Advertisement

A modelling framework to support design of complex engineering systems in early design stages

  • Shiva AbdoliEmail author
  • Sami Kara
Original Paper
  • 14 Downloads

Abstract

Production, assembly or logistic systems exist in widespread domains. It is agreed that more than 50% of life-cycle performance, costs and environmental impacts of such systems are due to those decisions that are made in their early design stages (Reich, Res Eng Design 28(4):411–419,  https://doi.org/10.1007/s00163-017-0270-7, 2017). However, the large scale and multi-disciplinary essence of such systems make their design considerably challenging. Most of the design approaches follow a sequential approach such that the design in each lower level is finalized/frozen before proceeding to the next level. However, such approaches do not properly address the interaction between different design disciplines which may later lead to design inconsistencies. Therefore, this paper aimed to propose a modelling framework that allows having an integrated approach in the early design stages of such systems. To this end, first the framework prescribed developing an executable meta-architecture that can embody all the design requirements. Second, the framework describes the interconnections between the meta-architecture with certain supporting algorithms and optimization models. This allows generating and simulating different design alternatives and observing the impact of different design decisions on system integrated performance. Therefore, the proposed framework with its providing outcomes can be used to support the decision making in early design stages of such systems. The framework is applied in a real case study from the warehousing domain, which serves to show the practical application of the proposed framework.

Keywords

Complex engineering systems Object Oriented modelling Systems engineering System logical architecture Discrete event simulation Finite state machine 

Notes

Supplementary material

163_2019_321_MOESM1_ESM.docx (58 kb)
Supplementary material 1 (DOCX 57 kb)

