Optimization Algorithm Study for Multiple-Constrained and Multiple-Objective Job-Shop Tool Dynamic Distribution

Cui Yan Wang; Wei Sheng Du; Jun Wang

doi:10.4028/www.scientific.net/AMM.551.612

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 551Optimization Algorithm Study for...

Optimization Algorithm Study for Multiple-Constrained and Multiple-Objective Job-Shop Tool Dynamic Distribution

Abstract:

It’s an NP problem for distributing tools for job-shop tasks when the schedules were formulated, and it belongs to multiple-constrained and multiple-objective problem. Based on adaptive weight approach, the restriction and multiple objective problems were solved. The optimization dynamic distribution model for this problem was established. Then heuristic and self-adaptive genetic algorithm was presented. In order to express the dynamic of the distribution result, Two-dimensional coding technology was adopted, a new coding rule combining dispatching rule was designed. The results show that hybrid self-adaptive genetic algorithm based on adaptive weight approach forms well for multiple-constrained and multiple-objective job-shop tool dynamic distribution.

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Periodical:

Applied Mechanics and Materials (Volume 551)

Pages:

612-616

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.551.612

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Online since:

May 2014

Authors:

Cui Yan Wang*, Wei Sheng Du, Jun Wang

Keywords:

Adaptive Weight Approach, Dynamic Tool Distribution, Hybrid Self-Adaptive Ga, Pareto Solutions, Two-Dimensional Coding

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* - Corresponding Author

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