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Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints

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Abstract

Configuration selection for reconfigurable manufacturing systems (RMS) is one of the key issue that needs to be addressed to transform RMS implementation from its nascent stage to a mature one. The problem is of vital importance because of the fact that the selection of machine configurations for operations to be performed on any job/part is based on multiple objectives, which often conflicts each other. In the present work, a framework for configuration selection for a manufacturing flow line (MFL) is proposed and demonstrated using non-dominated sorting genetic algorithm-II (NSGA-II). The proposed framework is explained with the help of a mathematical illustration. Results are presented in the form of non-dominated solutions obtained for machine configurations encompassing manufacturing of a product using a multi-tage reconfigurable serial product flow line (RSPFL). The outcome of this work would help in improving the performance of RMSs while considering multiple objectives as the selection criterions. The findings are finally discussed in detail in the light of previous works on the topic.

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References

  1. Ashraf M, Hasan F (2016) Product family formation for RMS: a review. In: Khan AA, Khan IA, Ali M (eds) National conference on mechanical engineering-ideas, innovation & initiatives. Excel India Publishers, Aligarh, pp 37–40

    Google Scholar 

  2. Kuzgunkaya O, Elmaraghy HA (2009) Economic and strategic justification of changeable, reconfigurable and flexible manufacturing. In: ElMaraghy HA (ed) Changeable and reconfigurable manufacturing systems. Springer, pp 303–320

  3. Koren Y, Shpitalni M (2011) Design of reconfigurable manufacturing systems. J Manuf Syst 29:130–141. https://doi.org/10.1016/j.jmsy.2011.01.001

    Article  Google Scholar 

  4. Ashraf M, Hasan F (2016) A comprehensive approach for part family formation using production similarities. In: IVth International conference on production and industrial engineering, CPIE-2016. New Delhi. In: pp 1–9

    Google Scholar 

  5. Krygier R (2005) The Integration of Flexible , Reconfigurable Manufacturing with Quality. In: 3rd CIRP International Conference on Reconfigurable Manufacturing. Ann Arbor, Michigan, pp 1–3

  6. Koren Y (2013) The rapid responsiveness of RMS. Int J Prod Res 51:6817–6827. https://doi.org/10.1080/00207543.2013.856528

    Article  Google Scholar 

  7. Koren Y (1999) Reconfigurable machining systems vision with examples. Ann Arbor

  8. Koren Y, Galip A (2002) Reconfigurable manufacturing system having a production capacity method for designing same and method for changing its production capacity

  9. Mehrabi MG, Ulsoy AG, Koren Y (2000) Reconfigurable manufacturing systems: key to future manufacturing. J Intell Manuf 11:403–419. https://doi.org/10.1023/A:1008930403506

    Article  Google Scholar 

  10. Dashchenko AI (2006) Reconfigurable manufacturing systems and transformable factories. Springer, Moscow

    Book  Google Scholar 

  11. Youssef AMA, ElMaraghy HA (2006) Assessment of manufacturing systems reconfiguration smoothness. Int J Adv Manuf Technol 30:174–193. https://doi.org/10.1007/s00170-005-0034-9

    Article  Google Scholar 

  12. Abbasi M, Houshmand M (2011) Production planning and performance optimization of reconfigurable manufacturing systems using genetic algorithm. Int J Adv Manuf Technol 54:373–392. https://doi.org/10.1007/s00170-010-2914-x

    Article  Google Scholar 

  13. Fromherz MPJ, Alto P (2011) Planning and scheduling reconfigurable systems around off-line resources. 2:12–15

  14. Fromherz MPJ, Alto P (2014) Planning and scheduling reconfigurable systemis with regular and dagnostic jobs

  15. Landers RG (2000) A new paradigm in machine tools: reconfigurable machine tools. Engineering 26:1–4

    Google Scholar 

  16. Hasan F, Jain PK, Kumar D (2013) Machine reconfigurability models using multi-attribute utility theory and power function approximation. Procedia Eng 64:1354–1363. https://doi.org/10.1016/j.proeng.2013.09.217

    Article  Google Scholar 

  17. Katz R (2007) Design principles of reconfigurable machines. Int J Adv Manuf Technol 34:430–439. https://doi.org/10.1007/s00170-006-0615-2

