全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Condition Based Maintenance Optimization of an Aircraft Assembly Process Considering Multiple Objectives

DOI: 10.1155/2014/204546

Full-Text   Cite this paper   Add to My Lib

Abstract:

The Commercial Aircraft Cooperation of China (COMAC) ARJ21 fuselage’s final assembly process is used as a case study. The focus of this paper is on the condition based maintenance regime for the (semi-) automatic assembly machines and how they impact the throughput of the fuselage assembly process. The fuselage assembly process is modeled and analyzed by using agent based simulation in this paper. The agent approach allows complex process interactions of assembly, equipment, and maintenance to be captured and empirically studied. In this paper, the built network is modeled as the sequence of activities in each stage, which are parameterized by activity lead time and equipment used. A scatter search is used to find multiobjective optimal solutions for the CBM regime, where the maintenance related cost and production rate are the optimization objectives. In this paper, in order to ease computation intensity caused by running multiple simulations during the optimization and to simplify a multiobjective formulation, multiple Min-Max weightings are used to trace Pareto front. The empirical analysis reviews the trade-offs between the production rate and maintenance cost and how sensitive the design solution is to the uncertainties. 1. Introduction Nowadays, aircraft manufacturers are operating in a global competitive environment. Increasing production rate and reducing costs are the key drivers in aircraft manufacturing. In order to meet the required production rate while meeting high quality requirements, (semi-) automatic assembly machines (e.g., Flexible Drilling Head [1], GRAWDE (Gear Rib Automated Wing Drilling Equipment), and HAWED (Horizontal Automated Wing Drilling Equipment) [2]) are increasingly being used in the aircraft assembly line. These machines can deliver significant productivity gains on the shop floor by reducing the manual multistep processes and overcoming the restricted worker access [3]. This in effect has shifted the production throughput to be now very much dependent on the operational availability of these (semi-) automatic machines [4]. Consequently, machine breakdowns and maintenance are therefore a major cause of bottlenecks in the assembly line. How to manage these machines in an efficient and cost-effective way to maximize the overall product rate is still a key challenge to the aircraft manufacturers [2]. Maintenance involves fixing when equipment becomes out of order (corrective maintenance) and also includes performing routine actions which will keep the equipment working in order or prevent failures from arising (i.e.,

