Robust Design Based on Cost-Quality Model in Micro-Manufacturing

• Autorzy: Han Yunxia , Xi Wende , Yang Shijuan , Wang Weillu , Wu Jiawei

Identyfikatory

DOI

10.17531/ein/190380

• Języki publikacji: EN

Abstrakty

EN

This paper proposes a novel total cost model for the micro‐products' entire life cycle that takes into account the uncertainty of the model parameters. The total cost includes pre-sale manufacturing and post-sale warranty costs. Additionally, different marketing strategies are also given based on the weight of internal and external costs. Furthermore, limited data and unknown effects in experiments may cause large errors in parameter estimates. This could prevent the achievement of reliable designs. To address this, robust optimization and interval estimation are used. This approach reduces the impact of uncertainty on parameter estimates. It ensures optimality and robustness in micro-manufacturing parameters. Example analysis and numerical simulation results show that the proposed method assists companies in selecting the optimal manufacturing parameter level that aligns with their marketing strategies. Besides, considering uncertainty factors can ensure that the optimization results remain guaranteed, even under the worst-case scenarios.

Słowa kluczowe

EN

model parameter uncertainty; interval estimation; warranty cost; economic quality design; manufacturing cost

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 3
• Strony: art. no. 190380
• Opis fizyczny: Bibliogr. 43 poz., tab., wykr.

Twórcy

autor

Han Yunxia

• Business School, Yangzhou University, China

• https://orcid.org/0009-0007-4968-182X

autor

Xi Wende

• Business School, Yangzhou University, China

• https://orcid.org/0009-0000-1443-7383

autor

Yang Shijuan

• School of Business, Anhui University, China

• https://orcid.org/0000-0002-8856-231X

autor

Wang Weillu

• Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, China

• https://orcid.org/0000-0003-1058-2908

autor

Wu Jiawei

• School of Information Management, Jiangxi University of Finance and Economics, China

