Advertisement

The layout design in reconfigurable manufacturing systems: a literature review

  • Isabela MaganhaEmail author
  • Cristovao Silva
  • Luis Miguel D. F. Ferreira
ORIGINAL ARTICLE
  • 13 Downloads

Abstract

The layout is an important issue in the design of manufacturing systems. In conventional systems, the layout rarely changes after the initial design. However, as the market demands are changing more frequently, layout configurations must be capable of reconfiguring the arrangement of resources to suit new production requirements, while minimising material handling and relocation costs and maximising savings in material flow and inventory costs. This paper presents a literature review on the layout design of reconfigurable manufacturing systems (RMS), focusing on reconfigurable layouts, which have been attracting increasing attention in recent years. A systematic literature network analysis was applied to identify trends, evolutionary trajectories and key issues that are influencing the development of knowledge in this field of study. The results are analysed and discussed using a bibliometric and a chronological citation network analysis. The major findings of this research includes (1) the layout design of RMS must be integrated in the process of RMS design, which, in turn, should be considered as a cyclic process, instead of divided into phases. (2) The core characteristics of reconfigurability and the layout design cannot be dissociated. (3) Operational performance measures are rarely considered in the reconfigurable layout problem, despite their importance. (4) Optimisation approaches have been widely used to solve the reconfigurable layout problem. However, they might not be the most suitable approach to deal with the uncertainty and variability present in manufacturing environments in which reconfigurable layouts are required. Finally, this paper identifies gaps in the literature and suggests directions for future research.

Keywords

Reconfigurable manufacturing system Layout design Choice of machines Layout problem Systematic literature network analysis 

Notes

Funding information

This research was supported by the Portugal 2020 project DM4Manufacturing POCI-01-0145-FEDER-016418 by UE/FEDER through the program COMPETE2020.

References

  1. 1.
    Koren Y, Gu X, Guo W (2018) Reconfigurable manufacturing systems: principles, design and future trends. Front Mech Eng 13:121–136CrossRefGoogle Scholar
  2. 2.
    Koren Y, Heisel U, Jovane F et al (1999) Reconfigurable manufacturing systems. CIRP Ann Manuf Technol 48:527–540CrossRefGoogle Scholar
  3. 3.
    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 72:403–418CrossRefGoogle Scholar
  4. 4.
    Hasan MA, Sarkis J, Shankar R (2012) Agility and production flow layouts: an analytical decision analysis. Comput Ind Eng 62:898–907CrossRefGoogle Scholar
  5. 5.
    Wang L (2011) Alternative shop-floor re-layout design due to dynamic operation changes. In: ASME-MSEC, pp 1–7Google Scholar
  6. 6.
    Setchi RM, Lagos N (2004) Reconfigurability and reconfigurable manufacturing systems - state-of-the-art review. Ind Informatics, 2004 INDIN ’04 2004 2nd IEEE Int Conf, pp 529–535Google Scholar
  7. 7.
    Koren Y, Shpitalni M (2010) Design of reconfigurable manufacturing systems. J Manuf Syst 29:130–141CrossRefGoogle Scholar
  8. 8.
    Xiaobo Z, Jiancai W, Zhenbi L (2000) A stochastic model of a reconfigurable manufacturing system part 1: a framework. Int J Prod Res 38:2273–2285zbMATHCrossRefGoogle Scholar
  9. 9.
    Youssef AMA, ElMaraghy HA (2008) Availability consideration in the optimal selection of multiple-aspect RMS configurations. Int J Prod Res 46:5849–5882zbMATHCrossRefGoogle Scholar
  10. 10.
    Saxena LK, Jain PK (2012) A model and optimisation approach for reconfigurable manufacturing system configuration design. Int J Prod Res 50:3359–3381CrossRefGoogle Scholar
  11. 11.
    Dahane M, Benyoucef L (2016) An adapted NSGA-II algorithm for a reconfigurable manufacturing system (RMS) design under machines reliability constraints. In: Metaheuristics for production systems. Springer, Cham, pp 93–107Google Scholar
  12. 12.
