A Simulation-Based Approach for Designing an Innovative Double Sampling Plan for Two Stages Process

Document Type : Research Paper

Authors

1 Assistant Professor, Department of Industrial Engineering, Arak University, Arak, Iran.

2 Assistant Professor, Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

3 M.Sc., School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

4 Assistant Professor, Department of Industrial Engineering, Qom University of Technology, Qom, Iran.

Abstract

One of the main aspects of the production industry is the optimization of acceptance sampling plans. Sampling plan performance depends on many uncertain factors that are difficult to model, especially in a multi-stage process. Therefore, it requires an innovative procedure to optimize it. This study presents an innovative double sampling plan for a multi-stage process based on discrete event simulation (DES) toward proposing an applicable plan to the inspection of the product whose accepting probability follows the hypergeometric distribution for a finite lot without replacement. This paper focuses on the five economic parameters of a double sampling plan that are determined by minimizing the average sample number (ASN) with the help of DES results and optimization methods. Several experiments based on DES were tested to determine the regression function of the ASN simulation study was carried out using Enterprise Dynamic software (ED). Our economic statistical lot acceptance sampling plan based on minimizing the average sample number has been developed to determine the acceptance parameters including the first acceptance number, first rejection, number, first sample size, second sample size, and second acceptance number in a multi-stage process. According to all runs of the simulation model, we concluded at a 95% level of confidence that ASN ranges from 530.16 to 554.93, which is given in detail in the paper.

