COMPARISON OF DIFFERENT CLUSTERING ALGORITHMS VIA GENETIC ALGORITHM FOR VRPTW
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Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Daaam International Vienna
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this paper, Vehicle Routing Problem with Time Windows (VRPTW) with known customer demands, a central depot and a set of vehicles with limited capacity, is considered. The objectives are both to minimize the total distance and the total waiting time of the vehicles while capacity and time windows constraints are secured. The applied solution techniques consist of three steps: clustering, routing and optimizing. By using K-means, Centroid-based heuristic, DBSCAN and SNN clustering algorithms in the initial population generation phase of genetic algorithm, the customers are divided into feasible clusters. Then feasible routes are constructed for each cluster. Lastly, the feasible route solutions are taken as the initial population and genetic algorithm is utilized for the optimization. A set of well-known benchmark data is used to compare the obtained results. According to the results of the study it is observed that using K-means clustering algorithm in generating the initial population of the genetic algorithm is more effective for the handled problem.
Açıklama
Anahtar Kelimeler
Vehicle Routing with Time Windows, Genetic Algorithm, Clustering, Multi-Objective Optimization, K-means Clustering Algorithm
Kaynak
International Journal of Simulation Modelling
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
18
Sayı
4