    Oke A, Abou-El-Hossein K, Theron NJ (2011) The design and development of a reconfigurable manufacturing system. S Afr J Ind Eng 22:121–132Google Scholar
  13. 13.
    Benderbal HH, Dahane M, Benyoucef L (2017) Layout evolution effort for product family in reconfigurable manufacturing system design. IFAC-PapersOnLine 50:10166–10171CrossRefGoogle Scholar
  14. 14.
    Singh SP, Sharma RRK (2006) A review of different approaches to the facility layout problems. Int J Adv Manuf Technol 30:425–433CrossRefGoogle Scholar
  15. 15.
    Abbasi M, Houshmand M (2011) Production planning and performance optimization of reconfigurable manufacturing systems using genetic algorithm. Int J Adv Manuf Technol 54:373–392CrossRefGoogle Scholar
  16. 16.
    Kheirkhah A, Navidi H, Bidgoli MM (2015) Dynamic facility layout problem: a new bilevel formulation and some metaheuristic solution methods. IEEE Trans Eng Manag 62:396–410CrossRefGoogle Scholar
  17. 17.
    Meng G, Heragu SS, Zijm H (2004) Reconfigurable layout problem. Int J Prod Res 42:4709–4729zbMATHCrossRefGoogle Scholar
  18. 18.
    Benjaafar S, Heragu SS, Irani SA (2002) Next generation factory layouts: research challenges and recent progress. Interfaces (Providence) 32:58–76CrossRefGoogle Scholar
  19. 19.
    Baykasoǧlu A (2003) Capability-based distributed layout approach for virtual manufacturing cells. Int J Prod Res 41:2597–2618CrossRefGoogle Scholar
  20. 20.
    Baykasoǧlu A, Göçken M (2010) Capability-based distributed layout and its simulation based analyses. J Intell Manuf 21:471–485CrossRefGoogle Scholar
  21. 21.
    Maganha I, Silva C (2017) A theoretical background for the reconfigurable layout problem. Procedia Manuf 11:2025–2033CrossRefGoogle Scholar
  22. 22.
    Drira A, Pierreval H, Hajri-Gabouj S (2007) Facility layout problems: a survey. Annu Rev Control 31:255–267CrossRefGoogle Scholar
  23. 23.
    Anjos MF, Vieira MVC (2017) Mathematical optimization approaches for facility layout problems: the state-of-the-art and future research directions. Eur J Oper Res 261:1–16MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Hosseini-Nasab H, Fereidouni S, Ghomi SMTF, Fakhrzad MB (2018) Classification of facility layout problems: a review study. Int J Adv Manuf Technol 94:957–977CrossRefGoogle Scholar
  25. 25.
    Askin RG (2013) Contributions to the design and analysis of cellular manufacturing systems. Int J Prod Res 51:6778–6787CrossRefGoogle Scholar
  26. 26.
    Houshyar AN, Leman Z, Moghadam HP et al (2014) Literature review on dynamic cellular manufacturing system. IOP Conf Ser Mater Sci Eng 58:012016CrossRefGoogle Scholar
  27. 27.
    Moslemipour G, Lee TS, Rilling D (2012) A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems. Int J Adv Manuf Technol 60:11–27CrossRefGoogle Scholar
  28. 28.
    Colicchia C, Strozzi F (2012) Supply chain risk management: a new methodology for a systematic literature review. Supply Chain Manag An Int J 17:403–418CrossRefGoogle Scholar
  29. 29.
    Ding Y, Chowdhury GG, Foo S (2001) Bibliometric cartography of information retrieval research by using co-word analysis. Inf Process Manag 37:817–842zbMATHCrossRefGoogle Scholar
  30. 30.
    Fera M, Fruggiero F, Lambiase A et al (2017) The role of uncertainty in supply chains under dynamic modeling. Int J Ind Eng Comput 8:119–140Google Scholar
  31. 31.
    Strozzi F, Colicchia C, Creazza A, Noè C (2017) Literature review on the ‘smart factory’ concept using bibliometric tools. Int J Prod Res 55:1–20CrossRefGoogle Scholar
  32. 32.
