A Genetic Algorithm and Tabu Search for Solving Flexible Job Shop Schedules

Author

Zhang, Guohui ; Shi, Yang ; Gao, Liang

Author_Institution

State Key Lab. of Digital Manuf. Equip. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan

Volume

1

fYear

2008

fDate

17-18 Oct. 2008

Firstpage

369

Lastpage

372

Abstract

Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. An improved genetic algorithm combined with local search is proposed to solve the FJSP with makespan criterion. To control the local search and convergence to the global optimal solution, time-varying crossover probability and time varying maximum step size of tabu search are introduced. Representative flexible job shop scheduling benchmark problems are solved in order to test the effectiveness and efficiency of the proposed algorithm. Computational results show that the proposed genetic algorithm is efficient and effective.

Keywords

genetic algorithms; job shop scheduling; search problems; genetic algorithm; makespan criterion; representative flexible job shop scheduling problem; tabu search; time varying maximum step size; time-varying crossover probability; Algorithm design and analysis; Computational intelligence; Flexible manufacturing systems; Genetic algorithms; Job design; Job shop scheduling; Manufacturing systems; Optimal control; Processor scheduling; Size control; Flexible Job Shop Schedules; Genetic Algorithm; Tabu Search;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design, 2008. ISCID '08. International Symposium on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3311-7

Type

conf

DOI

10.1109/ISCID.2008.202

Filename

4725629