Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling

Authors:
Deming Lei
Affiliations:
School of Automation, Wuhan University of Technology, 122 Ruoshi Load, Wuhan, Hubei Province, PR China
Venue:
Applied Soft Computing
Year:
2012

Citing 14
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Abstract

Fuzzy flexible job shop scheduling problem (FfJSP) is the combination of fuzzy scheduling and flexible scheduling in job shop environment, which is seldom investigated for its high complexity. We developed an effective co-evolutionary genetic algorithm (CGA) for the minimization of fuzzy makespan. In CGA, the chromosome of a novel representation consists of ordered operation list and machine assignment string, a new crossover operator and a modified tournament selection are proposed, and the population of job sequencing and the population of machine assignment independently evolve and cooperate for converging to the best solutions of the problem. CGA is finally applied and compared with other algorithms. Computational results show that CGA outperforms those algorithms compared.