• https://orcid.org/0009-0001-6755-1419

Bibliografia

• 1. Ouyang L, Zhou D, Park C, et al. Ensemble modelling technique for a micro-drilling process based on a two-stage bootstrap. Engineering Optimization 2019; 51(3): 503-519, https://doi.org/10.1080/0305215X.2018.1472251.
• 2. Wang J, Mao T, Tu Y. Simultaneous multi-response optimisation for parameter and tolerance design using Bayesian modelling method. International Journal of Production Research 2021; 59(8): 2269-2293, https://doi.org/10.1080/00207543. 2020.1730011.
• 3. Lu J C, Jeng S L, Wang K. A review of statistical methods for quality improvement and control in nanotechnology. Journal of Quality Technology 2009; 41(2): 148-164, https://doi.org/10.1080/00224065.2009.11917770.
• 4. Robinson T J, Borror C M, Myers R H. Robust parameter design: a review. Quality and reliability engineering international 2004; 20(1): 81-101, https://doi.org/10. 1002/qre.602.
• 5. Yang S, Wang J, Wu J, et al. Modeling and optimization for multiple correlated responses with distribution variability. IISE Transactions 2023; 55(5): 480-495, https://doi.org/10.1080/24725854.2022.2067915.
• 6. Ouyang L, Ma Y, Wang J, et al. An interval programming model for continuous improvement in micro-manufacturing. Engineering Optimization 2018; 50(3): 400-414, https://doi.org/10.1080/0305215X.2017.1317765.
• 7. Taguchi, G., and D. Clausing. Robust Quality. Harvard Business Review 1990; 68(1):65-75.
• 8. Hassan J S. External failure cost estimation using reliability models: an alternative to Taguchi’s loss function. Pennsylvania State University 2009.
• 9. Huang H Z, Liu Z J, Murthy D N P. Optimal reliability, warranty and price for new products. IIE Transactions 2007; 39(8): 819-827, https://doi.org/10.1080/0740 8170601091907.
• 10. Karmarkar U S, Pitbladdo R C. Quality, class, and competition. Management Science 1997; 43(1): 27-39, https://doi.org/10.1287/mnsc.43.1.27.
• 11. Kirkizoğlu Z, Karaer Ö. After-sales service and warranty decisions of a durable goods manufacturer. Omega 2022; 113: 102719, https://doi.org/10.1016/j.omega. 2022.102719.
• 12. Park S, Lee P, Yoo S H. Investigation of product quality and advertising: Government intervention in advertising. Managerial and Decision Economics, 2024, https://doi.org/10.1002/mde.4093.
• 13. Zhang Z, He S, He Z, et al. A systematic warranty-reliability-price decision model for two-dimensional warranted products with heterogeneous usage rates. Computers & Industrial Engineering 2022; 163: 107820, https://doi.org/10.1016/ j.cie.2021.107820.
• 14. Wang X L. Design and pricing of usage-driven customized two-dimensional extended warranty menus. IISE Transactions 2023; 55(9): 873-885, https://doi.org /10. 1080/24725854.2022.2104972.
• 15. Qiao P, Luo M, Ma Y, et al. Optimal warranty option and post-warranty maintenance strategy under a warranty menu: from a consumer perspective. International Journal of Production Research 2024; 62(5): 1586-1608, https:// doi.org/10.1080/00207543.2023.2197513.
• 16. Wang G, Shao M, Lv S, et al. Process parameter optimization for lifetime improvement experiments considering warranty and customer satisfaction. Reliability Engineering & System Safety 2022; 221: 108369, https://doi.org/ 10.1016 /j.ress.2022.108369.
• 17. Cheng Q, Wang S, Yan C. Robust optimal design of chilled water systems in buildings with quantified uncertainty and reliability for minimized life-cycle cost. Energy and Buildings 2016; 126: 159-169, https://doi.org/10.1016/j.enbuild. 2016.05.032.
• 18. Hassan J S. Integrated models and methodologies for parameter and tolerance designs. The Pennsylvania State University 2012.
• 19. Peterson J J. A posterior predictive approach to multiple response surface optimization. Journal of Quality Technology 2004; 36(2): 139-153, https://doi.org /10.1080/00224065.2004.11980261.
• 20. Ng S H. A Bayesian model-averaging approach for multiple-response optimization. Journal of Quality Technology 2010; 42(1): 52-68, https://doi.org/10.1080/00 224065.2010.11917806.
• 21. Tan M H Y, Wu C F J. Robust design optimization with quadratic loss derived from Gaussian process models. Technometrics 2012; 54(1): 51-63, https://doi.org/ 10.1080/00401706.2012.648866.
• 22. Feng Z, Wang J, Zhou X, et al. Robust optimization for functional multiresponse in 3D printing process. Simulation Modelling Practice and Theory 2023; 126: 102774, https://doi.org/10.1016/j.simpat.2023.102774.
• 23. Han Y, Tu Y, Ouyang L, et al. Economic quality design under model uncertainty in micro-drilling manufacturing process. International Journal of Production Research 2022; 60(3): 1086-1104, https://doi.org/10.1080/00207543.2020.185 1792.
• 24. Ouyang L, Dey S, Park C. Development of robust confidence intervals for the cost-based process capability index. Computers & Industrial Engineering 2024; 190: 110048, https://doi.org/10.1016/j.cie.2024.110048.
• 25. Zeybek M. Interval robust design under contaminated and non normal data. Communications in Statistics-Theory and Methods 2020; 49(22): 5406-5418, https:// doi.org/10.1080/03610926.2019.1710198.
• 26. Li A D, He Z, Zhang Y. Robust multi-response optimization considering location effect, dispersion effect, and model uncertainty using hybridization of NSGA-II and direct multi-search. Computers & Industrial Engineering 2022; 169: 108247, https://doi.org/ 10.1016/j.cie.2022.108247.
• 27. Wilcox R R. A note on computing a confidence interval for the mean. Communications in Statistics-Simulation and Computation 2024; 53(1): 164-166, https://doi.org/10. 1080/03610918.2021.2011926.
• 28. Shah K, Abdeljawad T. On complex fractal-fractional order mathematical modeling of CO2 emanations from energy sector. Physica Scripta 2023; 99(1): 015226, https://doi.org/ https://doi.org/10.1080/00207543.2020.1851792.
• 29. Khan Z A, Shah K, Abdalla B, et al. A numerical study of complex dynamics of a chemostat model under fractal-fractional derivative. Fractals 2023; 31(08): 2340181, https://doi.org/10.1080/00207543.2020.1851792.
• 30. Sher M, Shah K, Sarwar M, et al. Mathematical analysis of fractional order alcoholism model. Alexandria Engineering Journal 2023; 78: 281-291, https://doi.org/10.1016/j.aej.2023.07.010.
• 31. Ahmed S, Shah K, Jahan S, et al. An efficient method for the fractional electric circuits based on Fibonacci wavelet. Results in Physics 2023; 52: 106753, https://doi. org/10.1016/j.rinp.2023.106753.
• 32. Blischke W R, Murthy D N P. Product warranty management—I: A taxonomy for warranty policies. European Journal of Operational Research 1992; 62(2): 127-148, https://doi.org/10.1016/0377-2217(92)90242-2.
• 33. Nguyen D G, Murthy D N P. A general model for estimating warranty costs for repairable products. IIE transactions 1984; 16(4): 379-386, https://doi.org/10. 1080/07408178408975258.
• 34. Leemis L. Probability models and statistical methods in reliability, 2nd Edition. Prentice Hall, Englewood Cliffs, New Jersey, 2009.
• 35. Deleveaux V J. Analytical models for the justification of investment in continuous quality improvement. The Pennsylvania State University 1997.
• 36. Blue J. Reliability Functions Incorporating the Mean and the Variance. The Pennsylvania State University 2001.
• 37. Ouyang L, Zhou D, Ma Y, et al. Ensemble modeling based on 0-1 programming in micro-manufacturing process. Computers & Industrial Engineering 2018; 123: 242-253, https://doi.org/10.1016/j.cie.2018.06.020.
• 38. Lv S, Li S, Zhao Y, et al. Integrated parameter and tolerance design for multiple response optimization. Computers & Industrial Engineering 2023; 175: 108908. https://doi.org/10.1016/j.cie.2022.108908.
• 39. Boylan G L, Cho B R. Comparative studies on the high-variability embedded robust parameter design from the perspective of estimators. Computers & Industrial Engineering 2013; 64(1): 442-452, https://doi.org/10.1016/j.cie.2012. 10.012.
• 40. Liu L, Byun J H, Park C, et al. Modified sequential bifurcation for simulation factor screening under skew-normal response model. Computers & Industrial Engineering 2022; 169: 108274, https://doi.org/10.1016/j.cie.2022.108274.
• 41. Shi W, Chen A, Xie X. Generating and validating cluster sampling matrices for model-free factor screening. European Journal of Operational Research 2024; 313(1): 241-257, https://doi.org/10.1016/j.ejor.2023.08.007.
• 42. Rastogi A K, Taterh S, Kumar B S. Dimensionality Reduction Algorithms in Machine Learning: A Theoretical and Experimental Comparison. Engineering Proceedings 2023; 59(1): 82, https://doi.org/10.3390/engproc2023059082.
• 43. Yang Y, Tuong Z K, Yu D. Dimensionality reduction under scrutiny. Nature Computational Science 2023; 3(1): 8-9, https://doi.org/10.1038/s43588-022-00383-1.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).