References

  1. Abdoli S, Kara S (2017a) A modelling framework to design executable logical architecture of engineering systems. Mod Appl Sci 11(9):75Google Scholar
  2. Abdoli S, Kara S (2017b) A review of modelling approaches for conceptual design of complex engineering systems (CESs). In: 2017 IEEE international conference on industrial engineering and engineering management (IEEM). IEEE, Singapore, SingaporeGoogle Scholar
  3. Albers A, Braun A (2011) A generalised framework to compass and to support complex product engineering processes. Int J Prod Dev 15(1–3):6–25Google Scholar
  4. Alfaris AAF (2009) The evolutionary design model (EDM) for the design of complex engineered systems: Masdar City as a case study. Doctoral dissertation, Massachusetts Institute of TechnologyGoogle Scholar
  5. Alvarez Cabrera AA, Foeken MJ, Tekin OA, Woestenenk K, Erden MS, De Schutter B, van Tooren MJL, Babuška R, van Houten FJAM, Tomiyama T (2010) Towards automation of control software: a review of challenges in mechatronic design. Mechatronics 20(8):876–886.  https://doi.org/10.1016/j.mechatronics.2010.05.003 Google Scholar
  6. Alves AC, Silva SC (2009) A review of design methodologies for manufacturing systems. In: 1ST International Conference on Innovations, Recent Trends and Challenges in Mechatronics, Mechanical Engineering and New High-Tech Products Development MECAHITECH‘09. BucharestGoogle Scholar
  7. Baker P, Canessa M (2009) Warehouse design: a structured approach. Eur J Oper Res 193(2):425–436.  https://doi.org/10.1016/j.ejor.2007.11.045 Google Scholar
  8. Bar-Yam Y (2002) General features of complex systems. Encyclopedia of Life Support Systems (EOLSS), UNESCO, EOLSS Publishers, OxfordzbMATHGoogle Scholar
  9. Bar-Yam Y (2004) A mathematical theory of strong emergence using multiscale variety. Complexity 9(6):15–24.  https://doi.org/10.1002/cplx.20029 MathSciNetGoogle Scholar
  10. Bortolini M, Faccio M, Gamberi M, Manzini R, Pilati F (2016) Stochastic timed Petri nets to dynamically design and simulate industrial production processes. Int J Logist Syst Manag 25(1):20–43Google Scholar
  11. Braha D, Minai AA, Bar-Yam Y (2006) Complex engineered systems: science meets technology. Springer, Berli.  https://doi.org/10.1007/3-540-32834-3nzbMATHGoogle Scholar
  12. Chakrabarti A, Blessing LTM (2014) An anthology of theories and models of design. Springer, New YorkGoogle Scholar
  13. Christophe F, Bernard A, Coatanéa E (2010) RFBS: a model for knowledge representation of conceptual design. CIRP Ann Manuf Technol 59(1):155–158Google Scholar
  14. Cloutier RJ, Verma D (2007) Applying the concept of patterns to systems architecture. Syst Eng 10(2):138–154Google Scholar
  15. Cochran DS, Reinhart G, Linck J, Mauderer M (2000) Decision support for manufacturing system design-combining a decomposition methodology with procedural manufacturing system design. In: The Third world congress on intelligent manufacturing processes and systems. CambridgeGoogle Scholar
  16. Dauby JP, Dagli CH (2011) The canonical decomposition fuzzy comparative methodology for assessing architectures. IEEE Syst J 5(2):244–255.  https://doi.org/10.1109/JSYST.2011.2125250 Google Scholar
  17. de Koster R, Le-Duc T, Roodbergen KJ (2007) Design and control of warehouse order picking: a literature review. Eur J Oper Res 182(2):481–501.  https://doi.org/10.1016/j.ejor.2006.07.009 zbMATHGoogle Scholar
  18. Dori D (2002) Object-process methodology. Springer, New YorkzbMATHGoogle Scholar
  19. Dori D, Renick Aharon, Wengrowicz Niva (2016) When quantitative meets qualitative: enhancing OPM conceptual systems modeling with MATLAB computational capabilities. Res Eng Design 27(2):141–164.  https://doi.org/10.1007/s00163-015-0209-9 Google Scholar
  20. Douglass BP (2016) Chapter 1—What is model-based systems engineering? Agile Systems Engineering. Morgan Kaufmann, Boston, pp 1–39Google Scholar
  21. ElMaraghy HA, Kuzgunkaya O, Urbanic RJ (2005) Manufacturing systems configuration complexity. CIRP Ann Manuf Technol 54(1):445–450.  https://doi.org/10.1016/S0007-8506(07)60141-3 Google Scholar
  22. Estefan JA (2003) Survey of model-based systems engineering (MBSE) methodologies. Jet Propulsion Laboratory, PasadenaGoogle Scholar
  23. Figueira G, Almada-Lobo B (2014) Hybrid simulation–optimization methods: a taxonomy and discussion. Simul Model Pract Theory 46:118–134Google Scholar
  24. Fishwick PA (2007) Handbook of dynamic system modeling. CRC Press, Boca RatonzbMATHGoogle Scholar
  25. Fleck M, Berardinelli L, Langer P, Mayerhofer T, Cortellessa V (2013) Resource contention analysis of service-based systems through fUML-driven model execution. Proc. of NiM-ALP:6Google Scholar
  26. Gausemeier J, Dumitrescu R, Kahl S, Nordsiek D (2011) Integrative development of product and production system for mechatronic products. Robot Comput Integr Manuf 27(4):772–778.  https://doi.org/10.1016/j.rcim.2011.02.005 Google Scholar
