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Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing

Author

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  • A. S. Xanthopoulos

    (Democritus University of Thrace)

  • D. E. Koulouriotis

    (Democritus University of Thrace)

Abstract

Most sequencing problems deal with deterministic environments where all information is known in advance. However, in real-world problems multiple sources of uncertainty need to be taken into consideration. To model such a situation, in this article, a dynamic sequencing problem with random arrivals, processing times and due-dates is considered. The examined system is a manufacturing line with multiple job classes and sequence-dependent setups. The performance of the system is measured under the metrics of mean WIP, mean cycle time, mean earliness, mean tardiness, mean absolute lateness, and mean percentage of tardy jobs. Twelve job dispatching rules for solving this problem are considered and evaluated via simulation experiments. A statistically rigorous analysis of the solution approaches is carried out with the use of unsupervised and supervised learning methods. The cluster analysis of the experimental results identified classes of priority rules based on their observed performance. The characteristics of each priority rule class are documented and areas in objective space not covered by existing rules are identified. The functional relationship between sequencing priority rules and performance metrics of the production system was approximated by artificial neural networks. Apart from gaining insight into the mechanics of the sequencing approaches the results of this article can be used (1) as a component for prediction systems of dispatching rule output, (2) as a guideline for building new dispatching heuristic with entirely different characteristics than existing ones, (3) to significantly decrease the length of what-if simulation studies.

Suggested Citation

  • A. S. Xanthopoulos & D. E. Koulouriotis, 2018. "Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 69-91, January.
  • Handle: RePEc:spr:joinma:v:29:y:2018:i:1:d:10.1007_s10845-015-1090-0
    DOI: 10.1007/s10845-015-1090-0
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    References listed on IDEAS

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    1. Alexandros S. Xanthopoulos & Dimitrios E. Koulouriotis, 2012. "Comparing heuristic and evolutionary approaches for minimising the number of tardy jobs and maximum earliness on a single machine," International Journal of Multicriteria Decision Making, Inderscience Enterprises Ltd, vol. 2(2), pages 178-188.
    2. Olafsson, Sigurdur & Li, Xiaonan, 2010. "Learning effective new single machine dispatching rules from optimal scheduling data," International Journal of Production Economics, Elsevier, vol. 128(1), pages 118-126, November.
    3. Chen, Binchao & Matis, Timothy I., 2013. "A flexible dispatching rule for minimizing tardiness in job shop scheduling," International Journal of Production Economics, Elsevier, vol. 141(1), pages 360-365.
    4. Jayamohan, M. S. & Rajendran, Chandrasekharan, 2004. "Development and analysis of cost-based dispatching rules for job shop scheduling," European Journal of Operational Research, Elsevier, vol. 157(2), pages 307-321, September.
    5. Branke, Juergen & Pickardt, Christoph W., 2011. "Evolutionary search for difficult problem instances to support the design of job shop dispatching rules," European Journal of Operational Research, Elsevier, vol. 212(1), pages 22-32, July.
    6. Vinod, V. & Sridharan, R., 2011. "Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system," International Journal of Production Economics, Elsevier, vol. 129(1), pages 127-146, January.
    7. Rajendran, Chandrasekharan & Holthaus, Oliver, 1999. "A comparative study of dispatching rules in dynamic flowshops and jobshops," European Journal of Operational Research, Elsevier, vol. 116(1), pages 156-170, July.
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    Cited by:

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