Reconfigurable manufacturing systems: Principles, design, and future trends

Yoram KOREN , Xi GU , Weihong GUO

Front. Mech. Eng. ›› 2018, Vol. 13 ›› Issue (2) : 121 -136.

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Front. Mech. Eng. ›› 2018, Vol. 13 ›› Issue (2) : 121 -136. DOI: 10.1007/s11465-018-0483-0
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Reconfigurable manufacturing systems: Principles, design, and future trends

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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 cost-effective 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.

Keywords

reconfigurable manufacturing systems / responsiveness / intelligent manufacturing

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Yoram KOREN, Xi GU, Weihong GUO. Reconfigurable manufacturing systems: Principles, design, and future trends. Front. Mech. Eng., 2018, 13(2): 121-136 DOI:10.1007/s11465-018-0483-0

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References

[1]

Koren Y. Computer Control of Manufacturing Systems. New York: McGraw Hill, 1983

[2]

Koren Y, Heisel U, Jovane F, Reconfigurable manufacturing systems. CIRP Annals-Manufacturing Technology, 1999, 48(2): 527–540

[3]

Parsons J T, Stulen F L. US Patent 2820187, 1958-01-14

[4]

Koren Y. The rapid responsiveness of RMS. International Journal of Production Research, 2013, 51(23–24): 6817–6827

[5]

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

[6]

Koren Y. The Global Manufacturing Revolution: Product-Process-Business Integration and Reconfigurable Systems. Hoboken: John Wiley & Sons, 2010

[7]

Garbie I H. DFSME: Design for sustainable manufacturing enterprises (an economic viewpoint). International Journal of Production Research, 2013, 51(2): 479–503

[8]

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

[9]

Koren Y, Shpitalni M. Design of reconfigurable manufacturing systems. Journal of Manufacturing Systems, 2010, 29(4): 130–141

[10]

Zhang G, Liu R, Gong L, 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

[11]

Singh A, Gupta S, Asjad M, Reconfigurable manufacturing systems: Journey and the road ahead. International Journal of System Assurance Engineering and Management, 2017, 1–9 (in press)

[12]

Wang W, Koren Y. Scalability planning for reconfigurable manufacturing systems. Journal of Manufacturing Systems, 2012, 31(2): 83–91

[13]

Maier-Speredelozzi V, Koren Y, Hu S J. Convertibility measures for manufacturing systems. CIRP Annals-Manufacturing Technology, 2003, 52(1): 367–370

[14]

Gumasta K, Gupta S K, Benyouce L, Developing a reconfigurability index using multi-attribute utility theory. International Journal of Production Research, 2011, 49(6): 1669–1683

[15]

Bi Z M, Lang S Y T, Shen W, Reconfigurable manufacturing systems: The state of the art. International Journal of Production Research, 2008, 46(4): 967–992

[16]

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

[17]

Colledani M, Tolio T. A decomposition method to support the reconfiguration/reconfiguration of production systems. CIRP Annals-Manufacturing Technology, 2005, 54 (1): 441–444

[18]

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

[19]

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

[20]

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

[21]

Rösiö C, Säfsten K. Reconfigurable production system design––Theoretical and practical challenges. Journal of Manufacturing Technology Management, 2013, 24(7): 998–1018

[22]

Andersen A L, Brunoe T D, Nielsen K, 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

[23]

Koren Y, Kota S. US Patent 5943750, 1999-08-31

[24]

Koren Y, Katz R. US Patent 6567162, 2003-12-24

[25]

Koren Y, Ulsoy G. US Patent 6349237, 2002-02-19

[26]

Krygier R. The Integration of flexible, reconfigurable manufacturing with quality. In: Proceedings of CIRP 3rd Conference on Reconfigurable Manufacturing. Ann Arbor, 2005

[27]

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

[28]

Koren Y, Hu S J, Weber T W. Impact of manufacturing system configuration on performance. CIRP Annals-Manufacturing Technology, 1998, 47(1): 369–372

[29]

Freiheit T, Shpitalni M, Hu S J, Designing productive manufacturing systems without buffers. CIRP Annals-Manufacturing Technology, 2003, 52 (1): 105–108.