    Colicchia C, Creazza A, Noè C, Strozzi F (2019) Information sharing in supply chains: a review of risks and opportunities using the systematic literature network analysis (SLNA). Supply Chain Manag 24:5–21CrossRefGoogle Scholar
  33. 33.
    Vargas SA, Esteves GRT, Maçaira PM et al (2019) Wind power generation: a review and a research agenda. J Clean Prod 218:850–870CrossRefGoogle Scholar
  34. 34.
    Benderbal HH, Dahane M, Benyoucef L (2017) Flexibility-based multi-objective approach for machines selection in reconfigurable manufacturing system (RMS) design under unavailability constraints. Int J Prod Res 55:6033–6051CrossRefGoogle Scholar
  35. 35.
    Nooy W, Mrvar A, Batagelj V (2011) Exploratory social network analysis with Pajek (Strucutural analysis in the social sciences; 34), 2nd ed. Cambridge University PressGoogle Scholar
  36. 36.
    Liu JS, Ly LYY (2012) An integrated approach for main path analysis: development of the Hirsch index as an example. J Am Soc Inf Sci Technol 63:528–542CrossRefGoogle Scholar
  37. 37.
    Abdi MR, Labib AW (2003) A design strategy for reconfigurable manufacturing systems (RMSs) using analytical hierarchical process (AHP): a case study. Int J Prod Res 41:2273–2299CrossRefGoogle Scholar
  38. 38.
    Abdi MR, Labib AW (2004) Grouping and selecting products: the design key of reconfigurable manufacturing systems (RMSs). Int J Prod Res 42:521–546CrossRefGoogle Scholar
  39. 39.
    Youssef AMA, ElMaraghy HA (2006) Modelling and optimization of multiple-aspect RMS configurations. Int J Prod Res 44:4929–4958zbMATHCrossRefGoogle Scholar
  40. 40.
    Youssef AMA, ElMaraghy HA (2007) Optimal configuration selection for reconfigurable manufacturing systems. Int J Flex Manuf Syst 19:67–106zbMATHCrossRefGoogle Scholar
  41. 41.
    Dou JP, Dai X, Meng Z (2009) Graph theory-based approach to optimize single-product flow-line configurations of RMS. Int J Adv Manuf Technol 41:916–931CrossRefGoogle Scholar
  42. 42.
    Dou JP, Dai X, Meng Z (2010) Optimisation for multi-part flow-line configuration of reconfigurable manufacturing system using GA. Int J Prod Res 48:4071–4100zbMATHCrossRefGoogle Scholar
  43. 43.
    Benderbal HH, Dahane M, Benyoucef L (2018) Modularity assessment in reconfigurable manufacturing system (RMS) design: an archived multi-objective simulated annealing-based approach. Int J Adv Manuf Technol 94:729–749CrossRefGoogle Scholar
  44. 44.
    Waltman L, van Eck NJ, Noyons ECM (2010) A unified approach to mapping and clustering of bibliometric networks. J Inf Secur 4:629–635Google Scholar
  45. 45.
    Deif AM, ElMaraghy WH (2006) A systematic design approach for reconfigurable manufacturing systems. In: Advances in design. Springer, London, pp 219–228CrossRefzbMATHGoogle Scholar
  46. 46.
    Benkamoun N, Huyet A-L, Kouiss K (2013) Reconfigurable assembly system configuration design approaches for product change. In: 5th Industrial Engineering and Systems Management, pp 1–8Google Scholar
  47. 47.
    Andersen AL, Brunoe TD, Nielsen K, Rösiö C (2017) Towards a generic design method for reconfigurable manufacturing systems: analysis and synthesis of current design methods and evaluation of supportive tools. J Manuf Syst 42:179–195CrossRefGoogle Scholar
  48. 48.
    Rabbani M, Samavati M, Ziaee MS, Rafiei H (2014) Reconfigurable dynamic cellular manufacturing system: a new bi-objective mathematical model. RAIRO-Rech Opér 48:75–102Google Scholar
  49. 49.
