Title: An efficient hybrid of genetic and simulated annealing algorithms for multi server vehicle routing problem with multi entry

Authors: Hany Seidgar; Mehdi Abedi; Sahar Tadayoni Rad; Javad Rezaeian

Addresses: Mazandaran University of Science and Technology, P.O. Box 734, Tabarsi Street, Babol, Mazandaran, Iran ' Mazandaran University of Science and Technology, P.O. Box 734, Tabarsi Street, Babol, Mazandaran, Iran ' K.N. Toosi University of Technology, P.O. Box 470, Mirdamad Street, Tehran, Iran ' Mazandaran University of Science and Technology, P.O. Box 734, Tabarsi Street, Babol, Mazandaran, Iran

Abstract: This paper considers a multi-server-vehicle routing problem where vehicles could exist and enter the service depot several times. The central branch of bank has a number of nurses to service the failures. The objective is to find efficient routes for the nurses to service each task for each customer in order to minimise the total cost of routing and lateness/earliness penalties. In this paper, a mixed integer programming model is presented and two meta-heuristics approaches namely hybrid of genetic and simulated algorithms (HGSA) and imperialist competitive algorithm (ICA) are developed for solving the random generated problems. In HGSA, simulated annealing (SA) is employed with a certain probability to avoid being trapped in a local optimum. Furthermore, Taguchi experimental design method is applied to set the proper values of the algorithm's parameters. The available results show the higher performance of proposed HGSA compared with ICA, in quality of solutions within comparatively shorter periods of time.

Keywords: vehicle routing problem; multi server VRP; genetic algorithms; simulated annealing; imperialist competitive algorithm; ICA; Taguchi methods; mixed integer programming; MIP; experimental design; routing costs; lateness penalties; earliness penalties.

DOI: 10.1504/IJISE.2016.079823

International Journal of Industrial and Systems Engineering, 2016 Vol.24 No.3, pp.333 - 360

Available online: 17 Oct 2016 *

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