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Workflow balancing in parallel machines through genetic algorithm

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Abstract

Workflow balancing helps to remove the bottlenecks present in a manufacturing system. A genetic algorithm (GA) is used to solve the parallel machine scheduling problem of the manufacturing system with the objective of workflow balancing. The performance of GA is compared with three workflow balancing strategies namely random (RANDOM), shortest processing time (SPT) and longest processing time (LPT). The relative percentage of imbalance (RPI) is adopted among parallel machines for evaluating the performance of these heuristics. The GA shows better performance for the combination of various job sizes and machines. A computer program has been coded on an IBM/PC compatible system in the C++ language for experimentation to a standard manufacturing system environment in operation.

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Acknowledgements

The authors thank two anonymous referees for their extensive and valuable comments.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Sri Ramakrishna Mission Vidyalaya Polytechnic College, Coimbatore, 641 020, India

    S. Rajakumar

  2. Department of Mechanical Engineering, Government College of Technology, Coimbatore, 641 013, India

    V. P. Arunachalam

  3. Department of Mechanical Engineering, Coimbatore Institute of Technology, Coimbatore, 641 014, India

    V. Selladurai

Authors
  1. S. Rajakumar
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  2. V. P. Arunachalam
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  3. V. Selladurai
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Correspondence to S. Rajakumar.

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Rajakumar, S., Arunachalam, V.P. & Selladurai, V. Workflow balancing in parallel machines through genetic algorithm. Int J Adv Manuf Technol 33, 1212–1221 (2007). https://doi.org/10.1007/s00170-006-0553-z

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  • DOI: https://doi.org/10.1007/s00170-006-0553-z

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