Abstract
Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are costeffective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.
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References
Koren Y. Computer Control of Manufacturing Systems. New York: McGraw Hill, 1983
Koren Y, Heisel U, Jovane F, et al. Reconfigurable manufacturing systems. CIRP Annals-Manufacturing Technology, 1999, 48(2): 527–540
Parsons J T, Stulen F L. US Patent 2820187, 1958-01-14
Koren Y. The rapid responsiveness of RMS. International Journal of Production Research, 2013, 51(23–24): 6817–6827
Koren Y, Wang W, Gu X. Value creation through design for scalability of reconfigurable manufacturing systems. International Journal of Production Research, 2017, 55(5): 1227–1242
Koren Y. The Global Manufacturing Revolution: Product-Process-Business Integration and Reconfigurable Systems. Hoboken: John Wiley & Sons, 2010
Garbie I H. DFSME: Design for sustainable manufacturing enterprises (an economic viewpoint). International Journal of Production Research, 2013, 51(2): 479–503
Garbie I H. An analytical technique to model and assess sustainable development index in manufacturing enterprises. International Journal of Production Research, 2014, 52(16): 4876–4915
Koren Y, Shpitalni M. Design of reconfigurable manufacturing systems. Journal of Manufacturing Systems, 2010, 29(4): 130–141
Zhang G, Liu R, Gong L, et al. An analytical comparison on cost and performance among DMS, AMS, FMS and RMS. In: Dashchenko A I, ed. Reconfigurable Manufacturing Systems and Transformable Factories. Berlin: Springer, 2006, 659–673
Singh A, Gupta S, Asjad M, et al. Reconfigurable manufacturing systems: Journey and the road ahead. International Journal of System Assurance Engineering and Management, 2017, 1–9 (in press)
Wang W, Koren Y. Scalability planning for reconfigurable manufacturing systems. Journal of Manufacturing Systems, 2012, 31(2): 83–91
Maier-Speredelozzi V, Koren Y, Hu S J. Convertibility measures for manufacturing systems. CIRP Annals-Manufacturing Technology, 2003, 52(1): 367–370
Gumasta K, Gupta S K, Benyouce L, et al. Developing a reconfigurability index using multi-attribute utility theory. International Journal of Production Research, 2011, 49(6): 1669–1683
Bi Z M, Lang S Y T, Shen W, et al. Reconfigurable manufacturing systems: The state of the art. International Journal of Production Research, 2008, 46(4): 967–992
Bi Z M, Wang L, Lang S T Y. Current status of reconfigurable assembly systems. International Journal of Manufacturing Research, 2007, 2(3): 303–328
Colledani M, Tolio T. A decomposition method to support the reconfiguration/reconfiguration of production systems. CIRP Annals-Manufacturing Technology, 2005, 54(1): 441–444
Li J, Dai X, Meng Z. Automatic reconfiguration of petri net controllers for reconfigurable manufacturing systems with an improved net rewriting system-based approach. IEEE Transactions on Automation Science and Engineering, 2009, 6(1): 156–167
Meng X. Modeling of reconfigurable manufacturing systems based on colored timed object-oriented Petri nets. Journal of Manufacturing Systems, 2010, 29(2–3): 81–90
Zhao X, Wang K, Luo Z. A stochastic model of a reconfigurable manufacturing system Part I: A framework. International Journal of Production Research, 2000, 38(10): 2273–2285
Rösiö C, Säfsten K. Reconfigurable production system design––Theoretical and practical challenges. Journal of Manufacturing Technology Management, 2013, 24(7): 998–1018
Andersen A L, Brunoe T D, Nielsen K, et al. Towards a generic design method for reconfigurable manufacturing systems: Analysis and synthesis of current design methods and evaluation of supportive tools. Journal of Manufacturing Systems, 2017, 42(1): 179–195
Koren Y, Kota S. US Patent 5943750, 1999-08-31
Koren Y, Katz R. US Patent 6567162, 2003-12-24
Koren Y, Ulsoy G. US Patent 6349237, 2002-02-19
Krygier R. The Integration of flexible, reconfigurable manufacturing with quality. In: Proceedings of CIRP 3rd Conference on Reconfigurable Manufacturing. Ann Arbor, 2005
Gadalla M, Xue D. Recent advances in research on reconfigurable machine tools: A literature review. International Journal of Production Research, 2017, 55(5): 1440–1454
Koren Y, Hu S J, Weber T W. Impact of manufacturing system configuration on performance. CIRP Annals-Manufacturing Technology, 1998, 47(1): 369–372
Freiheit T, Shpitalni M, Hu S J, et al. Designing productive manufacturing systems without buffers. CIRP Annals-Manufacturing Technology, 2003, 52(1): 105–108.
