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An integer programming model for discovering associations between manufacturing system capabilities and product features

Author

Listed:
  • Mohamed Kashkoush

    (University of Windsor)

  • Hoda ElMaraghy

    (University of Windsor)

Abstract

Valuable implicit knowledge and patterns are accumulated over time in industrial databases at various stages of product development and production. An example of such hidden patterns would be the cutting tool which is typically used to produce a profiling feature in a given steel part. Discovering and interpreting such patterns would be useful in supporting and optimizing the operations and planning activities such as process planning and manufacturing systems synthesis. A novel knowledge discovery model is introduced to extract useful correlations between the manufacturing domain and design domain based on historical manufacturing data. An Integer Programming model is developed, for the first time, to extract association rules between sets of various product features and manufacturing capabilities used in their production. These associations identify the specific manufacturing system capabilities associated with (i.e. typically used for) the production of each product feature. The discovered knowledge is then used to synthesize the required manufacturing system capabilities for new products with new combinations of features. The proposed IP model was demonstrated using a case study of seven instances of machined parts and the corresponding milling machines used to produce them. The advantages of the proposed association rule discovery IP model were also demonstrated by comparing it with existing association rule discovery methods. The proposed model is simple and easy to implement and automate. Utilizing the proposed model in manufacturing system synthesis should greatly assist in speeding-up product development and manufacturing systems design and re-design.

Suggested Citation

  • Mohamed Kashkoush & Hoda ElMaraghy, 2017. "An integer programming model for discovering associations between manufacturing system capabilities and product features," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 1031-1044, April.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:4:d:10.1007_s10845-015-1044-6
    DOI: 10.1007/s10845-015-1044-6
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    References listed on IDEAS

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    1. S. Altuntas & T. Dereli & H. Selim, 2013. "Fuzzy weighted association rule based solution approaches to facility layout problem in cellular manufacturing system," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 15(3), pages 253-271.
    2. J. Jiao & L. Zhang & Y. Zhang & S. Pokharel, 2008. "Association rule mining for product and process variety mapping," Post-Print hal-00799025, HAL.
    3. Beasley, J. E. & Chu, P. C., 1996. "A genetic algorithm for the set covering problem," European Journal of Operational Research, Elsevier, vol. 94(2), pages 392-404, October.
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