Optimization of Quality Control Processes Using the NPGA Genetic Algorithm

• Autorzy: Chmielowiec, Andrzej , Sikorska-Czupryna, Sylwia , Klich, Leszek , Woś, Weronika , Kuraś, Paweł
• Języki publikacji: EN

Abstrakty

EN

In the article, the problem of multi-criteria optimization of quality control mechanisms is analyzed. The presented method assumes the use of the NPGA genetic algorithm to simultaneously manage costs and the level of detecting non-conformities. The main assumption of the presented approach is to treat individual quality control procedures as vectors, whose elements are probability generating functions of defect detection. Each of these procedures generates certain operational costs and covers specific types of defects within its scope. The task of the presented algorithm is to indicate which procedure and to what extent should operate to ensure an appropriate level of non-conformity detection while minimizing costs. The article presents the theoretical foundations of the developed algorithm and the results of its implementation. The software has been developed in C++ with a particular focus on performance aspects. Its essence lies in the implementation of data structures introduced in the theoretical part, as well as methods for their rapid processing. Thanks to this approach, the entire program is scalable and can be used to solve multidimensional optimization problems. The presented approach may also find application in other areas of enterprise management. This will be possible primarily in cases where the effectiveness of procedures or devices is primarily evaluated based on probability. Therefore, the presented methods can provide effective optimization of other areas related to enterprise management.

Słowa kluczowe

EN

quality control; quality management; enterprise management; multi-criteria process optimization; genetic algorithm; NPGA

• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 7
• Strony: 264--276
• Opis fizyczny: Bibliogr. 42 poz., fig., tab.

Twórcy

autor

Chmielowiec, Andrzej

• Faculty of Mechanics and Technology, Rzeszow University of Technology, ul. Kawiatkowskiego 4, 37-450 Stalowa Wola, Poland,

autor

Sikorska-Czupryna, Sylwia

• Faculty of Mechanics and Technology, Rzeszow University of Technology, ul. Kawiatkowskiego 4, 37-450 Stalowa Wola, Poland

autor

Klich, Leszek

• Faculty of Mechanics and Technology, Rzeszow University of Technology, ul. Kawiatkowskiego 4, 37-450 Stalowa Wola, Poland

autor

Woś, Weronika

• Faculty of Mechanics and Technology, Rzeszow University of Technology, ul. Kawiatkowskiego 4, 37-450 Stalowa Wola, Poland

autor

Kuraś, Paweł

• Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, ul. Wincentego Pola 2, 35-959 Rzeszow, Poland

