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

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.