  27. Gu P, Rao HA, Tseng MM (2001) Systematic design of manufacturing systems based on axiomatic design approach. CIRP Ann Manuf Technol 50(1):299–304Google Scholar
  28. Hopp WJ, Spearman ML (2011) Factory physics. Waveland Press, Long GroveGoogle Scholar
  29. Huang E, Ramamurthy R, McGinnis LF (2007) System and simulation modeling using SysML. In: Proceedings of the 2007 winter simulation conference. IEEE, Washington, DC, USAGoogle Scholar
  30. INCOSE (2015) INCOSE SE Handbook Working Group, INCOSE system engineering handbook San Diego, 4th edn. Wiley, HobokenGoogle Scholar
  31. Jørgensen HD (2004) Interactive process models. Doctoral thesis, Norwegian University of Science and Technology, Fakultet for informasjonsteknologi, matematikk og elektroteknikkGoogle Scholar
  32. Kapos GD (2015) Enabling system models automated evaluation through cross-concept information utilization. In: 2015 IEEE 9th international conference on research challenges in information science. IEEE, Athens, GreeceGoogle Scholar
  33. Kapos GD, Dalakas V, Nikolaidou M, Anagnostopoulos D (2014) An integrated framework for automated simulation of SysML models using DEVS. Simulation 90(6):717–744Google Scholar
  34. Khan I (2010) Methodology for the development of executable system architecture. In: Proceedings of the 8th international conference on frontiers of information technology. ACM, Islamabad.  https://doi.org/10.1145/1943628.1943677 Google Scholar
  35. Komoto H, Tomiyama T (2012) A framework for computer-aided conceptual design and its application to system architecting of mechatronics products. Comput Aided Des 44(10):931–946.  https://doi.org/10.1016/j.cad.2012.02.004 Google Scholar
  36. Koo HYB (2005) A meta-language for systems architecting. Technion, Israel Institute of Technology, HaifaGoogle Scholar
  37. Kossiakoff A, Sweet WN, Seymour SJ, Biemer SM (2011) Systems engineering principles and practice, vol 83. Wiley, New YorkGoogle Scholar
  38. Maropoulos PG, Ceglarek D (2010) Design verification and validation in product lifecycle. CIRP Ann 59(2):740–759.  https://doi.org/10.1016/j.cirp.2010.05.005 Google Scholar
  39. Matei I, Bock C (2012) SysML extension for dynamical system simulation tools. US Department of Commerce, National Institute of Standards and Technology.  https://doi.org/10.6028/NIST.IR.7888
  40. Mayerhofer T, Langer P, Wimmer M, Kappel G (2013) xMOF: executable DSMLs based on fUML. In: International conference on software language engineering, Springer, Cham, pp 56–75.  https://doi.org/10.1007/978-3-319-02654-1-4 Google Scholar
  41. McGinnis LF, Ustun V (2009) A simple example of SysML-driven simulation. In: Proceedings of the 2009 winter simulation conference. Austin, Texas, pp 1703–1710Google Scholar
  42. McGinnis LF, Huang E, Wu K (2006) Systems engineering and design of high-tech factories. In: Proceedings of the 2006 winter simulation conference. IEEE, Monterey, CA, USA.  https://doi.org/10.1109/WSC.2006.322969 Google Scholar
  43. McGinnis L, Huang E, Kwon KS, Ustun V (2011) Ontologies and simulation: a practical approach. J Simul 5(3):190–201Google Scholar
  44. Meng X (2010) Modeling of reconfigurable manufacturing systems based on colored timed object-oriented Petri nets. J Manuf Syst 29(2–3):81–90.  https://doi.org/10.1016/j.jmsy.2010.11.002 Google Scholar
  45. Mijatov S, Mayerhofer T, Langer P, Kappel G (2015) Testing functional requirements in UML activity diagrams. International conference on tests and proofs. Part of the Lecture notes in computer science, LNCS, vol 9154. Springer, Cham, pp 173–190.  https://doi.org/10.1007/978-3-319-21215-9_11 Google Scholar
  46. Mönch L, Lendermann P, McGinnis LF, Schirrmann A (2011) A survey of challenges in modelling and decision-making for discrete event logistics systems. Comput Ind 62(6):557–567.  https://doi.org/10.1016/j.compind.2011.05.001 Google Scholar
  47. Moses J (2002) The anatomy of large scale systems. ESD Internal SymposiumGoogle Scholar
  48. Nikolaidou M, Kapos GD, Dalakas V, Anagnostopoulos D (2012) Basic guidelines for simulating SysML models: an experience report. In: 2012 7th international conference on system of systems engineering (SoSE), IEEE, Genova, Italy, pp 95–100.  https://doi.org/10.1109/SYSoSE.2012.6384172 Google Scholar
  49. Osorio CA, Dori D, Sussman J (2011) COIM: an object-process based method for analyzing architectures of complex, interconnected, large-scale socio-technical systems. Syst Eng 14(4):364–382Google Scholar
  50. Pahl G, Beitz W, Feldhusen J, Grote KH (2007) Engineering design—a systematic approach, 3rd edn. Springer, New YorkGoogle Scholar
  51. Pape L, Giammarco K, Colombi J, Dagli C, Kilicay-Ergin N, Rebovich G (2013) A fuzzy evaluation method for system of systems meta-architectures. Procedia Comput Sci 16:245–254.  https://doi.org/10.1016/j.procs.2013.01.026 Google Scholar
  52. Reich Y (2017) What is a reference? Res Eng Design 28(4):411–419.  https://doi.org/10.1007/s00163-017-0270-7 Google Scholar