Bibliografia

• 1. Baranovskyi D., Myamlin S., Bulakh M., Podosonov D., Muradian L. Determination of the filler concentration of the composite tape. Applied Sciences. 2022, 12(21), 11044. https://doi.org/10.3390/app122111044.
• 2. Baranovskyi D. and Myamlin S. The criterion of development of processes of the selforganization of subsystems of the second level in tribosystems of diesel engine. Scientific Reports. 2023, 13(1), 5736. https://doi.org/10.1038/s41598-023-33015-3.
• 3. Baranovskyi D., Bulakh M., Michajłyszyn A., Myamlin S., Muradian L. Determination of the risk of failures of locomotive diesel engines in maintenance. Energies. 2023, 16, 4995. https://doi.org/10.3390/en16134995.
• 4. Daneshjo N, Malega P., Drábik P. Techniques for production quality control in the global company. Advances in Science and Technology Research Journal. 2021, 15(1), 174–83. https://doi.org/10.12913/22998624/131558.
• 5. Oborski P. and Wysocki P. Intelligent visual quality control system based on convolutional neural networks for holonic shop floor control of industry 4.0 manufacturing systems. Advances in Science and Technology Research Journal. 2022, 16(2), 89–98. https://doi.org/10.12913/22998624/145503.
• 6. Nikolay K., Mikhail O., Nikolay B., Oleg F., Hayakawa M. Various approaches to problems of multicriterion optimization processes of electric power systems. In Proceedings of 2014 International Symoposium On Electromagnetic Compatibility, Tokyo (EMC’14/Tokyo), 2014, 418–421.
• 7. Al-Zuheri A. Cross-comparison of evolutionary algorithms for optimizing design of sustainable supply chain network under disruption risks. Advances in Science and Technology Research Journal. 2021, 15(4), 342–351. https://doi.org/10.12913/22998624/142213.
• 8. Iwańkowicz R.R. and Sekulski Z. Solving the problem of vehicle routing by evolutionary algorithm. Advances in Science and Technology Research Journal. 2016, 10(29), 97–108. https://doi.org/10.12913/22998624/61937.
• 9. Kaya I. A genetic algorithm approach to determine the sample size for attribute control charts. Information Sciences. 2009, 179(10), 1552–1566. https://doi.org/10.1016/j.ins.2008.09.024.
• 10. Hatjimihail A.T. Genetic algorithms-based design and optimization of statistical quality-control procedures. Clinical Chemistry. 1993, 39(9), 1972–1978. https://doi.org/10.1093/clinchem/39.9.1972.
• 11. Amin M., Ghaly A., Ayad F., Hosny O. Overall schedule optimization using genetic algorithms. In Proceedings of the Canadian Society of Civil Engineering Annual Conference. 2024, 449–461. https://doi.org/10.1007/978-3-031-35471-733.
• 12. Hinojosa M.A., López-Sánchez A.D., Hernández-Díaz A.G., Santana-Quintero L.V. Compromise utilitarian solutions in multi-criteria optimization problems as a guide for evolutionary algorithms. Computers and Operations Research. 2013, 40(5), 1155–1164.
• 13. Goldberg D.E. Genetic algorithms in search, optimization and machine learning. Addison Wesley Longman Publishing Co., Inc., 1989, USA.
• 14. Chambers L. Practical Handbook of Genetic Algorithms: New Frontiers. CRC-Press. 2, 1995. https://doi.org/10.1201/9780429128332.
• 15. Zhou Q., Wan Y., Wong W.K., Loh P.L., Lee Y.C. Semiconductor integrated circuit product quality improvement utilizing genetic algorithm-based optimization technique. Journal of Intelligent Manufacturing, 2009, 20(1), 83–115.
• 16. Duffuaa S.O., Khan M., Daya B. An exploration of the impact of implementing jit and ie techniques for manufacturing quality systems. International Journal of Six Sigma and Competitive Advantage, 2004, 1(1), 95–118.
• 17. Chen Y.S. and Tsai C.H. An optimization approach to multi-characteristic quality control using genetic algorithms. International Journal of Advanced Manufacturing Technology. 2009, 40(7–8), 693–709.
• 18. Sadough Vanini Z.N., Mohammadi K., Khorshidi H.A. A hybrid approach of fuzzy genetic algorithm for optimization of maintenance plan in a quality control system. International Journal of Quality & Reliability Management, 2015, 32(3), 248–268.
• 19. Coello Coello C.A. Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering. 2002, 191(11–12), 1245–1287. https://doi.org/10.1016/S0045-7825(01)00323-1.
• 20. Eiben A.E., Hinterding R., Michalewicz Z. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation. 1999, 3(2), 124–141. https://doi.org/10.1109/4235.771166.
• 21. Von Lücken C., Baran B., Brizuela C.A. A survey on multi-objective evolutionary algorithms for many-objective problems. Computational Optimization and Applications. 2014, 58(3), 707–756. https://doi.org/10.1007/s10589-014-9644-1.
• 22. Coello Coello C.A., Lamont G.B., Van Veldhuizen D.A. Evolutionary algorithms for solving multi-objective problems. Springer, 2007, New York. https://doi.org/10.1007/978-0-387-36797-2.
