Increased Robustness of Product Sequencing Using Multi-objective Optimization
Almost all manufacturing processes are subject to uncontrollable variations, caused, for example, by human operators or worn-out machines. When optimizing real-world product sequencing problems, it is of importance to find solutions that are robust, that is, whose performance remains relatively unch...
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| Published in | Procedia CIRP Vol. 17; pp. 434 - 439 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
2014
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| Abstract | Almost all manufacturing processes are subject to uncontrollable variations, caused, for example, by human operators or worn-out machines. When optimizing real-world product sequencing problems, it is of importance to find solutions that are robust, that is, whose performance remains relatively unchanged when exposed to uncertain conditions. In this paper, an extension of the traditional method of handling variations through replications is suggested that aims at finding solutions with an increased degree of robustness. The basic idea is to use standard deviation as an additional optimization objective and transform the single-objective problem into a multi-objective problem. Using standard deviation as an additional objective aims to focus the optimization on solutions that exhibit both high performance and high robustness (that is, having low standard deviation). In order to optimize the two objectives simultaneously, a multi-objective evolutionary algorithm is utilized. The proposed method for improved robustness is evaluated using a real-world test case found at the company GKN Aerospace in Sweden. GKN Aerospace manufactures a variety of different components for aircraft engines and aero derivative gas turbines. The company has recently installed a new workshop, and the focus of the study is on the x-ray stations in this workshop. For performing optimizations the company has created a simulation model that realistically mimics the workshop. As an optimization technique, a multi-objective evolutionary algorithm called NSGA-II is being used. The algorithm considers the mean value and standard deviation from replications of the stochastic simulation as objectives, optimizing both of them simultaneously. Results from the study show that the optimization is able to successfully find robust solutions using the proposed method. However, the general increase in algorithm performance expected with the proposed method is absent, and possible reasons for this are discussed in the paper. |
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| AbstractList | Almost all manufacturing processes are subject to uncontrollable variations, caused, for example, by human operators or worn-out machines. When optimizing real-world product sequencing problems, it is of importance to find solutions that are robust, that is, whose performance remains relatively unchanged when exposed to uncertain conditions. In this paper, an extension of the traditional method of handling variations through replications is suggested that aims at finding solutions with an increased degree of robustness. The basic idea is to use standard deviation as an additional optimization objective and transform the single-objective problem into a multi-objective problem. Using standard deviation as an additional objective aims to focus the optimization on solutions that exhibit both high performance and high robustness (that is, having low standard deviation). In order to optimize the two objectives simultaneously, a multi-objective evolutionary algorithm is utilized. The proposed method for improved robustness is evaluated using a real-world test case found at the company GKN Aerospace in Sweden. GKN Aerospace manufactures a variety of different components for aircraft engines and aero derivative gas turbines. The company has recently installed a new workshop, and the focus of the study is on the x-ray stations in this workshop. For performing optimizations the company has created a simulation model that realistically mimics the workshop. As an optimization technique, a multi-objective evolutionary algorithm called NSGA-II is being used. The algorithm considers the mean value and standard deviation from replications of the stochastic simulation as objectives, optimizing both of them simultaneously. Results from the study show that the optimization is able to successfully find robust solutions using the proposed method. However, the general increase in algorithm performance expected with the proposed method is absent, and possible reasons for this are discussed in the paper. Almost all manufacturing processes are subject to uncontrollable variations, caused, for example, by human operators or worn-out machines. When optimizing real-world product sequencing problems, it is of importance to find solutions that are robust, that is, whose performance remains relatively unchanged when exposed to uncertain conditions. In this paper, an extension of the traditional method of handling variations through replications is suggested that aims at finding solutions with an increased degree of robustness. The basic idea is to use standard deviation as an additional optimization objective and transform the single-objective problem into a multi-objective problem. Using standard deviation as an additional objective aims to focus the optimization on solutions that exhibit both high performance and high robustness (that is, having low standard deviation). In order to optimize the two objectives simultaneously, a multi-objective evolutionary algorithm is utilized. The proposed method for improved robustness is evaluated using a real-world test case found at the company GKN Aerospace in Sweden. GKN Aerospace manufactures a variety of different components for aircraft engines and aero derivative gas turbines. The company has recently installed a new workshop, and the focus of the study is on the x-ray stations in this workshop. For performing optimizations the company has created a simulation model that realistically mimics the workshop. As an optimization technique, a multi-objective evolutionary algorithm called NSGA-2 is being used. The algorithm considers the mean value and standard deviation from replications of the stochastic simulation as objectives, optimizing both of them simultaneously. Results from the study show that the optimization is able to successfully find robust solutions using the proposed method. However, the general increase in algorithm performance expected with the proposed method is absent, and possible reasons for this are discussed in the paper. |