Gu X. The impact of maintainability on the manufacturing system architecture. International Journal of Production Research, 2017, 55(15): 4392–4410
Koren Y, Gu X, Guo W. Choosing the system configuration for high-volume manufacturing. International Journal of Production Research, 2017 (in press)
Youssef A M A, El Maraghy H A. Availability consideration in the optimal selection of multiple-aspect RMS configurations. International Journal of Production Research, 2008, 46(21): 5849–5882
Dou J, Dai X, Meng Z. Optimization for multipart flow-line configuration of reconfigurable manufacturing system using GA. International Journal of Production Research, 2010, 48(14): 4071–4100
Goyal K K, Jain P K, Jain M. Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. International Journal of Production Research, 2012, 50(15): 4175–4191
Webbink R F, Hu S J. Automated generation of assembly systemdesign solutions. IEEE Transactions on Automation Science and Engineering, 2005, 2(1): 32–39
Benkamoun N, Huyet A L, Kouiss K. Reconfigurable assembly system configuration design approaches for product change. In: Proceedings of the 2013 IEEE International Conference on Industrial Engineering and Systems Management (IESM). Rabat: IEEE, 2013
Narongwanich W, Duenyas I, Birge J R. Optimal Portfolio of Reconfigurable and Dedicated Capacity Under Uncertainty. Technical Report, University of Michigan ERC-RMS, 2002
Deif A M, El Maraghy W. Effect of reconfiguration costs on planning for capacity scalability in reconfigurable manufacturing systems. International Journal of Flexible Manufacturing Systems, 2006, 18(3): 225–238
Gyulai D, Kádár B, Kovács A, et al. Capacity management for assembly systems with dedicated and reconfigurable resources. CIRP Annals-Manufacturing Technology, 2014, 63(1): 457–460
Renna P. A decision investment model to design manufacturing systems based on a genetic algorithm and Monte-Carlo simulation. International Journal of Computer Integrated Manufacturing, 2017, 30(6): 590–605
Asl F M, Ulsoy A G. Stochastic optimal capacity management in reconfigurable manufacturing systems. CIRP Annals-Manufacturing Technology 2003, 52(1): 371–374
Spicer P, Carlo H J. Integrating reconfiguration cost into the design of multi-period scalable reconfigurable manufacturing systems. Journal of Manufacturing Science and Engineering, 2007, 129(1): 202–210
Carlo H J, Spicer J P, Rivera-Silva A. Simultaneous consideration of scalable-reconfigurable manufacturing system investment and operating costs. Journal of Manufacturing Science and Engineering, 2012, 134(1): 011003
Van Mieghem J A. Investment strategies for flexible resources. Management Science, 1998, 44(8): 1071–1078
Ceryan O, Koren Y. Manufacturing capacity planning strategies. CIRP Annals-Manufacturing Technology, 2009, 58(1): 403–406
Matta A, Tomasella M, Clerici M, et al. Optimal reconfiguration policy to react to product changes. International Journal of Production Research, 2008, 46(10): 2651–2673
Bryan A, Ko J, Hu S J, et al. Co-evolution of product families and assembly systems. CIRP Annals-Manufacturing Technology, 2007, 56(1): 41–44
Matta A, Tomasella M, Valente A. Impact of ramp-up on the optimal capacity-related reconfiguration policy. International Journal of Flexible Manufacturing Systems, 2007, 19(3): 173–194
Niroomand I, Kuzgunkaya O, Bulgak A A. Impact of reconfiguration characteristics for capacity investment strategies in manufacturing systems. International Journal of Production Economics, 2012, 139(1): 288–301
Shi J. Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes. Boca Raton: CRC Press, 2006
