Introducing the Localized Genetic Algorithm for Small Scale Capacitated Vehicle Routing Problems

2009 ◽  
Vol 47 (2) ◽  
pp. 133-149 ◽  
Author(s):  
Ziauddin Ursani ◽  
Daryl Essam ◽  
David Cornforth ◽  
Robert Stocker
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Small Scale ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Capacitated Vehicle

Enhancements to the Localized Genetic Algorithm for Large Scale Capacitated Vehicle Routing Problems

2013 ◽  
Vol 4 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Ziauddin Ursani ◽  
Daryl Essam ◽  
David Cornforth ◽  
Robert Stocker
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Large Scale ◽  
Computational Time ◽  
Small Scale ◽  
Problem Decomposition ◽  
Vehicle Routing Problems ◽  
Solution Quality ◽  
Routing Problems ◽  
Capacitated Vehicle

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.

A Genetic Algorithm's Approach to the Optimization of Capacitated Vehicle Routing Problems

2016 ◽  
pp. 209-235 ◽  
Author(s):  
Mariano Frutos ◽  
Fernando Tohmé ◽  
Fabio Miguel
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Real World ◽  
Selective Pressure ◽  
Fresh Fruit ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
The Family ◽  
Capacitated Vehicle ◽  
Real World Problems

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.

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