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Solving flexible job-shop scheduling problem using hybrid particle swarm optimisation algorithm and data mining

Author

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  • S. Karthikeyan
  • P. Asokan
  • S. Nickolas
  • Tom Page

Abstract

Flexible job-shop scheduling problem (FJSSP) is an extension of the classical job-shop scheduling problem that allows an operation to be processed by any machine from a given set along different routes. It is very important in both fields of production management and combinatorial optimisation. This paper presents a new approach based on a hybridisation of the particle swarm optimisation (PSO) algorithm with data mining (DM) technique to solve the multi-objective flexible job-shop scheduling problem. Three minimisation objectives - the maximum completion time, the total workload of machines and the workload of the critical machines are considered simultaneously. In this study, PSO is used to assign operations and to determine the processing order of jobs on machines. The objectives are optimised by data mining technique which extracts the knowledge from the solution sets to find the near optimal solution of combinatorial optimisation problems. The computational results have shown that the proposed method is a feasible and effective approach for the multi-objective flexible job-shop scheduling problems.

Suggested Citation

  • S. Karthikeyan & P. Asokan & S. Nickolas & Tom Page, 2012. "Solving flexible job-shop scheduling problem using hybrid particle swarm optimisation algorithm and data mining," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 26(1/2/3/4), pages 81-103.
    • Handle: RePEc:ids:ijmtma:v:26:y:2012:i:1/2/3/4:p:81-103
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    Cited by:

    1. Mohamed Habib Zahmani & Baghdad Atmani, 2021. "Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation," Journal of Scheduling, Springer, vol. 24(2), pages 175-196, April.

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