Hu S J, Koren Y. Stream-of-variation theory for automotive body assembly. CIRP Annals-Manufacturing Technology, 1997, 46(1): 1–6
Hu S J, Stecke K E. Analysis of automotive body assembly system configurations for quality and productivity. International Journal of Manufacturing Research, 2009, 4(3): 281–305
Kristianto Y, Gunasekaran A, Jiao J. Logical reconfiguration of reconfigurable manufacturing systems with stream of variations modelling: A stochastic two-stage programming and shortest path model. International Journal of Production Research, 2014, 52(5): 1401–1418
Abad A, Guo W, Jin J. Algebraic expression of system configurations and performance metrics for mixed model assembly systems. IIE Transactions, 2014, 46(3): 230–248
Gupta A, Jain P K, Kumar D. A novel approach for part family formation using K-means algorithm. Advances in Manufacturing, 2013 1(3): 241–250
Kimura F, Nielsen J. A design for product family under manufacturing resource constraints. CIRP Annals-Manufacturing Technology, 2005, 54(1): 139–142
Abdi M R, Labib A W. Grouping and selecting products: The design key of reconfigurable manufacturing systems (RMSs). International Journal of Production Research, 2004, 42(3): 521–546
Abdi M R, Labib A W. A design strategy for reconfigurable manufacturing systems (RMSs) using analytical hierarchical process (AHP): A case study. International Journal of Production Research, 2003, 41(10): 2273–2299
Galan R, Racero J, Eguia I, et al. A systematic approach for product families formation in reconfigurable manufacturing systems. Robotics and Computer-integrated Manufacturing, 2007, 23(5): 489–502
Abdi M R. Product family formation and selection for reconfigurability using analytical network process. International Journal of Production Research, 2012, 50(17): 4908–4921
Battaïa O, Dolgui A, Guschinsky N. Decision support for design of reconfigurable rotary machining systems for family part production. International Journal of Production Research, 2017, 55(5): 1368–1385
Goyal K K, Jain P K, Jain M. A comprehensive approach to operation sequence similarity based part family formation in the reconfigurable manufacturing system. International Journal of Production Research, 2013, 51(6): 1762–1776
Wang G, Huang S, Shang X, et al. Formation of part family for reconfigurable manufacturing systems considering bypassing moves and idle machines. Journal of Manufacturing Systems, 2016, 41: 120–129
Kashkoush M, El Maraghy H. Product family formation for reconfigurable assembly systems. Procedia CIRP, 2014, 17: 302–307
Eguia I, Lozano S, Racero J, et al. A methodological approach for designing and sequencing product families in reconfigurable disassembly systems. Journal of Industrial Engineering and Management, 2011, 4(3): 418–435
Azab A, El Maraghy H. Mathematical modeling for reconfigurable process planning. CIRP Annals-Manufacturing Technology, 2007, 56(1): 467–472
Azab A, Perusi G, El Maraghy H A, et al. Semi-generative macroprocess planning for reconfigurable manufacturing. Digital Enterprise Technology, 2007, 251–258
Bensmaine A, Dahane M, Benyoucef L. A simulation-based genetic algorithm approach for process plans selection in uncertain reconfigurable environment. IFAC Proceedings Volumes, 2013, 46(9): 1961–1966
Bensmaine A, Dahane M, Benyoucef L. A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems. International Journal of Production Research, 2014, 52(12): 3583–3594
Borisovsky P A, Delorme X, Dolgui A. Genetic algorithm for balancing reconfigurable machining lines. Computers & Industrial Engineering, 2013, 66(3): 541–547