| Author | Gustavsson, Patrik Syberfeldt, Anna |
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| Keywords | Evolutionary Algorithm Robustness Multi-objective optimization Product Sequencing |
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| References_xml | – reference: Deb, K. (2004) Multi-Objective Optimization using Evolutionary Algorithms, second edn, JohnWiley & Sons Ltd. – reference: Cagnina, L., Esquivel, S. and Gallard, R. (2004) Particle swarm optimization for sequencing problems: a case study. In Proceedings of Congress on Evolutionary Computation: 536 – 541. – reference: Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2000) A fast and elitist multi-objective genetic algorithm NSGA-II, KanGAL Report 2000001, Indian Institute of Technology Kanpur, India. – reference: Branke, J., Meisel, S. and Schmidt, C. (2007) Simulated annealing in the presence of noise, Journal of Heuristics 14(6): 627-654. – reference: Knowles, J. and Corne, D. (2000) Approximating the non-dominated front using the pareto archived evolution strategy, Evolutionary Computation Journal 8(2): 149-172. – reference: Goldberg, D. (1989) Genetic algorithms in search, optimization and machine learning, Addison-Wesley Publishing Co, Boston,Massachusetts. – reference: Bui, L., Abbass, H. and Essam, D. (2005) Fitness inheritance for noisy evolutionary multi-objective optimization, Proceedings of Genetic and Evolutionary Computation Conference,Washington, DC, USA, pp. 779– 785. – reference: Tan, K. C. and Goh, C. K. (2008) Handling uncertainties in evolutionary multi-objective optimization, Proceedings of IEEE World Congress on Computational Intelligence, Vol. 5050 of Lecture Notes in Computer Science, Springer, Hong Kong, China, pp. 262-292. – reference: Jin, Y. and Branke, J. (2005) Evolutionary optimization in uncertain environments – a survey, IEEE Transactions on Evolutionary Computation 9(3): 303-317. – reference: Zhoua, A., Qub, B-Y., Lic, H, Zhaob, Z., Nagaratnam Suganthanb, P. and Zhangd, Q. (2011) Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 1(1): 32-49. – reference: edn, MIT Press. – reference: Gockel, N. and Drechsler, R. (1997) Influencing parameters of evolutionary algorithms for sequencing problems. In Proceedings of IEEE International Conference on Evolutionary Computation: 575-580. – reference: Horn, J., Nafploitis, N. and Goldberg, D. (1994) A niched pareto genetic algorithm for multi-objective optimisation, Proceedings of the First IEEE Conference on Evolutionary Computation, Orlando, Florida, pp. 82-87. – reference: Bäck, T., Fogel, D. and Michalewicz, Z. (eds) (1997) Handbook of Evolutionary Computation, Oxford University Press. – reference: Tran, K. (2006) An Improved Multi-Objective Evolutionary Algorithm with Adaptable Parameters, PhD thesis, Graduate School of Computer and Information Sciences, Nova Southeastern University. – reference: Büche, D., Stoll, P., Dornberger, R. and Koumoutsakos, P. (2002) An evolutionary algorithm for multi-objective optimization of combustion processes, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 32(4): 460-473. – reference: Fonseca, C. and Fleming, P. (1993) Genetic algorithms for multiobjective optimization: Formulation, discussion, and generalization, Proceedings of the Fifth International Conference on Genetic Algorithms, Urbana- Champaign, Illinois, pp. 416-423. – reference: Cormen, T.H., L. C. R. R. and Stein, C. (2001) Introduction to Algorithms. USA: 2 – ident: 10.1016/j.procir.2014.01.141_bib0005 – ident: 10.1016/j.procir.2014.01.141_bib0045 doi: 10.1007/978-3-540-68860-0_13 – ident: 10.1016/j.procir.2014.01.141_bib0020 doi: 10.1887/0750308958 – ident: 10.1016/j.procir.2014.01.141_bib0035 doi: 10.1007/s10732-007-9058-7 – ident: 10.1016/j.procir.2014.01.141_bib0030 doi: 10.1145/1068009.1068141 – ident: 10.1016/j.procir.2014.01.141_bib0085 – ident: 10.1016/j.procir.2014.01.141_bib0080 – ident: 10.1016/j.procir.2014.01.141_bib0065 – ident: 10.1016/j.procir.2014.01.141_bib0070 doi: 10.1016/j.swevo.2011.03.001 – ident: 10.1016/j.procir.2014.01.141_bib0040 doi: 10.1109/TSMCB.2002.804372 – ident: 10.1016/j.procir.2014.01.141_bib0015 doi: 10.1109/CEC.2004.1330903 – ident: 10.1016/j.procir.2014.01.141_bib0010 doi: 10.1109/ICEC.1997.592376 – ident: 10.1016/j.procir.2014.01.141_bib0060 doi: 10.1162/106365600568167 – ident: 10.1016/j.procir.2014.01.141_bib0050 – ident: 10.1016/j.procir.2014.01.141_bib0055 doi: 10.1109/ICEC.1994.350037 – ident: 10.1016/j.procir.2014.01.141_bib0025 doi: 10.1109/TEVC.2005.846356 – ident: 10.1016/j.procir.2014.01.141_bib0075 doi: 10.1142/9789812702838_0003 |
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| SubjectTerms | Evolutionary Algorithm Multi-objective optimization Product Sequencing Production and Automation Engineering Produktion och automatiseringsteknik Robustness Technology Teknik |
| Title | Increased Robustness of Product Sequencing Using Multi-objective Optimization |
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