Skip to main content

Advertisement

SpringerLink
Log in

Optimal configuration selection for Reconfigurable Manufacturing Systems

  • Published:
International Journal of Flexible Manufacturing Systems Aims and scope Submit manuscript

Abstract

The selection of Reconfigurable Manufacturing Systems (RMS) configurations that include arrangement of machines, equipment selection, and assignment of operations, has a significant impact on their performance. This paper reviews the relevant literature and highlights the gaps that exist in this area of research. A novel “RMS Configuration Selection Approach” is introduced. It consists of two phases; the first deals with the selection of the near-optimal alternative configurations for each possible demand scenario over the considered configuration periods. It uses a constraint satisfaction procedure and powerful meta-heuristics, real-coded Genetic Algorithms (GAs) and Tabu Search (TS), for the continuous optimization of capital cost and system availability. The second phase utilizes integer-coded GAs and TS to determine the alternatives, from those produced in the first phase, that would optimize the degree of transition smoothness over the planning horizon. It uses a stochastic model of the level of reconfiguration smoothness (RS) across all the configuration periods in the planning horizon according to the anticipated demand scenarios. This model is based on a RS metric and a reconfiguration planning procedure that guide the development of execution plans for reconfiguration. The developed approach is demonstrated and validated using a case study. It was shown that it is possible to provide the manufacturing capacity and functionality needed when needed while minimizing the reconfiguration effort. The proposed approach can provide decision support for management in selecting RMS configurations at the beginning of each configuration period.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • 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(10):2273–2299

    Article  Google Scholar 

  • Abdi MR, Labib AW (2004) Grouping and selecting products: the design key of Reconfigurable Manufacturing Systems (RMSs). Int J Prod Res 43(3):521–546

    Article  Google Scholar 

  • Battiti R, Tecchiolli G (1994) The Reactive Tabu Search. ORSA J Comput 6:126–140

    MATH  Google Scholar 

  • Cochran DS, Arinez JF, Duda JW, Linck J (2001) A Decomposition Approach for Manufacturing System Design. J Manuf Syst 20(6):371–389

    Article  Google Scholar 

  • ElMaraghy HA (2005) Flexible and Reconfigurable Manufacturing Systems paradigms. Int J Flex Manuf Syst 17(4):261–276 (Special issue on Reconfigurable Manufacturing Systems)

    Article  Google Scholar 

  • Fukaya T (2004) HIPARMS (Highly Productive and Reconfigurable Manufacturing System) Final Report, http://www.ims.org/images/projects/hiparms/finalreport.pdf

  • Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Operat Res 13:533–549

    Article  MATH  MathSciNet  Google Scholar 

  • Glover F, Laguna M (1997) Tabu Search. Kluwer Academic Publishers, Boston

    MATH  Google Scholar 

  • Herrera F, Lozano M, Verdegay JL (1998) Tackling real-coded Genetic Algorithms: operators and tools for behavioral analysis. Artif Intell Rev 12(4):265–319

    Article  MATH  Google Scholar 

  • Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  • IEEE Std 1320.1 (1998) IEEE Standard for Functional Modeling Language—Syntax and Semantics for IDEF0, IEEE, New York

  • Kimms A (2000) Minimal investment budgets for flow line configuration. IIE Trans 32:287–298

    Google Scholar 

  • Koren Y, Hu SJ, Weber TW (1998) Impact of manufacturing system configuration on performance. Ann CIRP 47(1):369–372

    Google Scholar 

  • Koren Y, Heisel U, Jovane F, Moriwaki T, Pritschow G, Ulsoy G, Van Brussel H (1999) Reconfigurable manufacturing systems. Ann CIRP 48(2):527–540

    Google Scholar 

  • Kuo CH (2001) Resource allocation and performance evaluation of the Reconfigurable Manufacturing Systems. Proc IEEE Int Conf Syst Man Cybernet 4:2451–2456

    Google Scholar 

  • Lee GH (1997) Reconfigurability consideration design of components and manufacturing systems. Int J Adv Manuf Technol 13(5):376–386

    Article  Google Scholar 

  • Lee GH (1998) Designs of components and manufacturing systems for agile manufacturing. Int J Prod Res 36(4):1023–1044

    Article  MATH  Google Scholar 

  • Maier-Speredelozzi V, Hu SJ (2002) Selecting manufacturing system configurations based on performance using AHP. Technical Paper—Society of Manufacturing Engineers. MS, no MS02-179, pp 1–8

  • Maier-Speredelozzi V, Koren Y, Hu SJ (2003) Convertibility measures for manufacturing systems. Ann CIRP 52(1):367–370

    Google Scholar 

  • Makino H, Trai T (1994) New developments in assembly systems. Ann CIRP 43(2):501–522

    Google Scholar 

  • Matta A, Tolio T, Karaesmen F, Dallery Y (2001) An integrated approach for the configuration of automated manufacturing systems. Robot Comput Integ Manuf 17:19–26

    Article  Google Scholar 

  • Mehrabi MG, Ulsoy AG, Koren Y (2000) Reconfigurable manufacturing systems: key to future manufacturing. J Intell Manuf 11:403–419

