Mining scheduling knowledge for job shop scheduling problem

C. L. Wang, G. Rong, W. Weng, Y. P. Feng

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    The optimal or near-optimal schedules generated by traditional optimization or approximation methods for job shop scheduling problems (JSSP) contain valuable scheduling patterns about this kind of scheduling problems. These patterns could be used to improve the dispatching performance and provide insights into the corresponding scheduling problems. This paper uses timed Petri nets to describe the dispatching processes of the job shop scheduling scenarios. On this basis, a data mining based scheduling knowledge extraction framework is developed to mine the expected scheduling knowledge from the solutions generated by traditional optimization or approximation method for JSSP. Based on this, we show how to use the extracted knowledge as a new dispatching rule to generate complete schedules. A novel method is further developed to combine the extracted knowledge with traditional heuristics to construct new composite dispatching rules which could gain better performance. Besides, we propose a novel approach to utilize the extracted knowledge to improve a Petri net based branch and bound algorithm used in this paper. A series of experiments is carried out to evaluate the performance of the proposed methods.

    Original languageEnglish
    Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
    PublisherIFAC Secretariat
    Pages800-805
    Number of pages6
    Volume48
    Edition3
    DOIs
    Publication statusPublished - 2015 May 1
    Event15th IFAC Symposium on Information Control Problems in Manufacturing, INCOM 2015 - Ottawa, Canada
    Duration: 2015 May 112015 May 13

    Other

    Other15th IFAC Symposium on Information Control Problems in Manufacturing, INCOM 2015
    CountryCanada
    CityOttawa
    Period15/5/1115/5/13

    Fingerprint

    Scheduling
    Petri nets
    Data mining
    Job shop scheduling
    Composite materials
    Experiments

    Keywords

    • Branch and bound algorithm
    • Data mining
    • Dispatching rule
    • Job shop scheduling
    • Petri net

    ASJC Scopus subject areas

    • Control and Systems Engineering

    Cite this

    Wang, C. L., Rong, G., Weng, W., & Feng, Y. P. (2015). Mining scheduling knowledge for job shop scheduling problem. In IFAC Proceedings Volumes (IFAC-PapersOnline) (3 ed., Vol. 48, pp. 800-805). IFAC Secretariat. https://doi.org/10.1016/j.ifacol.2015.06.181

    Mining scheduling knowledge for job shop scheduling problem. / Wang, C. L.; Rong, G.; Weng, W.; Feng, Y. P.

    IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 48 3. ed. IFAC Secretariat, 2015. p. 800-805.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Wang, CL, Rong, G, Weng, W & Feng, YP 2015, Mining scheduling knowledge for job shop scheduling problem. in IFAC Proceedings Volumes (IFAC-PapersOnline). 3 edn, vol. 48, IFAC Secretariat, pp. 800-805, 15th IFAC Symposium on Information Control Problems in Manufacturing, INCOM 2015, Ottawa, Canada, 15/5/11. https://doi.org/10.1016/j.ifacol.2015.06.181
    Wang CL, Rong G, Weng W, Feng YP. Mining scheduling knowledge for job shop scheduling problem. In IFAC Proceedings Volumes (IFAC-PapersOnline). 3 ed. Vol. 48. IFAC Secretariat. 2015. p. 800-805 https://doi.org/10.1016/j.ifacol.2015.06.181
    Wang, C. L. ; Rong, G. ; Weng, W. ; Feng, Y. P. / Mining scheduling knowledge for job shop scheduling problem. IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 48 3. ed. IFAC Secretariat, 2015. pp. 800-805
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