Abstract
The main goal of this research is to find a solution of Vehicle Routing Problem using genetic algorithms. The Vehicle Routing Problem (VRP) is a complex combinatorial optimization problem that belongs to the NP-complete class. Due to the nature of the problem it is not possible to use exact methods for large instances of the VRP. Genetic algorithms provide a search technique used in computing to find true or approximate solution to optimization and search problems. However we used some heuristic in addition during crossover or mutation for tuning the system to obtain better result.
Highlights
The Vehicle Routing Problem (VRP) can be described as follows: given a fleet of vehicles with uniform capacity, a common depot, and several customer demands, finds the set of routes with overall minimum route cost which service all the demands [1]
Genetic algorithms have been inspired by the natural selection mechanism introduced by Darwin [2]
The operators used by genetic algorithms simulate the way natural selection is carried out
Summary
The VRP can be described as follows: given a fleet of vehicles with uniform capacity, a common depot, and several customer demands, finds the set of routes with overall minimum route cost which service all the demands [1]. Genetic algorithms have been inspired by the natural selection mechanism introduced by Darwin [2]. They apply certain operators to a population os solutions of the problem at hand, in such a way that the new population is improved compared with the previous one according to a prespecified criterion function. The crossover operator, which is applied to the temporary population produced after the application of the reproduction operator, selects pairs of solutions randomly, splits them at a random position, and exchanges their second parts. The mutation operator, which is applied after the application of the reproduction and crossover operators, selects randomly an element of a solution and alters it with some probability. Genetic algorithms provide a search technique used in computing to find true or approximate solutions to optimization and search problems
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