavelet neural network with improved genetic algorithm for traffic flow time series prediction

Yang

2016

Optik

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“…In this area, Sumichrast and Markham [32] formulated the problem of transporting raw materials from multiple sources, or depots, to a set of plants as in the multi-depot vehicle routing problem (MDVRP). Yucenur and Demirel [33] adopted a new geometric shape-based genetic clustering algorithm for the multi-depot vehicle routing problem. A genetic algorithm based on this clustering technique was developed for the solution process of the MDVRP and based on the obtained results; the proposed algorithm provided a better clustering performance in terms of the distance of each customer to each depot in clusters.…”

Section: Multi-depot Vehicle Routing Problem With Time Windows (Md-vrmentioning

confidence: 99%

High-Level Relay Hybrid Metaheuristic Method for Multi-Depot Vehicle Routing Problem with Time Windows

Noori

Ghannadpour

2011

J Math Model Algor

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This paper presents an efficient hybrid metaheuristic solution for multidepot vehicle routing with time windows (MD-VRPTW). MD-VRPTW involves the routing of a set of vehicles with limited capacity from a set of depots to a set of geographically dispersed customers with known demands and predefined time windows. The present work aims at using a hybrid metaheuristic algorithm in the class of High-Level Relay Hybrid (HRH) which works in three levels and uses an efficient genetic algorithm as the main optimization algorithm and tabu search as an improvement method. In the genetic algorithm various heuristics incorporate local exploitation in the evolutionary search. An operator deletion-retrieval strategy is executed which shows the efficiency of the inner working of the proposed method. The proposed algorithm is applied to solve the problems of the standard Cordeau's Instances. Results show that proposed approach is quite effective, as it provides solutions that are competitive with the best known in the literature.

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“…For example, G. Nilay Yucenur [2] proposed a new type of geometric shape based genetic clustering algorithm to solve MDVRP. William Ho and George TS Ho [3] proposed a hybrid genetic algorithm to solve MDVRP.…”

Section: Introductionmentioning

confidence: 99%

A tabu search algorithm with variable cluster grouping for multi-depot vehicle routing problem

Miao²,

Xie

et al. 2014

Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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We herein present a tabu search algorithm with variable cluster grouping (TSVCG for short) to deal with Multidepot vehicle routing problem (MDVRP for short). In TSVCG, we firstly adopt variable cluster grouping to convert a complicated MDVRP to typical single depot vehicle routing problem (SDVRP for short). And then we apply a tabu search algorithm to solve each SDVRP. In the grouping process, we discuss how to find a scale factor and minimum geometric semicircle correction factor to improve the customer points' grouping, and thus get the different groups for further problemsolving. The experimental results shown that the proposed variable cluster grouping can reduce grouping blindness and improve the efficiency of grouping and viability of group results. The results also shown that the proposed TSVCG performed well compared with the previous work with the geometric grouping.

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A new geometric shape-based genetic clustering algorithm for the multi-depot vehicle routing problem

Cited by 66 publications

References 43 publications

Wavelet neural network with improved genetic algorithm for traffic flow time series prediction

Wavelet neural network with improved genetic algorithm for traffic flow time series prediction

High-Level Relay Hybrid Metaheuristic Method for Multi-Depot Vehicle Routing Problem with Time Windows

A tabu search algorithm with variable cluster grouping for multi-depot vehicle routing problem

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