• 23. Deb K. Multiobjective optimization using evolutionary algorithms. Wiley, New York, 2001.
• 24. Corne D.W. and Knowles J.D. Techniques for highly multiobjective optimisation: Some nondominated points are better than others. In: Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference, New York, Association for Computing Machinery, 773–780, https://doi.org/10.48550/arXiv.0908.3025.
• 25. Farina M. and Amato P. On the optimal solution definition for many-criteria optimization problems. In: Proceedings of Annual Meeting of the North American Fuzzy Information Processing Society. NAFIPS-FLINT. New Orleans, LA, USA, IEEE 2002, 233–238. https://doi.org/10.1109/NAFIPS.2002.1018061.
• 26. Hughes E. Evolutionary many-objective optimisation: Many once or one many? In: 2005 IEE Congress on Evolutionary Computation, Proceedings, 2005, 1, 222–227. https://doi.org/10.1109/CEC.2005.1554688.
• 27. Khare V., Yao X., Deb K. Performance scaling of multi-objective evolutionary algorithms. In: Evolutionary Multi-Criterion Optimization, Berlin, Heidelberg, Springer, 2003, 376–390.
• 28. Knowles J.D and Corne D.W. Quantifying the effects of objective space dimension in evolutionary multiobjective optimization. In: Proceedings of the 4th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2007). Lecture Notes in Computer Science. Berlin, Heidelberg, Springer, 757–771. https://doi.org/10.1007/978-3-540-70928-2 57
• 29. Purshouse R.C. and Fleming P.J. Evolutionary many-objective optimisation: an exploratory analysis. In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003). 2066–2073. https://doi.org/10.1109/CEC.2003.1299927.
• 30. Purshouse R.C. and Fleming P.J. On the evolutionary optimization of many conflicting objectives. IEEE Transactions on Evolutionary Computation, 2007, 11, 770–784. https://doi.org/10.1109/CEC.2003.1299927.
• 31. Ishibuchi H., Tsukamoto N., Nojima Y. Evolutionary many-objective optimization: A short review. IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008, 2419–2426. https://doi.org/10.1109/CEC.2008.4631121.
• 32. López Jaimes. A., Coello Coello C.A., Urías Barrientos A.E. Online objective reduction to deal with many-objective problems. In: Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2009). Lecture Notes in Computer Science, Berlin, Heidelberg. Springer. 423–437. https://doi.org/10.1007/978-3-642-01020-0 34
• 33. Fonseca C.M. and Fleming P.J. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 1995, 3, 1–16. https://doi.org/10.1162/evco.1995.3.1.1
• 34. Mostaghim S., Branke J., Schmeck H. Multi-objective particle swarm optimization on computer grids. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO ’07), New York, ACM Press, 2007, 869–875. https://doi.org/10.1145/1276958.1277127.
• 35. Obayashi S. and Sasaki D. Visualization and data mining of pareto solutions using self-organizing map. In: Proceedings of the 2nd International Conference on Evolutionary Multi-Criterion Optimization (EMO 2003). Lecture Notes in Computer Science, Berlin, Springer, 796–809. https://doi.org/10.1007/3-540-36970-8 56.
• 36. Pryke A., Mostaghim S., Nazemi A. Heatmap visualization of population based multi objective algorithms. In: Proceedings of the 4th International Conference on Evolutionary MultiCriterion Optimization (EMO 2007). Lecture Notes in Computer Science, Berlin, Heidelberg, Springer, 361–375. https://doi.org/10.1007/978-3-540-70928-2 29.
• 37. Walker D., Fieldsend J., Everson R. Visualising many-objective populations. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion (GECCO Companion ’12), New York, USA. Association for Computing Machinery, 2012, 451–458. https://doi.org/10.1145/2330784.2330853.
• 38. Baraldi P., Pedroni N., Zio E. Application of a niched pareto genetic algorithm for selecting features for nuclear transients’ classification. International Journal of Intelligent Systems, 2009, 24, 118–151. https://doi.org/10.1002/int.20328.
• 39. Zitzler E. Evolutionary algorithms for multiobjective optimization: methods and applications. Swiss Federal Institute of Technology, Zurich, 1999.
• 40. Horn J.D., Nafpliotis N., Goldberg D.E. A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, 1994, 82–87.
• 41. Chmielowiec A. and Klich L. Application of python libraries for variance, normal distribution and weibull distribution analysis in diagnosing and operating production systems. Diagnostyka, 2021, 22(4), 89–105. https://doi.org/10.29354/diag/144479.
• 42. Chmielowiec A. Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data. Computational Statistics, 2021, 36(4), 2813–2840. https://doi.org/10.1007/s00180-021-01096-1.

Identyfikatory

DOI

10.12913/22998624/193195