    Article  Google Scholar 

  18. Koren Y, Wang W, Gu X (2016) Value creation through design for scalability of reconfigurable manufacturing systems. Int J Prod Res 7543:1–16. https://doi.org/10.1080/00207543.2016.1145821

    Google Scholar 

  19. Hasan F, Jain PK, Kumar D (2014) Optimum configuration selection in reconfigurable manufacturing system involving multiple part families. Opsearch 51:297–311. https://doi.org/10.1007/s12597-013-0146-1

    Article  Google Scholar 

  20. Tzeng G-H, Huang J-J (2011) Multiple attribute decision making. Taylor & Francis Group, London

    MATH  Google Scholar 

  21. Goyal KK, Jain PK, Jain M (2012) Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. Int J Prod Res 50:4175–4191. https://doi.org/10.1080/00207543.2011.599345

    Article  Google Scholar 

  22. Koren Y, Kota S (1999) Reconfigurable machine tool. 35:886–890

  23. Dhupia J, Powalka B, Katz R, Ulsoy AG (2007) Dynamics of the arch-type reconfigurable machine tool. Int J Mach Tools Manuf 47:326–334. https://doi.org/10.1016/j.ijmachtools.2006.03.017

    Article  Google Scholar 

  24. Xiaobo Z, Jiancai W, Zhenbi L (2000) A stochastic model of a reconfigurable manufacturing system part 1: a framework. Int J Prod Res 38:2273–2285. https://doi.org/10.1080/002075400411501

    Article  MATH  Google Scholar 

  25. Hasan F, Jain PK, Kumar D (2014) Service level as performance index for reconfigurable manufacturing system involving multiple part families. Procedia Eng 69:814–821. https://www.ncbi.nlm.nih.gov/nlmcatalog/101541420

  26. Ashraf M, Hasan F (2015) Product family formation based on multiple product similarities for a reconfigurable manufacturing system. Int J Model Oper Manag 5:247–265

    Google Scholar 

  27. Mehmood A, Noor MF, Upadhyay S, Hasan F, Ashraf M (2015) Part family formation using skip moves and lazy machines in a reconfigurable manufacturing system. Int J Model Oper Manag 5:196–208

    Google Scholar 

  28. Ashraf M, Hasan F, Siddiqui IH (2016) Grouping of part /product variants based on operation sequence similarity. New Delhi, pp 9–10

  29. Bensmaine A, Dahane M, Benyoucef L (2012) A non dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Comput Ind Eng 66:519–524. https://doi.org/10.1016/j.cie.2012.09.008

    Article  Google Scholar 

  30. Saxena LK, Jain PK (2012) A model and optimisation approach for reconfigurable manufacturing system configuration design. Int J Prod Res 50:3359–3381. https://doi.org/10.1080/00207543.2011.578161

    Article  Google Scholar 

  31. Limei M, Guoxiu L, Lixing Z (2014) Multi-objective optimal configuration of reconfigurable test platform: a modified discrete particle swarm optimization approach. JNW 9:3502–3509. http://road.issn.org/issn/1796-2056-journal-of-networks-#.WzdCu9VKjIU

  32. Youssef AMA, ElMaraghy HA (2007) Optimal configuration selection for reconfigurable manufacturing systems. Int J Flex Manuf Syst 19:67–106. https://doi.org/10.1007/s10696-007-9020-x

    Article  MATH  Google Scholar 

  33. Youssef AMA, ElMaraghy HA (2006) Modelling and optimization of multiple-aspect RMS configurations. Int J Prod Res 44:4929–4958. https://doi.org/10.1080/00207540600620955

    Article  MATH  Google Scholar 

  34. Maniraj M, Pakkirisamy V, Jeyapaul R (2015) An ant colony optimization-based approach for a single-product flow-line reconfigurable manufacturing systems. Proc Inst Mech Eng Part B J Eng Manuf B:1–8. https://doi.org/10.1177/0954405415585260

  35. Ashraf M, Khan MA, Hasan F, et al (2016) Optimum allocation of machines with multiple objectives using NSGA-II. In: 6th International & 27th All India Manufacturing Technology, Design and Research Conference (AIMTDR-2016). Pune, pp 1723–1728

  36. Hasan F, Jain PK (2015) Genetic Modelling for selecting optimal machine configurations in reconfigurable manufacturing system. Appl Mech Mater 789–790:1229–1239. https://doi.org/10.4028/www.scientific.net/AMM.789-790.1229