References

[1]  MTorres, Total Solution for Aircraft Automatic Assembly Jigs, MTorres, Santa Ana, Calif, USA, 2013.
[2]  P. Lute, An Investigation of Airbus A380 Stage 01 Wing Box Assembly Using Discrete Event Simulation, Cranfield University, Bedfordshire, UK, 2007.
[3]  Electroimpact Inc., A380 Stage 1 GRAWDE Machine, Electroimpact Inc., Mukilteo, Wash, USA, 2003.
[4]  IBM Corporation, Predictive Maintenance for Manufacturing, 2011.
[5]  Wikipedia, “Preventive maintenance,” Wikipedia, http://en.wikipedia.org/wiki/Preventive_maintenance.
[6]  ResolveFM, “Preventive/corrective maintenance,” ResolveFM, http://www.resolve.com.au/.
[7]  A. Kelly, Maintenance Strategy, Butterworth-Heinemann, Oxford, UK, 1997.
[8]  NACE, “Maintenance strategies,” 2013, http://www.nace.org/, http://events.nace.org/library/corrosion/Inspection/Strategies.asp.
[9]  I. Grigoryev, AnyLogic 6 in Three Days: A Quick Course in Simulation Modeling, Anylogic North America, 2012.
[10]  J. B. Leger, E. Neunreuthe, B. Iung, and G. Morel, “Integration of the predictive maintenance in manufacturing system,” in Advanced in Manufacturing, pp. 133–144, Springer, London, UK, 1999.
[11]  Z. Tian, D. Lin, and B. Wu, “Condition based maintenance optimization considering multiple objectives,” Journal of Intelligent Manufacturing, vol. 23, no. 2, pp. 333–340, 2009.
[12]  J. Yulan, J. Zuhua, and H. Wenrui, “Multi-objective integrated optimization research on preventive maintenance planning and production scheduling for a single machine,” International Journal of Advanced Manufacturing Technology, vol. 39, no. 9-10, pp. 954–964, 2008.
[13]  D. Achermann, Modelling, Simulation and Optimization of Maintenance Strategies Under Consideration of Logistic Processes, Südwestdeutscher, 2008.
[14]  ?. Val?uha, A. Goti, J. úradní?ek, and I. Navarro, “Multi-equipment condition based maintenance optimization by multi-objective genetic algorithm,” Journal of Achievements in Materials and Manufacturing Engineering, vol. 45, no. 2, pp. 188–193, 2011.
[15]  L. Tautou and H. Pierreval, “Using evolutionary algorithms and simulation for the optimization of manufacturing systems,” IIE Transactions, vol. 29, no. 3, pp. 181–189, 1997.
[16]  D. Baglee, “Maintenance strategy development in the UK food and drink industry,” International Journal of Strategic Engineering Asset Management, vol. 1, no. 3, pp. 289–300, 2013.
[17]  J. Reimann, G. Kacprzynski, D. Cabral, and R. Marini, “Using condition based maintenance to improve the profitability of performance based logistic contracts,” in Proceedings of the Annual Conference of the Prognostics and Health Management Society, 2009.
[18]  A. Grall, C. Bérenguer, and L. Dieulle, “A condition-based maintenance policy for stochastically deteriorating systems,” Reliability Engineering and System Safety, vol. 76, no. 2, pp. 167–180, 2002.
[19]  M. Rolón and E. Martínez, “Agent-based modeling and simulation of an autonomic manufacturing execution system,” Computers in Industry, vol. 63, no. 1, pp. 53–78, 2012.
[20]  W. Shen, Q. Hao, H. J. Yoon, and D. H. Norrie, “Applications of agent-based systems in intelligent manufacturing: an updated review,” Advanced Engineering Informatics, vol. 20, no. 4, pp. 415–431, 2006.
[21]  M. A. Majid, U. Aickelin, and P. O. Siebers, “Comparing simulation output accuracy of discrete event and agent based models: a quantitave approach,” in Proceedings of the Summer Computer Simulation Conference (SCSC '09), Vista, Calif, USA, 2009.
[22]  P. O. Siebers, C. M. MacAl, J. Garnett, D. Buxton, and M. Pidd, “Discrete-event simulation is dead, long live agent-based simulation!,” Journal of Simulation, vol. 4, no. 3, pp. 204–210, 2010.
[23]  L. Holst, Integrating Discrete-Event Simulation into the Manufacturing System Development Process, Division of Robotics, Lund, Sweden, 2001.
[24]  J. A. B. Montevechi, R. d. C. Miranda, and J. D. Friend, “Sensitivity analysis in discrete-event simulation using design of experiments,” in Discrete Event Simulations-Development and Applications, InTech, Rijeka, Croatia, 2012.
[25]  Y. Carson and A. Maria, “Simulation optimization: methods and applications,” in Proceedings of the Winter Simulation Conference, pp. 118–126, Atlanta, Ga, USA, December 1997.
[26]  R. Martí and M. Laguna, “Scatter search: basic design and advanced strategies,” Revista Iberoamericana de Inteligencia Artificial, vol. 7, no. 19, pp. 123–130, 2003.
[27]  A. Ghosh and S. Dehuri, “Evolution algorithms for multi-criterion optimization: a survey,” International Journey of Computing and Information Sciences, vol. 2, no. 1, 2004.
[28]  M. Laguna, R. Martí, M. Gallego, and A. Duarte, “The scatter search methodology,” in Wiley Encyclopedia of Operations Research and Management Science, Wiley-Blackwell, Hoboken, NJ, USA, 2011.
[29]  COMAC, “ARJ21 regional jet program,” 2013, http://english.comac.cc/products/rj/pi2/index.shtml.
[30]  Electroimpact, Flex Track, Electroimpact, Mukilteo, Wash, USA, 2013.
[31]  IEEE/PES Task Force on Impact of Maintenance Strategy on Reliability of the Reliability, Risk and Probability Applications Subcommittee, S. Aboresheid, R. N. Allan et al., “The present status of maintenance strategies and the impact of maintenance on reliability,” IEEE Transactions on Power Systems, vol. 16, no. 4, pp. 638–646, 2001.
[32]  M. Kaegi, R. Mock, and W. Kr?ger, “Analyzing maintenance strategies by agent-based simulations: a feasibility study,” Reliability Engineering and System Safety, vol. 94, no. 9, pp. 1416–1421, 2009.
[33]  A. Borshchev, Designing State-Based Behavior: Statecharts, Anylogic, 2013.
[34]  Process Engineering Group, Introduction to SSm, Instituto de Investigaciones Marinas (C.S.I.C.), Vigo, Spain, 2009.
[35]  J. April, F. Glover, J. P. Kelly, and M. Laguna, “Practical introduction to simulation optimization,” in Proceedings of the Winter Simulation Conference, pp. 71–78, Boulder, Colo, USA, December 2003.
[36]  W. Abo-Hamad and A. Arisha, “Simulation optimisation methods in supply chain applications: a review,” Irish Journal of Management, vol. 30, no. 2, pp. 95–124, 2011.
[37]  C.-H. Chen and L. H. Lee, “Introduction to stochastic simulation optimization,” in Stochastic Simulation Optimization: An Optimal Computing Budget Allocation, System Engineering and Operations Research, World Scientific, Hackensack, NJ, USA, 2010.
[38]  M. Laguna, OptQuest: Optimization of Complex Systems, OptTek Systems, 2011.
[39]  R. Martí, M. Laguna, and F. Glover, “Principles of scatter search,” European Journal of Operational Research, vol. 169, no. 2, pp. 359–372, 2006.
[40]  OptTek, “How the OptQuest engine works,” OptTek, http://www.opttek.com.
[41]  F. Glover and A. Reinholz, “Metaheuristics in science and industry: new developments,” in Proceedings of the Metaheuristics International Conference, Montreal, Canada, June 2007.
[42]  I. Boussa?d, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristics,” Information Sciences, vol. 237, pp. 82–117, 2013.
[43]  K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, Wiley-Blackwell, Chichester, UK, 2001.
[44]  M. Gen and R. Cheng, Genetic Algorithms and Engineering Optimization, John Wiley & Sons, New York, NY, USA, 2000.
[45]  A. Ghosh and S. Dehui, “Evolutionary algorithms for multi-criterion optimization: a survey,” International Journal of Computing and Information Sciences, vol. 2, no. 1, pp. 38–57, 2004.
[46]  E. J. Hughes, “Multiple single objective pareto sampling,” Evolutionary Computation, vol. 4, pp. 2678–2684, 2003.
[47]  T. Screenuch, A. Tsourdos, E. J. Hughes, and B. A. White, “Fuzzy gain-scheduled missile autopilot design using evolutionary algorithms,” IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 4, pp. 1323–1339, 2006.
[48]  Y. Jin and J. Branke, “Evolutionary optimization in uncertain environments—a survey,” IEEE Transactions on Evolutionary Computation, vol. 9, no. 3, pp. 303–317, 2005.
[49]  J. E. Fieldsend and R. M. Everson, “Multi-objective optimisation in the presence of uncertainty,” in Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 243–250, Edinburgh, UK, September 2005.
[50]  OptTek, “Multi-objective optimization,” OptTek, http://www.opttek.com.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413