Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

Authors

  • Patrick Ruane Johnson & Johnson Vision Care, Rivers, V94 N732 Limerick, Ireland // Technological University of the Shannon, Moylish, V94 EC5T, Limerick, Ireland
  • Patrick Walsh Technological University of the Shannon, Moylish, V94 EC5T, Limerick, Ireland
  • John Cosgrove Technological University of the Shannon, Moylish, V94 EC5T, Limerick, Ireland

DOI:

https://doi.org/10.14513/actatechjaur.00668

Keywords:

Digital Model, Digitalization, Genetic Algorithm, JaamSim, Optimization, Simulation

Abstract

Simulation in manufacturing is often applied in situations where conducting experiments on a real system is very difficult often because of cost or the time to carry out the experiment is too long. Optimization is the organized search for such designs and operating modes to find the best available solution from a set of feasible solutions. It determines the set of actions or elements that must be implemented to achieve an optimized manufacturing line. As a result of being able to concurrently simulate and optimize equipment processes, the understanding of how the actual production system will perform under varying conditions is achieved. The author has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated tray loader manufacturing system in the Johnson & Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates how a digital model developed using JaamSim was integrated with an author developed genetic algorithm optimization system and how both tools can be used for the optimization and development of an automated manufacturing line in the medical devices industry.

Downloads

Download data is not yet available.

References

W. Erwin Diewert, “The New Palgrave Dictionary of Economics,” Palgrave Macmillan UK, 27 April 2017. [Online]. https://link.springer.com/referenceworkentry/10.1057/978-1-349-95121-5_659-2.

S. Boyd and L. Vandenberghe, Convex Optimization, vol. 7, Cambridge University Press, 2009.

S. Amaran, N. Sahinidis, B. Sharda and S. Bury, “Simulation Optimization: A Review of Algorithms and Applications,” Annals of Operations Research, vol. 240, pp. 351-380, 2016. https://doi.org/10.1007/s10479-015-2019-x

R. Nance and R. Sargent, “Perspectives on the Evolution of Simulation,” Operations Research, vol. 50, no. 1, pp. 161-172, 2002. https://doi.org/10.1287/opre.50.1.161.17790

Juan, J. Faulin, S. Grasman, M. Rabe and G. Figueira, “A review of Simheuristics: Extending Metaheuristics to deal with Stochastic Combinatorial Optimization Problems,” Operations Research Perspectives, vol. 2, pp. 62-72, 2015. https://doi.org/10.1016/j.orp.2015.03.001

K. Sörensen and F. Glover, “Metaheuristics,” In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA (2013) pp. 960-970. https://doi.org/10.1007/978-1-4419-1153-7_1167

King, D.H, and H.S Harrison. 2013. “Open Source Simulation Software “JaamSim”.” Proceedings of the 2013 Winter Simulation Conference. Washington, DC, USA. https://doi.org/10.1109/WSC.2013.6721593

N. Gunantara and Q. Ai, “A review of multi-objective optimization: Methods and its applications.,” Cogent Engineering, vol. 5, no. 1, pp. 1 - 16, 2018. https://doi.org/10.1080/23311916.2018.1502242

J. Pelamatti, L. Brevault, M. Balesdent, E. Talbi and Y. Guerin, “Efficient global optimization of constrained mixed variable problems.,” Journal of Global Optimization, vol. 73, no. 3, pp. 583-613, 2019. https://doi.org/10.1007/s10898-018-0715-1

L. Bianchi, M. Dorigo and L. Gambardella, “A Survey on Metaheuristics for Stochastic Combinatorial Optimization,” 2009. https://doi.org/10.1007/s11047-008-9098-4

C. Blum and A. Roli, “Metaheuristics in Combinatorial Optimization Overview and Conceptual Comparison,” ACM Computing Surveys, vol. 35, no. 3, pp. 268-308, 2003. https://doi.org/10.1145/937503.937505

F. Castro, C. Gutierrez-Antonio, A. Briones-Ramirez and J. Herandez, “Genetic Algorithms: A tool for Optimizing Intensified Distillation Sequences,” Genetic Algorithms: Advances in Research and Applications, pp. 1-17, 2017.