    Article  Google Scholar 

  • Ohiro T, Myreshka, Morikawa K, Takahashi K (2003) A stochastic model for deciding an optimal production order and its corresponding configuration in a reconfigurable manufacturing system with multiple product groups. Proceedings of the CIRP 2nd international conference on reconfigurable manufacturing, Michigan

  • Son SY (2000) Design principles and methodologies for reconfigurable machining systems. PhD Dissertation, University of Michigan

  • Spicer JP (2002) A design methodology for scalable machining systems. PhD Dissertation, University of Michigan

  • Spicer P, Koren Y, Shpitalni M, Yip-Hoi D (2002) Design principles for machining system configurations. Ann CIRP 51(1):275–280

    Google Scholar 

  • Tang L, Yip-Hoi DM, Wang W, Koren Y (2004) Concurrent line-balancing, equipment selection and throughput analysis for multi-part optimal line design. Int J Manuf Sci Prod 6(1–2):71–81

    Google Scholar 

  • Tang L, Yip-Hoi DM, Wang W, Koren Y (2005) Selection principles on manufacturing system for part family. Proceedings of the CIRP 3rd international conference on reconfigurable manufacturing, Ann Arbor, MI

  • Tesfamariam D (2005) Configuration design of a high performance and responsive manufacturing system. PhD Dissertation, KTH Royal Institute of Technology, Stockholm, Sweden

  • Toenshoff HK, Schnuelle A, Rietz W (2001) Planning of manufacturing systems with methods analogous to nature. International CIRP design seminar, Stockholm, pp 255–260

  • Ushakov IA (1986) A universal generating function. Soviet J Comput Syst Sci 24(5):118–129

    MATH  MathSciNet  Google Scholar 

  • Wiendahl H-P, Heger CL (2003) Justifying changeability: a methodical approach to achieving cost effectiveness. Proceedings of the CIRP 2nd international conference on reconfigurable manufacturing, Michigan

  • Xiaobo Z, Jiancai W, Zhenbi L (2000a) A stochastic model of a reconfigurable manufacturing system. Part 1. A framework. Int J Prod Res 38(10):2273–2285

    Google Scholar 

  • Xiaobo Z, Wang J, Luo Z (2000b) A stochastic model of a reconfigurable manufacturing system. Part 2. Optimal configurations. Int J Prod Res 38(12):2829–2842

    Article  Google Scholar 

  • Xiaobo Z, Wang J, Luo Z (2001a) A stochastic model of a reconfigurable manufacturing system. Part 3. Optimal selection policy. Int J Prod Res 39(4):747–758

    Article  MATH  Google Scholar 

  • Xiaobo Z, Wang J, Luo Z (2001b) A stochastic model of a reconfigurable manufacturing system. Part 4. Performance measure. Int J Prod Res 39(6):1113–1126

    Article  MATH  Google Scholar 

  • 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. IEEE Int Conf Intell Robots Syst 2:2049–2054

    Google Scholar 

  • Yang S, Hu SJ (2000) Productivity analysis of a six CNC machine manufacturing system with different configurations. Proceedings of the 2000 Japan–USA flexible automation conference, Michigan, July, pp 499–505

  • Youssef AMA (2006) Optimal configuration selection for reconfigurable manufacturing systems. PhD Dissertation, University of Windsor, Canada

  • Youssef AMA, ElMaraghy HA (2006a) Assessment of manufacturing systems reconfiguration smoothness. Int J Adv Manuf Technol 30(1–2):174–193

    Article  Google Scholar 

  • Youssef AMA, ElMaraghy HA (2006b) Modelling and optimization of multiple-aspect rms configurations. Int J Prod Res 44(22):4929–4958 (Special issue on advances in evolutionary computation for design and manufacturing problems)

    Article  MATH  Google Scholar 

  • Youssef AMA, Mohib A, ElMaraghy HA (2006) Availability assessment of multi-state manufacturing systems using universal generating function. Ann CIRP 55(1):445–448

    Article  Google Scholar 

  • Youssef AMA, ElMaraghy HA (2007) Availability consideration in the optimal selection of multiple-aspect RMS configurations. Int J Prod Res (in press) Manuscript ID is TPRS-226074

  • Zhong W, Maier-Speredelozzi V, Bratzel A, Yang S, Chick SE, Hu SJ (2000) Performance analysis of machining systems with different configurations. Proceedings of the 2000 Japan–USA flexible automation conference, Michigan, July, pp 783–790

Download references

Acknowledgements

The contributions from the Canada Research Chairs (CRC) program and the Natural Sciences and Engineering Research Council (NSERC) of Canada in support of this research are greatly acknowledged.

Author information

Authors and Affiliations

  1. Intelligent Manufacturing Systems Centre, Industrial & Manufacturing Systems Engineering, Faculty of Engineering, University of Windsor, Room 204, Odette Building, 401 Sunset Avenue, Windsor, ON, Canada, N9B 3P4

    Ayman M. A. Youssef & Hoda A. ElMaraghy

Authors
  1. Ayman M. A. Youssef
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hoda A. ElMaraghy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hoda A. ElMaraghy.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Youssef, A.M.A., ElMaraghy, H.A. Optimal configuration selection for Reconfigurable Manufacturing Systems. Int J Flex Manuf Syst 19, 67–106 (2007). https://doi.org/10.1007/s10696-007-9020-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10696-007-9020-x

Keywords

Search

Navigation