Title :
A genetics algorithm for solving job-shop scheduling problems in FMS
Author :
Shoutao Li ; Wei Jiang ; Wei Tian
Author_Institution :
Coll. of Commun. Eng., Jilin Univ., Changchun, China
Abstract :
Job shop scheduling problem (Job shop Scheduling Problem) is one of the hardest combinatorial optimization problems. Due to its nonlinear characteristic and NP hard, the traditional algorithm cannot solve this problem. This paper proposes a genetic algorithm to solve the classic job shop machine scheduling problems and emphasizes on the genetic algorithms coding so as to present a coding mode based on the relationship between machines and different working procedures. On this point, this paper aimed at designing a new crossover and mutation genetic operators to perform a global search. Furthermore, the experiments are conducted and the results show that this method has high efficiency in solving the large scale job shop problem.
Keywords :
combinatorial mathematics; flexible manufacturing systems; genetic algorithms; job shop scheduling; search problems; FMS; NP hard; coding mode; combinatorial optimization problems; crossover genetic operators; genetics algorithm; global search; job shop machine scheduling problems; large scale job shop problem; mutation genetic operators; nonlinear characteristic; Biological cells; Encoding; Genetic algorithms; Job shop scheduling; Machine tools; Sociology; Statistics; Genetic algorithm; Job-shop; Scheduling;
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
DOI :
10.1109/CCDC.2015.7162181