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An adaptive neuro-fuzzy inference system for makespan estimation of flexible manufacturing system assembly shop: a case study

Author

Listed:
  • Vineet Jain

    (Amity University Haryana)

  • Tilak Raj

    (YMCA University of Science and Technology)

Abstract

This paper considers the use of combination of neural networks and fuzzy system i.e. adaptive neuro-fuzzy inference system (ANFIS) applied to the n job, m machine real flexible manufacturing system assembly shop problem with the objective of prediction of makespan. Assembly shop makespan is calculated by Nawaz, Enscor, and Ham (NEH) algorithm. On the basis of this algorithm, adaptive neuro-fuzzy inference system model is made to predict the makespan of the jobs. The purpose of this study is to find the makespan estimation in advance if processing time of machines is known. The purpose of this research is to gain the advantage of the capabilities of both Fuzzy systems, which is a rule-based approach and neural network which focus on the network training. This model has been verified by testing and actual data set with the average percentage accuracy achieved is 95.97%. Coefficient of determination and Correlation coefficient is 0.9310 and 0.9649 respectively. The derived values of ANFIS model output are found within the range after being verified practically. Therefore, it can be concluded that makespan calculation of the production system, by the proposed adaptive neuro-fuzzy inference system, can be used as a reliable approach in estimating the makespan of flexible manufacturing system assembly shop.

Suggested Citation

  • Vineet Jain & Tilak Raj, 2018. "An adaptive neuro-fuzzy inference system for makespan estimation of flexible manufacturing system assembly shop: a case study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(6), pages 1302-1314, December.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:6:d:10.1007_s13198-018-0729-6
    DOI: 10.1007/s13198-018-0729-6
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    References listed on IDEAS

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