    Lee S, Tilbury DM (2007) Deadlock-free resource allocation control for a reconfigurable manufacturing system with serial and parallel configuration. IEEE Trans Syst Man Cybern Part C Appl Rev 37:1373–1381CrossRefGoogle Scholar
  50. 50.
    Padayachee J, Bright G (2014) Synthesis of evolving cells for reconfigurable manufacturing systems. IOP Conf Ser Mater Sci Eng 65:12009–12017CrossRefGoogle Scholar
  51. 51.
    Niroomand I, Kuzgunkaya O, Bulgak AA (2014) The effect of system configuration and ramp-up time on manufacturing system acquisition under uncertain demand. Comput Ind Eng 73:61–74CrossRefGoogle Scholar
  52. 52.
    Koren Y, Wang W, Gu X (2016) Value creation through design for scalability of reconfigurable manufacturing systems. Int J Prod Res 55:1227–1242CrossRefGoogle Scholar
  53. 53.
    Bruccoleri M, Renna P, Perrone G (2005) Reconfiguration: a key to handle exceptions and performance deteriorations in manufacturing operations. Int J Prod Res 43:4125–4145CrossRefGoogle Scholar
  54. 54.
    Aljuneidi T, Bulgak AA (2016) A mathematical model for designing reconfigurable cellular hybrid manufacturing-remanufacturing systems. Int J Adv Manuf Technol 87:1585–1596CrossRefGoogle Scholar
  55. 55.
    Galan R, Racero J, Eguia I, Canca D (2007) A methodology for facilitating reconfiguration in manufacturing: the move towards reconfigurable manufacturing systems. Int J Adv Manuf Technol 33:345–353CrossRefGoogle Scholar
  56. 56.
    Singh RK, Khilwani N, Tiwari MK (2007) Justification for the selection of a reconfigurable manufacturing system: a fuzzy analytical hierarchy based approach. Int J Prod Res 45:3165–3190zbMATHCrossRefGoogle Scholar
  57. 57.
    Li A, Lv C, Xu L (2007) Analysis and research of system configuration and economic evaluation of reconfigurable manufacturing system. 2007 IEEE Int Conf Robot Biomimetics, ROBIO, pp 1727–1732Google Scholar
  58. 58.
    Spicer P, Carlo HJ (2007) Integrating reconfiguration cost into the design of multi-period scalable reconfigurable manufacturing systems. J Manuf Sci Eng 129:202CrossRefGoogle Scholar
  59. 59.
    Dou JP, Dai X, Meng Z (2009) Precedence graph-oriented approach to optimise single-product flow-line configurations of reconfigurable manufacturing system. Int J Comput Integr Manuf 22:923–940CrossRefGoogle Scholar
  60. 60.
    Bensmaine A, Dahane M, Benyoucef L (2013) A non dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Comput Ind Eng 66:519–524CrossRefGoogle Scholar
  61. 61.
    Benderbal HH, Dahane M, Benyoucef L (2015) A new robustness index for machines selection in reconfigurable manufacturing system. Proc 2015 Int Conf Ind Eng Syst Manag IEEE IESM, pp 1019–1026Google Scholar
  62. 62.
    Goyal KK, Jain PK, Jain M (2013) A novel methodology to measure the responsiveness of RMTs in reconfigurable manufacturing system. J Manuf Syst 32:724–730CrossRefGoogle Scholar
  63. 63.
    Molina A, Rodriguez CA, Ahuett H et al (2005) Next-generation manufacturing systems: key research issues in developing and integrating reconfigurable and intelligent machines. Int J Comput Integr Manuf 18:525–536CrossRefGoogle Scholar
  64. 64.
    Goyal KK, Jain PK, Jain M (2012) Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. Int J Prod Res 50:4175–4191CrossRefGoogle Scholar
  65. 65.
    Eguia I, Molina JC, Lozano S, Racero J (2017) Cell design and multi-period machine loading in cellular reconfigurable manufacturing systems with alternative routing. Int J Prod Res 55:2775–2790CrossRefGoogle Scholar
  66. 66.
    Xiaobo Z, Jiancai W, Zhenbi L (2000) A stochastic model of a reconfigurable manufacturing system part 2: optimal configurations. Int J Prod Res 38:747–758zbMATHGoogle Scholar
  67. 67.
