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

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...

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Bibliographic Details
Published in:	2008 International Symposium on Computational Intelligence and Design Vol. 1; pp. 369 - 372
Main Authors:	Guohui Zhang, Yang Shi, Liang Gao
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-10-2008
Subjects:	Algorithm design and analysis Computational intelligence Flexible Job Shop Schedules Flexible manufacturing systems Genetic Algorithm Genetic algorithms Job design Job shop scheduling Manufacturing systems Optimal control Processor scheduling Size control Tabu Search
Online Access:	Get full text
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Summary:	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.
ISBN:	0769533116 9780769533117
DOI:	10.1109/ISCID.2008.202