Title: Adaptive probabilities of crossover and mutation in genetic algorithm for solving stochastic vehicle routing problem

Authors: Rekik Ali; Gabsi Mounir; Temani Moncef

Addresses: Department of Computer Science, High Institute of Technological Studies, Direction of ISET's, Sfax, Tunisia ' Department of Computer Science, High Institute of Technological Studies, Direction of ISET's, Sfax, Tunisia ' Department of Computer Science, Faculty of Sciences, University of Tunis El Manar, Tunis, Tunisia

Abstract: The vehicle routing problem (vehicle routing problem, VRP in remainder of this paper) is a combinatorial optimisation problem and operational research. It belongs to the category of transportation problems, as the travelling salesman problem (travelling salesman problem, TSP) and the chance-constrained programming (CCP). These problems in the field of logistics, one or more vehicles must cover transportation network to deliver goods to customers or cover the roads network. Solving the problem is to determine a set of tours that minimise the best targets as the total distance travelled, the number of vehicles used, the sum of the delays of customers, i.e. this article describes a new algorithm for solving transportation problems with modified boundary conditions to minimise the uncertainty in the travel parameters, where a gain is associated with each customer and where the objective is to maximise the total gain collected and minimise the routing costs.

Keywords: vehicle routing problem; genetic algorithms; stochastic VRP; crossover; mutation.

DOI: 10.1504/IJAIP.2016.077498

International Journal of Advanced Intelligence Paradigms, 2016 Vol.8 No.3, pp.318 - 326

Received: 08 Feb 2015
Accepted: 04 May 2015
Published online: 04 Jul 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article