Borisovsky P A, Delorme X, Dolgui A. Balancing reconfigurable machining lines via a set partitioning model. International Journal of Production Research, 2014, 52(13): 4026–4036
Essafi M, Delorme X, Dolgui A. A reactive GRASP and path relinking for balancing reconfigurable transfer lines. International Journal of Production Research, 2012, 50(18): 5213–5238
Makssoud F, Battaïa O, Dolgui A. Reconfiguration of machining transfer lines. In: Borangiu T, Thomas A, Trentesaux D, eds. Service Orientation in Holonic and Multi Agent Manufacturing and Robotics. Berlin: Spring, 2013, 339–353
Delorme X, Malyutin S, Dolgui A. A multi-objective approach for design of reconfigurable transfer lines. IFAC-PapersOnLine, 2016, 49(12): 509–514
da Silva R M, Junqueira F, Santos Filho D J, et al. Control architecture and design method of reconfigurable manufacturing systems. Control Engineering Practice, 2016, 49: 87–100
Mehrabi M G, Ulsoy A G, Koren Y. Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing, 2000, 11(4): 403–419
Ni J, Jin X. Decision support systems for effective maintenance operations. CIRP Annals-Manufacturing Technology, 2015, 61(1): 411–414
Guo W, Jin J, Hu S J. Allocation of maintenance resources in mixed model assembly systems. Journal of Manufacturing Systems, 2013, 32(3): 473–479
Gu X, Jin X, Ni J. Prediction of passive maintenance opportunity windows on bottleneck machines in complex manufacturing systems. ASME Journal Manufacturing Science and Engineering, 2015, 137(3): 031017
Ni J, Gu X, Jin X. Preventive maintenance opportunities for large production systems. CIRP Annals-Manufacturing Technology, 2015, 64(1): 447–450
Gu X, Jin X, Guo W, et al. Estimation of active maintenance opportunity windows in Bernoulli production lines. Journal of Manufacturing Systems, 2017, 45: 109–120
Zhou J, Djurdjanovic D, Ivy D, et al. Integrated reconfiguration and age-based preventive maintenance decision making. IIE Transactions, 2007, 39(12): 1085–1102
Xia T, Xi L, Pan E, et al. Reconfiguration-oriented opportunistic maintenance policy for reconfigurable manufacturing systems. Reliability Engineering & System Safety, 2017, 166: 87–98
Xia T, Tao X, Xi L. Operation process rebuilding (OPR)-oriented maintenance policy for changeable system structures. IEEE Transactions on Automation Science and Engineering, 2017, 14(1): 139–148
Brettel M, Klein M, Friederichsen N. The relevance of manufacturing flexibility in the context of Industrie 4.0. Procedia CIRP, 2016, 41: 105–110
Dubey R, Gunasekaran A, Helo P, et al. Explaining the impact of reconfigurable manufacturing systems on environmental performance: The role of top management and organizational culture. Journal of Cleaner Production, 2017, 141: 56–66
Michalek J J, Ceryan O, Papalambros P Y, et al. Balancing marketing and manufacturing objectives in product line design. Journal of Mechanical Design, 2006, 128(6): 1196–1204
Tang L, Yip-Hoi D M, Wang W, et al. Concurrent line-balancing, equipment selection and throughput analysis for multi-part optimal line design. Journal for Manufacturing Science and Production, 2004, 6(1–2): 71–82
Ausaf M F, Gao L, Li X. Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm. Frontiers of Mechanical Engineering, 2015, 10(4): 392–404
Wang B, Guan Z, Chen Y, et al. An assemble-to-order production planning with the integration of order scheduling and mixed-model sequencing. Frontiers of Mechanical Engineering, 2013, 8(2): 137–145