    Cedeno-Campos VM, Trodden PA, Dodd TJ, Heley J (2013) Highly flexible self-reconfigurable systems for rapid layout formation to offer manufacturing services. In: IEEE International Conference on Systems, Man, and Cybernetics, pp 4819–4824Google Scholar
  68. 68.
    Zheng L, Zhu L, Wang B, Bai L (2013) A simulation analysis of facility layout problems in reconfigurable manufacturing systems. In: International Conference on Computer Sciences and Applications. pp 423–427Google Scholar
  69. 69.
    Kuo C-H (2001) Resource allocation and performance evaluation of the reconfigurable manufacturing systems. In: International Conference on Systems, Man and Cybernetics, pp 2451–2456Google Scholar
  70. 70.
    Azevedo MM, Crispim JA, Sousa JP (2013) Flexible and reconfigurable layouts in complex manufacturing systems. IFIP AICT 397:484–493Google Scholar
  71. 71.
    Azevedo MM, Crispim JA, Sousa JP (2016) Layout design and reconfiguration in a collaborative manufacturing network. In: IFIP AICT. Springer, Cham, pp 545–556Google Scholar
  72. 72.
    Azevedo MM, Crispim JA, Sousa JP (2017) A dynamic multi-objective approach for the reconfigurable multi-facility layout problem. J Manuf Syst 42:140–152CrossRefGoogle Scholar
  73. 73.
    Purnomo MRA, Wiwoho YS (2016) Multi-objective mixed integer programming approach for facility layout design by considering closeness ratings, material handling, and re-layout cost. IOP Conf Ser Mater Sci Eng 105:012045CrossRefGoogle Scholar
  74. 74.
    Guan X, Dai X, Qiu B, Li J (2012) A revised electromagnetism-like mechanism for layout design of reconfigurable manufacturing system. Comput Ind Eng 63:98–108CrossRefGoogle Scholar
  75. 75.
    Bejlegaard M, Brunoe TD, Nielsen K, Bossen J (2015) Machine-part formation enabling reconfigurable manufacturing systems configuration design: line balancing problem for low volume and high variety. In: Managing Complexity : Proceedings of the 8th World Conference on Mass Customization, Personalization, and Co-Creation (MCPC 2015), pp 139–146Google Scholar
  76. 76.
    Ren C, Barlotti C, Cohen Y et al (2015) Re-layout of an assembly area: a case study at Bosch Rexroth oil control. Assem Autom 35:94–103CrossRefGoogle Scholar
  77. 77.
    Vitayasak S, Pongcharoen P (2015) Re-layout and robust machine layout design under stochastic demand. Appl Mech Mater 789–790:1252–1257CrossRefGoogle Scholar
  78. 78.
    Lacksonen TA, Chao-Yen H (1998) Project scheduling algorithms for re-layout projects. IIE Trans 30:91–99CrossRefGoogle Scholar
  79. 79.
    Ferrari E, Pareschi A, Persona A, Regattieri A (2003) Plant layout computerised design: logistic and relayout program (LRP). Int J Adv Manuf Technol 21:917–922CrossRefGoogle Scholar
  80. 80.
    Keshavarzmanesh S, Wang L, Feng H-Y (2010) A hybrid approach for dynamic routing planning in an automated assembly shop. Robot Comput Integr Manuf 26:768–777CrossRefGoogle Scholar
  81. 81.
    Maniraj M, Pakkirisamy V, Jeyapaul R (2017) An ant colony optimization-based approach for a single-product flow-line reconfigurable manufacturing systems. Proc Inst Mech Eng B J Eng Manuf 231:1229–1236CrossRefGoogle Scholar
  82. 82.
    Dou J, Dai X, Ma X, Meng Z (2008) A GA-based approach to optimize single-product flow-line configurations of RMS. In: Proceedings of 2008 IEEE International Conference on Mechatronics and Automation, pp 654–659Google Scholar
  83. 83.
