Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 293–297, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-293-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 293–297, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-293-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

A HYBRID OPTIMIZATION METHOD FOR VEHICLE ROUTING PROBLEM USING ARTIFICIAL BEE COLONY AND GENETIC ALGORITHM

M. Davoodi1, M. Malekpour Golsefidi2, and M. S. Mesgari1 M. Davoodi et al.
  • 1Faculty of Geodesy and Geomatics, K.N.Toosi University of Technology, Iran
  • 2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran

Keywords: GIS, Vehicle Routing Problem, Optimization, Genetic Algorithm, Artificial Bee Colony

Abstract. Vehicle Routing Problem is one of the classic problems in GIS (Geospatial Information System) which had been studied for long times. An answer can be accepted as a good solution if it would be able to optimize the total length of the route or decrease the number of vehicles. A VRP defines finding the optimum route for some vehicles that serve to some customers and return to the service center. This problem is economically important because the cost and the time of serving to costumers are related to optimization of the problem’s answer. Furthermore, there are many problems like BUS management, Post pickup and delivery system and other servicing systems, which are technically similar to VRP. The aim of these problems is finding a composition of optimum routes between server and costumers. In addition, as the cost is related to time, finding shortest path means decreasing cost serving and decreasing time. In this article, a hybrid model using Artificial Bee Colony and Genetic Algorithm is proposed to solve VRP. In the first step, Artificial Bee Colony has been used to find a solution for five vehicles. The scout and the onlooker bees produced in 8 modes by two methods including the nearest neighborhood and the wide neighborhood. In the second step, the Genetic Algorithm helps to optimize the solutions. The results show that the production of the scout bees is the most effective factor in the answers to the problem and helps greatly converging the answers as soon as possible.