[30]

Gu X. The impact of maintainability on the manufacturing system architecture. International Journal of Production Research, 2017, 55(15): 4392–4410

[31]

Koren Y, Gu X, Guo W. Choosing the system configuration for high-volume manufacturing. International Journal of Production Research, 2017 (in press)

[32]

Youssef A M A, ElMaraghy H A. Availability consideration in the optimal selection of multiple-aspect RMS configurations. International Journal of Production Research, 2008, 46(21): 5849–5882

[33]

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

[34]

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

[35]

Webbink R F, Hu S J. Automated generation of assembly system-design solutions. IEEE Transactions on Automation Science and Engineering, 2005, 2(1): 32–39

[36]

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

[37]

Narongwanich W, Duenyas I, Birge J R. Optimal Portfolio of Reconfigurable and Dedicated Capacity Under Uncertainty. Technical Report, University of Michigan ERC-RMS, 2002

[38]

Deif A M, ElMaraghy 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

[39]

Gyulai D, Kádár B, Kovács A, Capacity management for assembly systems with dedicated and reconfigurable resources. CIRP Annals-Manufacturing Technology, 2014, 63(1): 457–460

[40]

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

[41]

Asl F M, Ulsoy A G. Stochastic optimal capacity management in reconfigurable manufacturing systems. CIRP Annals-Manufacturing Technology 2003, 52 (1): 371–374

[42]

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

[43]

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

[44]

Van Mieghem J A. Investment strategies for flexible resources. Management Science, 1998, 44(8): 1071–1078

[45]

Ceryan O, Koren Y. Manufacturing capacity planning strategies. CIRP Annals-Manufacturing Technology, 2009, 58(1): 403–406

[46]

Matta A, Tomasella M, Clerici M, Optimal reconfiguration policy to react to product changes. International Journal of Production Research, 2008, 46(10): 2651–2673

[47]

Bryan A, Ko J, Hu S J, Co-evolution of product families and assembly systems. CIRP Annals-Manufacturing Technology, 2007, 56(1): 41–44

[48]

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

[49]

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

[50]

Shi J. Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes. Boca Raton: CRC Press, 2006

[51]

Hu S J, Koren Y. Stream-of-variation theory for automotive body assembly. CIRP Annals-Manufacturing Technology, 1997, 46(1): 1–6

[52]

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

[53]

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

[54]

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

[55]

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

[56]

Kimura F, Nielsen J. A design for product family under manufacturing resource constraints. CIRP Annals-Manufacturing Technology, 2005, 54 (1): 139–142

[57]

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

[58]

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

[59]

Galan R, Racero J, Eguia I, A systematic approach for product families formation in reconfigurable manufacturing systems. Robotics and Computer-integrated Manufacturing, 2007, 23(5): 489–502

[60]

Abdi M R. Product family formation and selection for reconfigurability using analytical network process. International Journal of Production Research, 2012, 50(17): 4908–4921

[61]

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

[62]

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

[63]

Wang G, Huang S, Shang X, Formation of part family for reconfigurable manufacturing systems considering bypassing moves and idle machines. Journal of Manufacturing Systems, 2016, 41: 120–129

[64]

Kashkoush M, ElMaraghy H. Product family formation for reconfigurable assembly systems. Procedia CIRP, 2014, 17: 302–307

[65]

Eguia I, Lozano S, Racero J, A methodological approach for designing and sequencing product families in reconfigurable disassembly systems. Journal of Industrial Engineering and Management, 2011, 4(3): 418–435

[66]

Azab A, ElMaraghy H. Mathematical modeling for reconfigurable process planning. CIRP Annals-Manufacturing Technology, 2007, 56(1): 467–472

[67]

Azab A, Perusi G, ElMaraghy H A, Semi-generative macro-process planning for reconfigurable manufacturing. Digital Enterprise Technology, 2007, 251–258

[68]

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

[69]

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

[70]

Borisovsky P A, Delorme X, Dolgui A. Genetic algorithm for balancing reconfigurable machining lines. Computers & Industrial Engineering, 2013, 66(3): 541–547

[71]

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

[72]

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

[73]

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

[74]

Delorme X, Malyutin S, Dolgui A. A multi-objective approach for design of reconfigurable transfer lines. IFAC-PapersOnLine, 2016, 49(12): 509–514

[75]

da Silva R M, Junqueira F, Santos Filho D J, Control architecture and design method of reconfigurable manufacturing systems. Control Engineering Practice, 2016, 49: 87–100

