Title :
A self-crossover Genetic Algorithm for job shop scheduling problem
Author :
Hou, Shiwang ; Liu, Yongjiang ; Wen, Haijun ; Chen, Yuepeng
Author_Institution :
Sch. of Mech. Eng. & Autom., North Univ. of China, Taiyuan, China
Abstract :
During the application of Genetic Algorithm (GA) for job shop scheduling problem (JSSP), chromosome representation and evolution strategy are the main consideration in order to guarantee the feasibility of solution. Crossover operation between two feasible solutions (parents) may result in infeasible solution (offspring).Inspired by the existence of self-reproducing in nature, this paper presents a self-crossover genetic algorithm for job shop scheduling problem (JSSP). The chromosome representation of the problem is based on work piece and the crossover operation is based on single individual. The approach was tested on a standard six-job six-machine (6×6) JSSP. The computational results validate the effectiveness of the proposed algorithm.
Keywords :
genetic algorithms; job shop scheduling; single machine scheduling; chromosome representation; evolution strategy; job shop scheduling problem; self-crossover genetic algorithm; standard six-job six-machine JSSP; Biological cells; Computers; Genetic algorithms; Job shop scheduling; Processor scheduling; Schedules; Job shop scheduling; genetic algorithms; self-crossover;
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-0740-7
Electronic_ISBN :
2157-3611
DOI :
10.1109/IEEM.2011.6117977
