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2014 | OriginalPaper | Buchkapitel

Data Mining Based Approach for Jobshop Scheduling

verfasst von : Yan-hong Wang, Ye-hong Zhang, Yi-hao Yu, Cong-yi Zhang

Erschienen in: Proceedings of 2013 4th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2013)

Verlag: Springer Berlin Heidelberg

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Abstract

In manufacturing system, there usually have been some unpredictable dynamic events, which would make the production scheme invalid. Therefore, it’s necessary to inject some new vitality to traditional scheduling algorithms. To harness the power of complex real-world data in manufacturing processes, a jobshop scheduling algorithm basing on data mining technique is presented. This approach is explored in view of seeking knowledge that is assumed to be embedded in the historical production database. Under the proposed scheduling system framework, C4.5 program is used as a data mining algorithm for the induction of rule-set. A rule-based scheduling algorithm is elaborated on the basis of the elaborated data mining solutions. The objective is to explore the patterns in data generated by conventional intellectualized scheduling algorithm and hence to obtain a rule-set capable of approximating the efficient solutions in a dynamic job shop scheduling environment. Simulation results indicate the superiority of the suggested approach.

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Metadaten
Titel
Data Mining Based Approach for Jobshop Scheduling
verfasst von
Yan-hong Wang
Ye-hong Zhang
Yi-hao Yu
Cong-yi Zhang
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-40060-5_73

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