    Kant R, Pandey V, Pattanaik LN (2017) An NSGA II-based approach for optimization of reconfigurable cellular manufacturing system. In: Advances in intelligent systems and computing. Springer, Singapore, pp 57–66Google Scholar
  84. 84.
    Qiu RG, Mcdonnell P, Joshi S, Russell DW (2005) A heuristic game theoretic approach to resource sharing in reconfigurable manufacturing. Int J Adv Manuf Technol 25:78–87CrossRefGoogle Scholar
  85. 85.
    Fan S, Li J, Catherine A, Pan M (2011) GASD based relayout method in mass customization manufacturing quality assurance. Adv Mater Res 225–226:368–371Google Scholar
  86. 86.
    Wang G, Yan Y, Zhang X, et al (2008) A simulation optimization approach for facility layout problem. In: International Conference on Industrial Engineering and Engineering Management, pp 734–738Google Scholar
  87. 87.
    Jiang S, Ong SK, Nee AYC (2014) An AR-based hybrid approach for facility layout planning and evaluation for existing shop floors. Int J Adv Manuf Technol 72:457–473CrossRefGoogle Scholar
  88. 88.
    Yamada Y, Lei J (2006) Reconfiguration process design of a reconfigurable manufacturing system. IMACS Multiconference Computational Eng Syst Appl 1012–1019Google Scholar
  89. 89.
    Yamada Y (2006) Dynamic reconfiguration of reconfigurable manufacturing systems using particle swarm optimization. In: International Conference on Robotics and Automation, pp 1444–1449Google Scholar
  90. 90.
    Kia R, Baboli A, Javadian N et al (2012) Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing. Comput Oper Res 39:2642–2658MathSciNetzbMATHCrossRefGoogle Scholar
  91. 91.
    Kia R, Javadian N, Paydar MM, Saidi-Mehrabad M (2013) A simulated annealing for intra-cell layout design of dynamic cellular manufacturing systems with route selection, purchasing machines and cell reconfiguration. Asia Pacific J Oper Res 30:1350004MathSciNetzbMATHCrossRefGoogle Scholar
  92. 92.
    Shafigh F, Defersha FM, Moussa SE (2017) A linear programming embedded simulated annealing in the design of distributed layout with production planning and systems reconfiguration. Int J Adv Manuf Technol 88:1119–1140CrossRefGoogle Scholar
  93. 93.
    Kulturel-Konak S, Smith AE, Norman BA (2007) Bi-objective facility expansion and relayout considering monuments. IIE Trans 39:747–761CrossRefGoogle Scholar
  94. 94.
    Giordani S, Lujak M, Martinelli F (2009) A decentralized scheduling policy for a dynamically reconfigurable production system. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer, Berlin Heidelberg, pp 102–113Google Scholar
  95. 95.
    Leitao P, Barbosa J, Trentesaux D (2012) Bio-inspired multi-agent systems for reconfigurable manufacturing systems. Eng Appl Artif Intell 25:934–944CrossRefGoogle Scholar
  96. 96.
    Hsieh FS (2018) Design of scalable agent-based reconfigurable manufacturing systems with Petri nets. Int J Comput Integr Manuf 31:748–759CrossRefGoogle Scholar
  97. 97.
    AlGeddawy T, ElMaraghy HA (2010) Design of single assembly line for the delayed differentiation of product variants. Flex Serv Manuf J 22:163–182zbMATHCrossRefGoogle Scholar
  98. 98.
    AlGeddawy T, ElMaraghy HA (2010) Assembly systems layout design model for delayed products differentiation. Int J Prod Res 48:5281–5305zbMATHCrossRefGoogle Scholar
  99. 99.
    Wu ZJ, Ning RX (2006) Design and application of virtual reconfigurable manufacturing system. In: Proceedings of the 13th International Conference on Industrial Engineering and Engineering Management, pp 763–767Google Scholar
  100. 100.
    Ming D, Fei L, Forest H et al (2007) Multi-objective layout optimization in dynamic environments: a heuristic approach. Int Conf Manag 05:159–164Google Scholar
  101. 101.
    Lin HW, Murata T (2010) Decision support for the dynamic reconfiguration of machine layout and part routing in cellular manufacturing. In: Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010Google Scholar
  102. 102.