P. Chu and J. Beasley, “A Genetic Algorithm for the Generalised Assignment Problem,” Computers & Operations Research, vol. 24, no. 1, pp. 17-23, 1997. https://doi.org/10.1016/S0305-0548(96)00032-9

D. Goldberg and J. Holland, “Genetic Algorithms and Machine Learning,” Machine Learning, vol. 3, no. 2-3, pp. 95-99, 1988. https://doi.org/10.1023/A:1022602019183

M. Dalle Mura and G. Dini, “A Multi-Objective Software Tool for Manual Assembly Line Balancing using a Genetic Algorithm,” CIRP Journal of Manufacturing Science and Technology, vol. 19, pp. 72-83, 2017. http://dx.doi.org/10.1016/j.cirpj.2017.06.002

R. Haupt and S. Haupt, Practical Genetic Algorithms, New York, NY, USA: John Wiley & Sons, 2004. https://doi.org/10.1002/0471671746

M. Hamza, H. Yap and I. A. Choudhury, “Genetic Algorithm and Particle Swarm Optimization Based Cascade Interval Type 2 Fuzzy PD Controller for Rotary Inverted Pendulum System.,” Mathematical Problems in Engineering, vol. 2015, pp. 1 - 15, 2015. https://doi.org/10.1155/2015/695965

P. Rajendran and K. Yit Yok, “The Optimization of a Genetic Algorithm for Unmanned Aerial Vehicle Path Planning,” Genetic Algorithms: Advances in Research and Applications, pp. 19 - 32, 2017.

K. Kok, P. Rajendran, R. Rainis, I. Wan and M. Wan Mohd, “Investigation on selection schemes and population sizes for genetic algorithm in unmanned aerial vehicle path planning.,” International Symposium on Technology Management & Emerging Technologies (ISTMET), pp. 6 - 10, 2015. https://doi.org/10.1109/ISTMET.2015.7358990

K. Yit Kok, P. Rajendran, R. R and W. Mohd Muhiyuddin Wan Ibrahim, “Investigation on Selection Schemes and Population Sizes for Genetic Algorithm in Unmanned Aerial Vehicle Path Planning,” in 2015. https://doi.org/10.1109/ISTMET.2015.7358990

C. Grosan and M. Oltean, “The Role of Elitism in Multiobjective Optimization with Evolutionary Algorithms,” Acta Universitatis Apulensis 5 (2003) pp. 83-90.

G. Guariso and M. Sangiorgio, “Improving the Performance of Multiobjective Genetic Algorithms: An Elitism-Based Approach,” Information, 11 (12) pp. 1 - 14, 2020. https://doi.org/10.3390/info11120587

Eiben, R. Hinterding and Z. Michalewicz, “Parameter Control in Evolutionary Algorithms,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 124-141, 1999. https://doi.org/10.1109/4235.771166

N. Srinivas and K. Deb, “Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,” Evolutionary Computation, p. 22 1– 248, 1994. https://doi.org/10.1162/evco.1994.2.3.221

K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182 - 197, 07 August 2002. https://doi.org/10.1109/4235.996017

T. Goel, R. Vaidyanathan, R. Haftka, W. Shyy, N. Queipo and K. Tucker, “Response Surface Approximation of Pareto Optimal Front in Multi-Objective Optimization,” Computer Methods in Applied Mechanics and Engineering, vol. 196, no. 4, pp. 879 - 893, 2007. https://doi.org/10.1016/j.cma.2006.07.010

C. Leung and H. Lau, “Simulation-based optimization for material handling systems in manufacturing and distribution industries,” Wireless Networks, p. 4839 – 4860, 2020. https://doi.org/10.1007/s11276-018-1894-x

N. Mahammed, M. B. S, O. A and M. Fahsi, “Evolutionary Business Process Optimization using a Multiple-Criteria Decision Analysis method,” in International Conference on Computer, Information and Telecommunication Systems (CITS), 2018. https://doi.org/10.1109/CITS.2018.8440166

Y. Yusoff, M. Salihin Ngadiman and A. Mohd Zain, “Overview of NSGA-II for Optimizing Machining Process Parameters,” Procedia Engineering 15, p. 3978 – 3983, 2011. https://doi.org/10.1016/j.proeng.2011.08.745

Ruiz, S. Martínez, J. Rocha, J. Villanueva, J. Menchaca, M. Berrones, M. Flores and A. Pineda, “Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences,” Symmetry 2021, vol. 13, no. 2, 20 Febuary 2021. https://doi.org/10.3390/sym13020344

Y. Chang, Z. Bouzarkouna and D. Devegowda, “Multi Objective Optimization for Rapid and Robust Optimal Oil Field Development Under Geological Uncertainty,” Computational Geosciences, vol. 19, p. 933 – 950, 2015. https://doi.org/10.1007/s10596-015-9507-6

H. Kumar and S. Yadav, “Hybrid NSGA‑II Based Decision Making in Fuzzy Multi Objective Reliability Optimization Problem,” SN Applied Sciences, 2019. https://doi.org/10.1007/s42452-019-1512-2

P. K. Davis, “Generalizing Concepts and Methods of Verification, Validation and Accreditation (VV&A) for Military Simulations,” National Defense Research Institute, pp. 5-6, 1992.

