scholarly journals An Effective Order-Aware Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problems in Internet of Things

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 86102-86114 ◽  
Author(s):  
Na Lin ◽  
Yanjun Shi ◽  
Tongliang Zhang ◽  
Xuping Wang
2012 ◽  
Vol 60 (3) ◽  
pp. 611-624 ◽  
Author(s):  
Thibaut Vidal ◽  
Teodor Gabriel Crainic ◽  
Michel Gendreau ◽  
Nadia Lahrichi ◽  
Walter Rei

Author(s):  
Mariano Frutos ◽  
Fernando Tohmé ◽  
Fabio Miguel

This chapter addresses the family of problems known in the literature as Capacitated Vehicle Routing Problems (CVRP). A procedure is introduced for the optimization of a version of the generic CVRP. It generates feasible clusters and, in a first step, yields a coding of their ordering. The next stage provides this information to a genetic algorithm for its optimization. A selective pressure process is added in order to improve the selection and subsistence of the best candidates. This arrangement allows improving the performance of the algorithm. We test it using Van Breedam and Taillard's problems, yielding similar results as other algorithms in the literature. Besides, we test the algorithm on real-world problems, corresponding to an Argentinean company distributing fresh fruit. Four instances, with 50, 100, 150 and 200 clients were examined, giving better results than the current plans of the company.


2013 ◽  
Vol 4 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Ziauddin Ursani ◽  
Daryl Essam ◽  
David Cornforth ◽  
Robert Stocker

This paper is a continuation of two previous papers where the authors used Genetic Algorithm with automated problem decomposition strategy for small scale capacitated vehicle routing problems (CVRP) and vehicle routing problem with time windows (VRPTW). In this paper they have extended their scheme to large scale capacitated vehicle routing problems by introducing selective search version of the automated problem decomposition strategy, a faster genotype to phenotype translation scheme, and various search reduction techniques. The authors have shown that genetic algorithm used with automated problem decomposition strategy outperforms the GAs applied on the problem as a whole not only in terms of solution quality but also in terms of computational time on the large scale problems.


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