Renzi C, Leali F, Cavazzuti M, et al. A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems. International Journal of Advanced Manufacturing Technology, 2014, 72(1–4): 403–418
Koren Y, Gu X, Freiheit T. The impact of corporate culture on manufacturing system design. CIRP Annals-Manufacturing Technology, 2016, 65(1): 413–416
He N, Zhang D Z, Li Q. Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system. International Journal of Production Economics, 2014, 149: 117–130
Gao R, Wang L, Teti R, et al. Cloud-enabled prognosis for manufacturing. CIRP Annals-Manufacturing Technology, 2015, 64(2): 749–772
Xiong Y, Yin Z. Digital manufacturing––The development direction of the manufacturing technology in the 21st century. Frontiers of Mechanical Engineering in China, 2006, 1(2): 125–130
Monostori L, Kádár B, Bauernhansl T, et al. Cyber-physical systems in manufacturing. CIRP Annals-Manufacturing Technology, 2016, 65(2): 621–641
Guo W, Chen R, Jin J. On-line eccentricity monitoring of seamless tubes in cross-roll piercing mill. ASME Journal Manufacturing Science and Engineering, 2015, 137(2): 021007
Guo W, Shao C, Kim T H, et al. Online process monitoring with near-zero misdetection for ultrasonic welding of Lithium-ion batteries. Journal of Manufacturing Systems, 2016, 38(1): 141–150
Wang S, Chen T, Sun J. Design and realization of a remote monitoring and diagnosis and prediction system for large rotating machinery. Frontiers of Mechanical Engineering in China, 2010, 5(2): 165–170
Li X, Jiang J, Su H, et al. Identification of abnormal operating conditions and intelligent decision system. Frontiers of Mechanical Engineering in China, 2011, 6(4): 456–462
Xu X, Deng S. Trend prediction technology of condition maintenance for large water injection units. Frontiers of Mechanical Engineering, 2010, 5(2): 171–175
Hu Y, Yang S, Du R. Distributed flexible reconfigurable condition monitoring and diagnosis technology. Frontiers of Mechanical Engineering in China, 2006, 1(3): 276–281
Lee J, Lapira E, Bagheri B, et al. Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 2013, 1(1): 38–41
Guo W, Guo S, Wang H, et al. A data-driven diagnostic system utilizing manufacturing data mining and analytics. SAE International Journal of Materials and Manufacturing, 2017, 10(3): 01632923
Guo N, Leu M C. Additive manufacturing: Technology, applications and research needs. Frontiers of Mechanical Engineering, 2013, 8(3): 215–243
Koren Y, Hu S J, Gu P, et al. Open architecture products. CIRP Annals-Manufacturing Technology, 2013, 62(2): 719–729
Hu S J, Ko J, Weyand L, et al. Assembly system design and operations for product variety. CIRP Annals-Manufacturing Technology, 2011, 60(2): 715–733
Koren Y, Hill R. US Patent 6920973, 2004-07-26
Cherubini A, Passama R, Crosnier A, et al. Collaborative manufacturing with physical human-robot interaction. Robotics and Computer-Integrated Manufacturing, 2016, 40: 1–13
Pellegrinelli S, Moro F L, Pedrocchi N, et al. A probabilistic approach to workspace sharing for human-robot cooperation in assembly tasks. CIRP Annals-Manufacturing Technology, 2016, 65(1): 57–60
Wang X V, Kemény Z, Váncza J, et al. Human-robot collaborative assembly in cyber-physical production: Classification framework and implementation. CIRP Annals-Manufacturing Technology, 2017, 66(1): 5–8
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Koren, Y., Gu, X. & Guo, W. Reconfigurable manufacturing systems: Principles, design, and future trends. Front. Mech. Eng. 13, 121–136 (2018). https://doi.org/10.1007/s11465-018-0483-0
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DOI: https://doi.org/10.1007/s11465-018-0483-0