A New Hybrid Optimization Algorithm and its Application in Job Shop Scheduling

Xian Zhou Cao; Zhen He Yang

doi:10.4028/www.scientific.net/AMM.55-57.1789

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 55-57A New Hybrid Optimization Algorithm and its...

A New Hybrid Optimization Algorithm and its Application in Job Shop Scheduling

Abstract:

In this paper, a dual-resource constrained job shop scheduling problem was studied by designing a hybrid genetic algorithm based on Genetic Algorithm (GA) and Simulated Annealing (SA). GA is used to search for a group of better solutions to the problem of minimizing production cost and then SA is applied to searching them for the best one. The combination of GA and SA utilizes the advantages of the two algorithms and overcomes their disadvantages. The operation-based encoding and an active schedule decoding method were employed. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. The results of numerical simulations, which are compared with those of other well-known algorithms, show better performance of the proposed algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 55-57)

Pages:

1789-1793

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.55-57.1789

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Online since:

May 2011

Authors:

Xian Zhou Cao, Zhen He Yang

Keywords:

Dual Resources, Hybrid Genetic Algorithm, Job Shop Scheduling, Production Costs

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References

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