Keywords

Main Subjects

References

  1. Abedsoltan, H., Taleizadeh, A. A., & Sarker, R. B. (2022). Optimal production of remanufactured products with collaboration and co-branding of luxury brands. Computers & Industrial Engineering, 172, 108533.
  2. Ahmed, B., Ali, M. M., & Yousof, H. M. (2024). A novel G family for single acceptance sampling plan with application in quality and risk decisions. Annals of Data Science, 11(1), 181-199.
  3. Aslam, M. (2019). A new attribute sampling plan using neutrosophic statistical interval method. Complex & Intelligent Systems, 5(4), 365-370.
  4. Aslam M., Wu Ch., Azam M., Jun Ch. (2013). Variable sampling inspection for resubmitted lots based on process capability index Cpk for normally distributed items. Applied Mathematical Modelling, 37, 667–675.
  5. Balamurali, S., Aslam, M., & Jun, C. H. (2014). A new system of skip-lot sampling plans including resampling.The Scientific World Journal, 2014.
  6. Balamurali, S., Jun, Ch. (2007). Multiple dependent state sampling plans for lot acceptance based on measurement data. European Journal of Operational Research, 1221–1230.
  7. Bhattacharya, R., & Aslam, M. (2020). Generalized multiple dependent state sampling plans in presence of measurement data.IEEE Access, 8, 162775-162784.
  8. Brailsford, S. C., Eldabi, T., Kunc, M., Mustafee, N., & Osorio, A. F. (2019). Hybrid simulation modelling in operational research: A state-of-the-art review.European Journal of Operational Research, 278(3), 721-737.
  9. Chakraborty, N., Balakrishnan, N., & Finkelstein, M. (2024). Optimal precedence tests under single and double-sampling framework. Journal of Computational and Applied Mathematics, 115805.
  10. Chen, W., Hong, W., Zhang, H., Yang, P., & Tang, K. (2022). Multi-Fidelity Simulation Modeling for Discrete Event Simulation: An Optimization Perspective. IEEE Transactions on Automation Science and Engineering, 20(2), 1156-1169.
  11. Cheng T., Chen Y. (2007). A GA mechanism for optimizing the design of attribute double sampling plan. Automation in Construction, 16, 345–353.
  12. Engin O., Celik A., Kaya I. (2008). A fuzzy approach to define sample size for attributes control chart in multistage processes: an application in engine valve manufacturing process. Applied Soft Computing, 1654-1663.
  13. Fayomi, A., & Khan, K. (2024). A group acceptance sampling plan for ‘another generalized transmuted‐exponential distribution’based on truncated lifetimes. Quality and Reliability Engineering International, 40(1), 145-153.
  14. Kaya, I. (2009). A genetic algorithm approach to determine the sample size for attribute control charts. Information Sciences, 179, 1552–1566.
  15. Gulbay, M., Kahraman, C. (2007). An alternative approach to fuzzy control charts: direct fuzzy approach. Information Sciences, 1463–1480.
  16. Jafari Jozani, M., Mirkamali, S. J. (2010). Improved attribute acceptance sampling plans based on maxima nomination sampling. Journal of Statistical Planning and Inference, 2448–2460.
  17. Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L. K., Young, T. (2010). Simulation in manufacturing and business: A review. European Journal of Operational Research, 1-13.
  18. Kandasamy, S., Subramanian, A., Venugopal, H. (2019). Designing optimal multiple deferred state Mds-1 sampling plan using truncated Poisson distribution. International Journal of Innovative Technology and Explorig Engineering, 8 (11):883–88.
  19. Montgomery, D. (2001). Introduction to Statistical Quality Control (Vol. Fifth ed.). New York: Wiley.
  20. Mourtzis, D. (2020). Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research, 58(7), 1927-1949.
  21. Murthy, S. S. N., Rambabu, Y. (1997). Design and application of Economic process control charts. Defence science journal 147, 45-53.
  22. Rasay, H., Naderkhani, F., & Golmohammadi, A. M. (2020). Designing variable sampling plans based on lifetime performance index under failure censoring reliability tests. Quality Engineering, 32(3), 354-370.
  23. Schilling, E. G., Neubauer, D. V. (2009). Acceptance Sampling in Quality Control, 2nd edition, 2009 by . New York: Chapman & Hall/CRC Press.
  24. Shahabi, A., Raissi, S., Khalili-Damghani, K., & Rafei, M. (2022). An event-driven simulation-optimisation approach to improve the resiliency of operation in a double-track urban rail line. Journal of Simulation, 16(5), 526-545.
  25. Tashkandy, Y., Emam, W., Ali, M. M., Yousof, H. M., & Ahmed, B. (2023). Quality control testing with experimental practical illustrations under the modified lindley distribution using single, double, and multiple acceptance sampling plans. Mathematics, 11(9), 2184.
  26. Tong, X., Wang, Z., Xie, H., Liang, D., Jiang, Z., Li, J., & Li, J. (2011). Designing a two-rank acceptance sampling plan for quality inspection of geospatial data products. Computers & geosciences, 37(10), 1570-1583.
  27. Tong, X., & Wang, Z. (2012). Fuzzy acceptance sampling plans for inspection of geospatial data with ambiguity in quality characteristics.Computers & Geosciences, 48, 256-266.
  28. Wang, T. C. (2023). Developing a flexible and efficient dual sampling system for food quality and safety validation. Food Control, 145, 109483.
  29. Wu, C. W., Darmawan, A., & Liu, S. W. (2023). Stage-independent multiple sampling plan by variables inspection for lot determination based on the process capability index Cpk. International Journal of Production Research, 61(10), 3171-3183.
  30. Wu, C. W., Lee, A. H., Liu, S. W., & Shih, M. H. (2017). Capability-based quick switching sampling system for lot disposition. Applied Mathematical Modelling, 52, 131-144.
  31. Wu, Ch., Aslam, M., Jun, Ch. (2012). Variables sampling inspection scheme for resubmitted lots based on the process capability index Cpk. European Journal of Operational Research, 560-566.
  32. Wu, Ch., Aslam, M., Jun, Ch. (2013). Multiple states repetitive group sampling plans with process loss consideration . Applied Mathematical Modelling, 9063-9075.
  33. Yüksel, D., Kazancoglu, Y., & Sarma, P. R. S. (2022). A multiphase acceptance sampling model by attributes to investigate the production interruptions in batch production within tobacco industry. International Journal of Quality & Reliability Management, 39(3), 836-858.
Advances in Industrial Engineering
Volume 58, Issue 2
December 2024
Pages 341-351

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