  53. Robinson S (2006) Conceptual modeling for simulation: issues and research requirements. In: Proceedings of the 2006 winter simulation conference. IEEE, Monterey, CA, USA, pp 792–800.  https://doi.org/10.1109/WSC.2006.323160 Google Scholar
  54. Rouwenhorst B, Reuter B, Stockrahm V, van Houtum GJ, Mantel RJ, Zijm WHM (2000) Warehouse design and control: framework and literature review. Eur J Oper Res 122(3):515–533.  https://doi.org/10.1016/S0377-2217(99)00020-X zbMATHGoogle Scholar
  55. Roy R, Hinduja S, Teti R (2008) Recent advances in engineering design optimisation: challenges and future trends. CIRP Ann Manuf Technol 57(2):697–715.  https://doi.org/10.1016/j.cirp.2008.09.007 Google Scholar
  56. Schönherr O, Rose O (2009) First steps towards a general SysML model for discrete processes in production systems. In: Proceedings of the 2009 Winter Simulation Conference (WSC). IEEE, Austin, TX, USA, USA, pp 1711–1718.  https://doi.org/10.1109/WSC.2009.5429164 Google Scholar
  57. Schotborgh WO, McMahon C, Van Houten FJAM (2012) A knowledge acquisition method to model parametric engineering design processes. Int J Comput Aided Eng Technol 4(4):373–391Google Scholar
  58. Schuh G, Monostori L, Csáji BC, Döring S (2008) Complexity-based modeling of reconfigurable collaborations in production industry. CIRP Ann Manuf Technol 57(1):445–450Google Scholar
  59. Sitton M, Reich Y (2018) EPIC framework for enterprise processes integrative collaboration. Syst Eng 21(1):30–46Google Scholar
  60. Steponavičė I, Ruuska S, Miettinen K (2014) A solution process for simulation-based multiobjective design optimization with an application in the paper industry. Comput Aided Des 47:45–58Google Scholar
  61. Thiers G (2014) A model-based systems engineering methodology to make engineering analysis of discrete-event logistics systems more cost-accessible. Doctoral dissertation, Georgia Institute of TechnologyGoogle Scholar
  62. Tomiyama T, D’Amelio V, Urbanic J, ElMaraghy W (2007) Complexity of multi-disciplinary design. CIRP Ann Manuf Technol 56(1):185–188Google Scholar
  63. Tomiyama T, Gu P, Jin Y, Lutters D, Kind C, Kimura F (2009) Design methodologies: industrial and educational applications. CIRP Ann Manuf Technol 58(2):543–565.  https://doi.org/10.1016/j.cirp.2009.09.003 Google Scholar
  64. Umeda Y, Ishii M, Yoshioka M, Shimomura Y, Tomiyama T (1996) Supporting conceptual design based on the function-behavior-state modeler. Artif Intell Eng Des Anal Manuf 10(04):275–288Google Scholar
  65. Vrabič R, Butala P (2011) Computational mechanics approach to managing complexity in manufacturing systems. CIRP Ann Manuf Technol 60(1):503–506.  https://doi.org/10.1016/j.cirp.2011.03.050 Google Scholar
  66. Wagenhals LW, Haider S, Levis AH (2003) Synthesizing executable models of object oriented architectures. Syst Eng 6(4):266–300.  https://doi.org/10.1002/sys.10049 Google Scholar
  67. Wang R (2012) Search-based system architecture development using a holistic modeling approach. Doctoral Dissertation, Missouri University of Science and Technology (2256)Google Scholar
  68. Wang R, Dagli CH (2008) An executable system architecture approach to discrete events system modeling using SysML in conjunction with colored Petri Net. In: 2008 2nd annual IEEE systems conference. IEEE, Montreal, Que., Canada, pp 1–8.  https://doi.org/10.1109/SYSTEMS.2008.4518997 Google Scholar
  69. Wang RZ, and Dagli CH (2013) Developing a holistic modeling approach for search-based system architecting. In: 2013 Conference on Systems Engineering Research, vol 16, pp 206–215.  https://doi.org/10.1016/j.procs.2013.01.022
  70. White BE (2007) On interpreting scale (or view) and emergence in complex systems engineering. In: 2007 1st annual IEEE systems conference. IEEE, Honolulu, HI, USA, pp 1–7.  https://doi.org/10.1109/SYSTEMS.2007.374660 Google Scholar
  71. Wolfram S (1985) Complex systems theory. The Institute for Advanced Study, PrincetonGoogle Scholar
  72. Yaroker Y, Perelman V, Dori D (2013) An OPM conceptual model-based executable simulation environment: implementation and evaluation. Syst Eng 16(4):381–390Google Scholar
  73. Zheng C, Hehenberger P, Duigou JL, Bricogne M, Eynard B (2016) Multidisciplinary design methodology for mechatronic systems based on interface model. Res Eng Des 28(3):1–24Google Scholar
  74. Zhow W, Yang F, Zhu Y (2015) A transformation method of OPM Model to CPN model for system concept development. In: Proceedings of the First International Conference on Information Science and Electronic Technology (ISET)Google Scholar
  75. Ziv-Av A, Reich Y (2005) SOS–subjective objective system for generating optimal product concepts. Des Stud 26(5):509–533Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Sustainability in Manufacturing and Life Cycle Engineering Research Group, School of Mechanical and Manufacturing EngineeringUniversity of New South WalesSydneyAustralia

Personalised recommendations