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

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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A Genetic Algorithm for Optimizing Parameters for Ant Colony Optimization Solving Capacitated Vehicle Routing Problems

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence ◽

10.1145/3396474.3396489 ◽

2020 ◽

Author(s):

Oliver S. Faust ◽

Carlo G. Mehli ◽

Thomas Hanne ◽

Rolf Dornberger

Keyword(s):

Genetic Algorithm ◽

Ant Colony Optimization ◽

Vehicle Routing ◽

Ant Colony ◽

Vehicle Routing Problems ◽

Routing Problems ◽

Capacitated Vehicle

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A hybrid genetic algorithm for vehicle routing problems with dynamic requests

2017 IEEE Symposium Series on Computational Intelligence (SSCI) ◽

10.1109/ssci.2017.8285301 ◽

2017 ◽

Cited By ~ 2

Author(s):

Ruikang Yi ◽

Wenjian Luo ◽

Chenyang Bu ◽

Xin Lin

Keyword(s):

Genetic Algorithm ◽

Vehicle Routing ◽

Hybrid Genetic Algorithm ◽

Vehicle Routing Problems ◽

Routing Problems

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

International Journal of Applied Evolutionary Computation ◽

10.4018/jaec.2013010102 ◽

2013 ◽

Vol 4 (1) ◽

pp. 17-38 ◽

Cited By ~ 1

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.

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