    Lv C, Li AP, Xu LY (2010) Research and optimization of reconfigurable manufacturing system configuration based on system reliability. Kybernetes 39:1058–1065zbMATHCrossRefGoogle Scholar
  103. 103.
    Abdi MR, Labib AW (2004) Feasibility study of the tactical design justification for reconfigurable manufacturing systems using the fuzzy analytical hierarchical process. Int J Prod Res 42:3055–3076zbMATHCrossRefGoogle Scholar
  104. 104.
    Al-Zaher A, Elmaraghy W, Pasek ZJ (2013) RMS design methodology for automotive framing systems BIW. J Manuf Syst 32:436–448CrossRefGoogle Scholar
  105. 105.
    Izquierdo LE, Hu SJ, Du H et al (2009) Robust fixture layout design for a product family assembled in a multistage reconfigurable line. J Manuf Sci Eng 131:041008CrossRefGoogle Scholar
  106. 106.
    Jefferson TG, Benardos P, Ratchev S (2016) Reconfigurable assembly system design methodology: a wing assembly case study. Int J Mater Manuf 9:31–48CrossRefGoogle Scholar
  107. 107.
    Kamrani AK (2003) A template-based engineering methodology for integrated product design and reconfigurable manufacturing layout. Int J Ind Eng Theory Appl Pract 10:147–156Google Scholar
  108. 108.
    Kochhar JS, Heragu SS (1999) Facility layout design in a changing environment. Int J Prod Res 37:2429–2446zbMATHCrossRefGoogle Scholar
  109. 109.
    Li J, Dai X, Meng Z et al (2009) Rapid design and reconfiguration of Petri net models for reconfigurable manufacturing cells with improved net rewriting systems and activity diagrams. Comput Ind Eng 57:1431–1451CrossRefGoogle Scholar
  110. 110.
    Moghaddam SK, Houshmand M, Valilai OF (2018) Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL). Int J Prod Res 56:3932–3954CrossRefGoogle Scholar
  111. 111.
    Unglert J, Becker JJ, Hoekstra S (2016) Computational design synthesis of reconfigurable cellular manufacturing systems: a design engineering model. Procedia CIRP 57:374–379CrossRefGoogle Scholar
  112. 112.
    Unglert J, Hoekstra S, Becker JJ, van Houten F (2016) Towards decision-support for reconfigurable manufacturing systems based on computational design synthesis. Procedia CIRP 41:153–158CrossRefGoogle Scholar
  113. 113.
    Unglert J, Hoekstra S, Becker JJ (2016) Supporting the design of reconfigurable cellular manufacturing systems by computational design synthesis. In: DS 84: Proceedings of the DESIGN 2016 14th International Design Conference, pp 1417–1426Google Scholar
  114. 114.
    Zhang S, Li Y, Bilberg A, Hadar R (2014) Design and evaluation of a reconfigurable manufacturing system. In: Lecture Notes in Production Engineering: Twenty Years of Mass Customization – Towards New Frontiers, pp 115–127Google Scholar
  115. 115.
    Benama Y, Alix T, Perry N (2014) Reconfigurable manufacturing system design: the case of mobile manufacturing system. In: Advances in Production Management Systems (Ajaccio; 2014), pp 1–8Google Scholar
  116. 116.
    Dou J, Dai X, Meng Z (2007) Optimization for flow-line configurations of RMS based on graph theory. Proc 2007 IEEE Int Conf Mechatronics Autom ICMA 2007 1261–1266Google Scholar
  117. 117.
    Farid AM (2013) An axiomatic design approach to production path enumeration in reconfigurable manufacturing systems. 2013 IEEE Int Conf Syst Man, Cybern, pp 3862–3869Google Scholar
  118. 118.
    Guerra-Zubiaga D, Rosas R, Camacho R, Molina A (2005) Information models to support reconfigurable manufacturing system design. In: Bouras A, Gurumoorthy B, Sudarsan R (eds) Product lifecycle management: emerging solutions and challenges for global networked enterprises. Inderscience Enterprises Limited, Genève, pp 55–63Google Scholar
  119. 119.
