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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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    1. H.M. Raaymakers, Wenny & Will M. Bertrand, J. & C. Fransoo, Jan, 2001. "Makespan estimation in batch process industries using aggregate resource and job set characteristics," International Journal of Production Economics, Elsevier, vol. 70(2), pages 145-161, March.
    2. Herbert G. Campbell & Richard A. Dudek & Milton L. Smith, 1970. "A Heuristic Algorithm for the n Job, m Machine Sequencing Problem," Management Science, INFORMS, vol. 16(10), pages 630-637, June.
    3. Mellit, Adel & Kalogirou, Soteris A., 2011. "ANFIS-based modelling for photovoltaic power supply system: A case study," Renewable Energy, Elsevier, vol. 36(1), pages 250-258.
    4. Framinan, Jose M. & Perez-Gonzalez, Paz, 2015. "On heuristic solutions for the stochastic flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 246(2), pages 413-420.
    5. Sabuncuoglu, Ihsan & Gurgun, Burckaan, 1996. "A neural network model for scheduling problems," European Journal of Operational Research, Elsevier, vol. 93(2), pages 288-299, September.
    6. Wilson, Amy D. & King, Russell E. & Wilson, James R., 2004. "Case study on statistically estimating minimum makespan for flow line scheduling problems," European Journal of Operational Research, Elsevier, vol. 155(2), pages 439-454, June.
    7. Taillard, E., 1990. "Some efficient heuristic methods for the flow shop sequencing problem," European Journal of Operational Research, Elsevier, vol. 47(1), pages 65-74, July.
    8. Raaymakers, W. H. M. & Weijters, A. J. M. M., 2003. "Makespan estimation in batch process industries: A comparison between regression analysis and neural networks," European Journal of Operational Research, Elsevier, vol. 145(1), pages 14-30, February.
    9. S. M. Johnson, 1954. "Optimal two‐ and three‐stage production schedules with setup times included," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(1), pages 61-68, March.
    10. Cheng, T. C. E. & Gupta, M. C., 1989. "Survey of scheduling research involving due date determination decisions," European Journal of Operational Research, Elsevier, vol. 38(2), pages 156-166, January.
    11. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    12. Kalczynski, Pawel Jan & Kamburowski, Jerzy, 2007. "On the NEH heuristic for minimizing the makespan in permutation flow shops," Omega, Elsevier, vol. 35(1), pages 53-60, February.
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