Journal
ADVANCES IN MECHANICAL ENGINEERING
Volume 10, Issue 10, Pages -Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1687814018804096
Keywords
Flexible job shop scheduling problem; dual-resource constrained; learning ability; genetic algorithm; variable neighborhood search
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Funding
- Science and Technology Support Program of Hubei Province in China [2015BAA063]
- Fundamental Research Funds for the Central Universities [2016-YB-020, 2016III024]
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In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker's learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a three-dimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.
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