    Huang L, Gao Y, Qian F et al (2010) Configuration selection for reconfigurable manufacturing systems by means of characteristic state space. Chinese J Mech Eng 23:1–10CrossRefGoogle Scholar
  120. 120.
    Kahloul L, Bourekkache S, Djouani K (2016) Designing reconfigurable manufacturing systems using reconfigurable object Petri nets. Int J Comput Integr Manuf 29:889–906CrossRefGoogle Scholar
  121. 121.
    Lamotte FF, Berruet P, Philippe JL (2006) Evaluation of reconfigurable manufacturing systems configurations using tolerance criteria. IECON Proc Industrial Electron Conf pp 3715–3720Google Scholar
  122. 122.
    Chao L, Aiping L, Liyun X (2007) The research of performance evaluation system on manufacturing system with reconfigurable configuration. Proc 2007 IEEE Int Conf Mechatronics Autom ICMA 2007 pp 1005–1010Google Scholar
  123. 123.
    Orozco OJL, Lastra JLM (2007) Analysis and design of a distributed model of coordination control for reconfigurable manufacturing systems. In: Proceedings of the 13th IASTED International Conference on Robotics and Applications, pp 537–542Google Scholar
  124. 124.
    Tang Y, Qiu RG (2004) Integrated design approach for virtual production line-based reconfigurable manufacturing systems. Int J Prod Res 42:3803–3822zbMATHCrossRefGoogle Scholar
  125. 125.
    Wang Q, Lassalle S, Mileham AR, Owen GW (2009) Analysis of a linear walking worker line using a combination of computer simulation and mathematical modeling approaches. J Manuf Syst 28:64–70CrossRefGoogle Scholar
  126. 126.
    Baqai A, Shafiq A (2013) Dimensional analysis of the generated design solutions for reconfigurable manufacturing system. Int Mech Eng Congr Expo IMECE pp 1–7Google Scholar
  127. 127.
    Schmidt KW (2013) Optimal configuration changes for reconfigurable manufacturing systems. 52nd IEEE Conf Decis Control 16:7621–7626Google Scholar
  128. 128.
    Yamada Y, Ookoudo K, Komura Y (2003) Layout optimization of manufacturing cells and allocation optimization of transport robots in reconfigurable manufacturing systems using particle swarm optimization. Proc 2003 IEEE/RSJ Int Conf Intell Robot Syst 2:2049–2054Google Scholar
  129. 129.
    Zheng P, Wang H, Sang Z et al (2018) Smart manufacturing systems for industry 4.0: conceptual framework, scenarios, and future perspectives. Front Mech Eng 13:137–150CrossRefGoogle Scholar
  130. 130.
    Scholz S, Mueller T, Plasch M, Limbeck H, Adamietz R, Iseringhausen T, Kimmig D, Dickerhof M, Woegerer C (2016) A modular flexible scalable and reconfigurable system for manufacturing of microsystems based on additive manufacturing and e-printing. Robot Comput Integr Manuf 40:14–23CrossRefGoogle Scholar
  131. 131.
    Wang L (2011) Combining facility layout redesign and dynamic routing for job-shop assembly operations. In: International Symposium on Assembly and Manufacturing, ISAM 2011, pp 1–6Google Scholar
  132. 132.
    Singh A, Gupta S, Asjad M, Gupta P (2017) Reconfigurable manufacturing systems: journey and the road ahead. Int J Syst Assur Eng Manag 8:1849–1857CrossRefGoogle Scholar
  133. 133.
    Maganha I, Silva C, Ferreira LMDF (2018) Understanding reconfigurability of manufacturing systems: an empirical analysis. J Manuf Syst 48:120–130CrossRefGoogle Scholar
  134. 134.
    Bortolini M, Galizia FG, Mora C (2018) Reconfigurable manufacturing systems: literature review and research trend. J Manuf Syst 49:93–106CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.CEMMPRE, Department of Mechanical EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.CEMMPRE-CeBER, Department of Mechanical EngineeringUniversity of CoimbraCoimbraPortugal

Personalised recommendations