    Article  Google Scholar 

  37. Spicer P, Carlo HJ (2007) Integrating reconfiguration cost into the design of multi-period scalable reconfigurable manufacturing systems. J Manuf Sci Eng 129:202. https://doi.org/10.1115/1.2383196

    Article  Google Scholar 

  38. Koren Y, Gu X, Guo W (2017) Choosing the system configuration for high-volume manufacturing. Int J Prod Res 56:1–15. https://doi.org/10.1080/00207543.2017.1387678

    Google Scholar 

  39. Lateef-Ur-Rehman A-U-R (2013) Manufacturing configuration selection using multicriteria decision tool. Int J Adv Manuf Technol 65:625–639. https://doi.org/10.1007/s00170-012-4201-5

    Article  Google Scholar 

  40. Zhakypov Z, Uzunovic T, Nergiz AO, Baran EA, Golubovic E, Sabanovic A (2017) Modular and reconfigurable desktop microfactory for high precision manufacturing. Int J Adv Manuf Technol 90:3749–3759. https://doi.org/10.1007/s00170-016-9689-7

    Article  Google Scholar 

  41. Saliba MA, Zammit D, Azzopardi S (2017) A study on the use of advanced manufacturing technologies by manufacturing firms in a small, geographically isolated, developed economy: the case of Malta. Int J Adv Manuf Technol 89:3691–3707. https://doi.org/10.1007/s00170-016-9294-9

    Article  Google Scholar 

  42. Urbanic RJ, Hedrick RW (2015) A matrix-based framework for assessing machine tool reconfiguration alternatives. Int J Adv Manuf Technol 81:1893–1919. https://doi.org/10.1007/s00170-015-7287-8

    Article  Google Scholar 

  43. Renzi C, Leali F, Cavazzuti M, Andrisano AO (2014) A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems. Int J Adv Manuf Technol 72:403–418. https://doi.org/10.1007/s00170-014-5674-1

    Article  Google Scholar 

  44. Gumasta K, Kumar Gupta S, Benyoucef L, Tiwari MK (2011) Developing a reconfigurability index using multi-attribute utility theory. Int J Prod Res 49:1669–1683. https://doi.org/10.1080/00207540903555536

    Article  Google Scholar 

  45. Hasan F, Jain PK, Kumar D (2014) Prediction of machine reconfigurability using artificial neural network for a reconfigurable serial product flow line. Int J Ind Syst Eng 18:283–305. https://doi.org/10.1504/IJISE.2014.065535

    Google Scholar 

  46. Farid AM (2014) Measures of reconfigurability and its key characteristics in intelligent manufacturing systems. J Intell Manuf 28:353–369. https://doi.org/10.1007/s10845-014-0983-7

    Article  Google Scholar 

  47. Mesa J, Maury H, Arrieta R, Bula A, Riba C (2015) Characterization of modular architecture principles towards reconfiguration: a first approach in its selection process. Int J Adv Manuf Technol 80:221–232. https://doi.org/10.1007/s00170-015-6951-3

    Article  Google Scholar 

  48. Koren Y, Katz R (2003) Reconfigurable apparatus and method for inspection during a manufacturing process

  49. Koren Y, Hill RL (2005) Integrated reconfigurable manufacturing system

  50. Fromherz MPJ, Alto P (2001) Planning and scheduling reconfigurable systems with alternative capabilities. 2:2–6

  51. Makssoud F, Battaïa O, Dolgui A (2014) An exact optimization approach for a transfer line reconfiguration problem. Int J Adv Manuf Technol 72:717–727. https://doi.org/10.1007/s00170-014-5694-x

    Article  Google Scholar 

  52. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  53. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423

    Article  MathSciNet  MATH  Google Scholar 

  54. Zeleny M, Cochrane JL (1973) Multiple criteria decision making. University of South Carolina Press

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Acknowledgements

This work was supported by the Council of Scientific and Industrial Research (CSIR), Human resource development group (HRDG), India under Grant Ack. No. 141542/2K15/1, File No. 09/112(0552) 2K17 EMP-I.

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Correspondence to Masood Ashraf.

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Ashraf, M., Hasan, F. Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints. Int J Adv Manuf Technol 98, 2137–2156 (2018). https://doi.org/10.1007/s00170-018-2361-7

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  • DOI: https://doi.org/10.1007/s00170-018-2361-7

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