A fast genetic algorithm for the flexible job shop scheduling problem

Abstract

This paper presents a fast genetic algorithm (GA) for solving the flexible job shob scheduling problem (FJSP). The FJSP is an extension of a classical NP-hard job shop scheduling problem. Here, we combine the active schedule constructive crossover (ASCX) with the generalized order crossover (GOX). Also, we show how to divide a population of solutions in the high-low fit selection scheme in order to guide the search efficiently. An initial experimental study indicates high convergence capabilities of the proposed GA.