[76]

Mehrabi M G, Ulsoy A G, Koren Y. Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing, 2000, 11(4): 403–419

[77]

Ni J, Jin X. Decision support systems for effective maintenance operations. CIRP Annals-Manufacturing Technology, 2015, 61(1): 411–414

[78]

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

[79]

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

[80]

Ni J, Gu X, Jin X. Preventive maintenance opportunities for large production systems. CIRP Annals-Manufacturing Technology, 2015, 64(1): 447–450

[81]

Gu X, Jin X, Guo W, Estimation of active maintenance opportunity windows in Bernoulli production lines. Journal of Manufacturing Systems, 2017, 45: 109–120

[82]

Zhou J, Djurdjanovic D, Ivy D, Integrated reconfiguration and age-based preventive maintenance decision making. IIE Transactions, 2007, 39(12): 1085–1102

[83]

Xia T, Xi L, Pan E, Reconfiguration-oriented opportunistic maintenance policy for reconfigurable manufacturing systems. Reliability Engineering & System Safety, 2017, 166: 87–98

[84]

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

[85]

Brettel M, Klein M, Friederichsen N. The relevance of manufacturing flexibility in the context of Industrie 4.0. Procedia CIRP, 2016, 41: 105–110

[86]

Dubey R, Gunasekaran A, Helo P, 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

[87]

Michalek J J, Ceryan O, Papalambros P Y, Balancing marketing and manufacturing objectives in product line design. Journal of Mechanical Design, 2006, 128(6): 1196–1204

[88]

Tang L, Yip-Hoi D M, Wang W, 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

[89]

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

[90]

Wang B, Guan Z, Chen Y, 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

[91]

Renzi C, Leali F, Cavazzuti M, 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

[92]

Koren Y, Gu X, Freiheit T. The impact of corporate culture on manufacturing system design. CIRP Annals-Manufacturing Technology, 2016, 65(1): 413–416

[93]

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

[94]

Gao R, Wang L, Teti R, Cloud-enabled prognosis for manufacturing. CIRP Annals-Manufacturing Technology, 2015, 64(2): 749–772

[95]

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

[96]

Monostori L, Kádár B, Bauernhansl T, Cyber-physical systems in manufacturing. CIRP Annals-Manufacturing Technolo-gy, 2016, 65(2): 621–641

[97]

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

[98]

Guo W, Shao C, Kim T H, Online process monitoring with near-zero misdetection for ultrasonic welding of Lithium-ion batteries. Journal of Manufacturing Systems, 2016, 38(1): 141–150

[99]

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

[100]

Li X, Jiang J, Su H, Identification of abnormal operating conditions and intelligent decision system. Frontiers of Mechanical Engineering in China, 2011, 6(4): 456–462

[101]

Xu X, Deng S. Trend prediction technology of condition maintenance for large water injection units. Frontiers of Mechanical Engineering, 2010, 5(2): 171–175

[102]

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

[103]

Lee J, Lapira E, Bagheri B, Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 2013, 1(1): 38–41

[104]

Guo W, Guo S, Wang H, A data-driven diagnostic system utilizing manufacturing data mining and analytics. SAE International Journal of Materials and Manufacturing, 2017, 10(3): 01632923

[105]

Guo N, Leu M C. Additive manufacturing: Technology, applications and research needs. Frontiers of Mechanical Engineering, 2013, 8(3): 215–243

[106]

Koren Y, Hu S J, Gu P, Open architecture products. CIRP Annals-Manufacturing Technology, 2013, 62(2): 719–729

[107]

Hu S J, Ko J, Weyand L, Assembly system design and operations for product variety. CIRP Annals-Manufacturing Technology, 2011, 60(2): 715–733

[108]

Koren Y, Hill R. US Patent 6920973, 2004-07-26

[109]

Cherubini A, Passama R, Crosnier A, Collaborative manufacturing with physical human-robot interaction. Robotics and Computer-Integrated Manufacturing, 2016, 40: 1–13

[110]

Pellegrinelli S, Moro F L, Pedrocchi N, A probabilistic approach to workspace sharing for human-robot cooperation in assembly tasks. CIRP Annals-Manufacturing Technology, 2016, 65(1): 57–60

[111]

Wang X V, Kemény Z, Váncza J, 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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