A. M. Law, "How to Build Valid and Credible Simulation Models," 2019 Winter Simulation Conference (WSC), 2019, pp. 1402-1414. https://doi.org/10.1109/WSC40007.2019.9004789

J. P. Kleijnen, “Theory and Methodology of Verification and validation of simulation models,” European Journal of Operational Research, vol. 82, pp. 145-162, 1995.

H. Hunter-Zinck, A. de Siqueira, V. Vásquez, R. Barnes and C. Martinez, “Ten Simple Rules on Writing Clean and Reliable Open-Source Scientific Software,” PLOS Computational Biology, pp. 1 - 9, 2021. https://doi.org/10.1371/journal.pcbi.1009481

J. Blank and K. Deb, “Pymoo - Multi-objective Optimization in Python,” IEEE Access, vol. 8, pp. 89497 - 89509, 2020. https://doi.org/10.1109/ACCESS.2020.2990567

Blank, J, and K Deb. 2020. “A Running Performance Metric and Termination Criterion for Evaluating Multi and Many Objective Optimization Algorithms.” 2020 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 9. https://doi.org/10.1109/CEC48606.2020.9185546

Lim, S.M, A.B Sultan, N Sulaiman, A Mustapha, and K.Y Leong. 2017. “Crossover and Mutation Operators of Genetic Algorithms.” International Journal of Machine Learning and Computing 7 (1). https://doi.org/10.18178/ijmlc.2017.7.1.611

Duc Tran, Khoa. 2005. “Elitist non-dominated sorting GA-II (NSGA-II) as a parameter-less multi-objective genetic algorithm.” Proceedings. IEEE Southeast Conference. Ft. Lauderdale, FL, USA. pp. 359 - 367. https://doi.org/10.1109/SECON.2005.1423273

Samsuri, S, R Ahmad, M Zakaria, and M Zain. 2019. “Parameter Tuning for Comparing Multi-Objective Evolutionary Algorithms Applied to System Identification Problems.” Proc. of the 2019 IEEE 6th International Conference on Smart Instrumentation, Measurement and Applications. Kuala Lumpur, Malaysia. https://doi.org/10.1109/ICSIMA47653.2019.9057333

Arin, A, G Rabadi, and R Unal. 2011. “Comparative studies on design of experiments for tuning parameters in a genetic algorithm for a scheduling problem.” International Journal of Experimental Design and Process Optimisation 102-124. https://doi.org/10.1504/IJEDPO.2011.040262

Badduri, J, R.A Srivatsan, Kumar G.S, and S Bandyopadhyay. 2012. “Coupler-Curve Synthesis of a Planar Four-Bar Mechanism Using NSGA-II.” Asia-Pacific Conference on Simulated Evolution and Learning. 460-469. https://doi.org/10.1007/978-3-642-34859-4_46

Cao, Z, and Z Zhang. 2010. “Parameter Settings of Genetic Algorithm Based on Multi-Factor Analysis of Variance.” 2010 Fourth International Conference on Genetic and Evolutionary Computing. Shenzhen, China. 305 - 307. https://doi.org/10.1109/ICGEC.2010.82

Deb, K. 2011. “Multi-Objective Optimization Using Evolutionary Algorithms.” Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, PIN 208016, India, 1 - 24. Accessed January 10, 2022. https://www.egr.msu.edu/~kdeb/papers/k2011003.pdf

Deb, K, and H Beyer. 2001. “Self Adaptive Genetic Algorithms with Simulated Binary Crossover.” Evolutionary Computation 9 (2): 197 - 221. https://doi.org/10.1162/106365601750190406

M. Jeong, J. H. Choi and B. H. Koh, “Performance evaluation of modified genetic and swarm‐based optimization algorithms,” Structural Control and Health Monitoring, p. 878–889, 2013. https://doi.org/10.1002/stc.507

J. Shen and Y. Zhu, “Chance-Constrained Model for Uncertain Job Shop Scheduling Problem,” Soft Computing - A Fusion of Foundations, Methodologies & Applications., vol. 20, no. 6, pp. 2383-2391, June 2016. https://doi.org/10.1007/s00500-015-1647-z

G. Shao, S. Jain, C. Laroque, L. H. Lee, P. Lendermann and O. Rose, "Digital Twin for Smart Manufacturing: The Simulation Aspect," 2019 Winter Simulation Conference (WSC), 2019, pp. 2085-2098. https://doi.org/10.1109/WSC40007.2019.9004659

Q. Qi, F. Tao, T. Hu, N. Anwer, A. Liu, Y. Wei and L. Wang, “Enabling Technologies and Tools for Digital Twin,” Journal of Manufacturing Systems, vol. 58, pp. 3-21, 2021.

Downloads

Published

2022-08-31

How to Cite

Ruane, P., Walsh, P., & Cosgrove, J. (2022). Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line. Acta Technica Jaurinensis, 15(3), 174–187. https://doi.org/10.14513/actatechjaur.00668

Issue

Section

Research articles