Advertisement

Machine layout design problem under product family evolution in reconfigurable manufacturing environment: a two-phase-based AMOSA approach

  • Hichem Haddou BenderbalEmail author
  • Lyes BenyoucefEmail author
ORIGINAL ARTICLE
  • 156 Downloads

Abstract

Reconfigurable manufacturing systems (RMSs) are designed to manufacture a specific product, incorporating the scalability to other products in the same family. This ability is based primarily on the reconfiguration capabilities offered by reconfigurable machines tools (RMTs). This paper addresses one of the most important aspects related to the reactivity of RMSs. More specifically, it considers the relations, which link the conceived system with two important environments: its logical environment, i.e., the product family (products that share similarities) in which the RMS can evolve, and its physical environment, i.e., the physical workshop that implements this RMS. We study the machine layout problem by considering the product family evolution where two sub-problems are addressed. The first sub-problem concerns the evolution of the product, in the same family, towards new products to meet the evolutions and the requirements of the customers. The second sub-problem deals with the machine layout problem based on the results of the first sub-problem. For this, our two-phase-based approach combines the well-known metaheuristic, archived multi-objective simulated annealing (AMOSA), with an exhaustive search–based heuristic to determine the best machine layout for all the selected machines of the product family. The developed approach is based on the initially generated process plans of products (in the product family) for the RMS design under performance metrics. Moreover, the proposed layout must, at its best, respect both the constraints imposed by the generated process plans and those depicting the available location in the shop floor where machines can be placed. An illustrative numerical example is proposed to demonstrate the applicability of our approach.

Keywords

Reconfigurable manufacturing system (RMS) Changeable manufacturing system Layout Machine layout design Machine importance index Performance metrics AMOSA 

Notes

References

  1. 1.
    Altuntas S, Selim H (2012) Facility layout using weighted association rule-based data mining algorithms: evaluation with simulation. Expert Syst Appl 39(1):3–13CrossRefGoogle Scholar
  2. 2.
    Andersen AL, Brunoe TD, Nielsen K (2015) Reconfigurable manufacturing on multiple levels: literature review and research directions. In: IFIP international conference on advances in production management systems. Springer, Berlin, pp 266–273Google Scholar
  3. 3.
    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
  4. 4.
    Ashraf M, Hasan F (2016) Product family formation for RMS - a review. In: In proceedings of the NCMEI3. Aligarh Muslim University, AligarhGoogle Scholar
  5. 5.
    Azevedo MM, Crispim JA, de Sousa JP (2017) A dynamic multi-objective approach for the reconfigurable multi-facility layout problem. J Manuf Syst 42:140–152CrossRefGoogle Scholar
  6. 6.
    Bandyopadhyay S, Saha S, Maulik U, Deb K (2008) A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans Evol Comput 12(3):269–283CrossRefGoogle Scholar
  7. 7.
    Battaïa O, Dolgui A, Guschinsky N (2017) Decision support for design of reconfigurable rotary machining systems for family part production. Int J Prod Res 55:1368–1385CrossRefGoogle Scholar
  8. 8.
    Benjaafar S, Heragu SS, Irani SA (2002) Next generation factory layouts: research challenges and recent progress. Interfaces 32(6):58–76CrossRefGoogle Scholar
  9. 9.
    Bensmaine A, Mohammed D, Lyes B (2013) A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Comput Ind Eng 66(3):519–524Google Scholar
  10. 10.
    Bi ZM, Lang SY, Shen W, Wang L (2008) Reconfigurable manufacturing systems: the state of the art. Int J Prod Res 46:967–992CrossRefzbMATHGoogle Scholar
  11. 11.
    Bortolini M, Galizia FG, Mora C (2018) Reconfigurable manufacturing systems: literature review and research trend. J Manuf Syst 49:93–106CrossRefGoogle Scholar
  12. 12.
    Chaube A, Lyés B, Manoj KT (2012) An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system. J Intell Manuf 23(4):1141–1155Google Scholar
  13. 13.
    Devise O, Pierreval H (2000) Indicators for measuring performances of morphology and material handling systems in flexible manufacturing systems. Int J Prod Econ 64(1):209–218CrossRefGoogle Scholar
  14. 14.
    Dou J, Dai X, Meng Z (2010) Optimisation for multi-part flow-line configuration of re-configurable manufacturing systems using GA. Int J Prod Res 48(14):4071–4100CrossRefzbMATHGoogle Scholar
  15. 15.
    Drira A, Pierreval H, Hajri-Gabouj S (2007) Facility layout problems: a survey. Annu Rev Control 31(2):255–267CrossRefGoogle Scholar
  16. 16.
    ElMaraghy HA (2007) Reconfigurable process plans for responsive manufacturing systems. In: Digital enterprise technology. Springer, New York, pp 35–44CrossRefGoogle Scholar
  17. 17.
    Gadalla M, Xue D (2018) An approach to identify the optimal configurations and reconfiguration processes for design of reconfigurable machine tools. Int J Prod Res 56(11):3880–3900CrossRefGoogle Scholar
  18. 18.
    Galan R, Racero J, Eguia I, Garcia JM (2007) A systematic approach for product families formation in reconfigurable manufacturing systems. Robot Comput Integr Manuf 23(5):489–502CrossRefGoogle Scholar
  19. 19.
    Goyal KK, Jain PK (2016) Design of reconfigurable flow lines using MOPSO and maximum deviation theory. Int J Adv Manuf Technol 84(5–8):1587–1600Google Scholar
  20. 20.
    Goyal KK, Jain PK, Jain M (2013) A comprehensive approach to operation sequence similarity based part family formation in the reconfigurable manufacturing system. Int J Prod Res 51(6):1762–1776CrossRefGoogle Scholar
  21. 21.
    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(1):98–108CrossRefGoogle Scholar
  22. 22.
    Haddou Benderbal H, Dahane M, Benyoucef L (2017a) Flexibility based multi-objective approach for machines selection in reconfigurable manufacturing system (RMS) design under unavailability constraints. Int J Prod Res 55(20):6033–6051CrossRefGoogle Scholar
  23. 23.
    Haddou Benderbal H, Dahane M, Benyoucef L (2017b) Layout evolution effort for product family in reconfigurable manufacturing system design. IFAC-PapersOnLine 50(1):10166–10171 ISSN 2405-8963CrossRefGoogle Scholar
  24. 24.
    Haddou Benderbal H, Dahane M, Benyoucef L (2018a) Exhaustive search based heuristic for solving machine layout problem in reconfigurable manufacturing system design. IFAC-Papers 51(11):78–83 ISSN 2405-8963CrossRefGoogle Scholar
  25. 25.
    Haddou Benderbal H, Dahane M, Benyoucef L (2018b) 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
  26. 26.
    Heragu S, Zijm WHM, Meng G, Heragu SS, van Ommeren JCW, van Houtum GJ (2001) Design and analysis of reconfigurable layout systems. (Memorandum faculteit TW; no. 1604). Stochastic Operations Research (SOR), EnschedeGoogle Scholar
  27. 27.
    Huang L, Gao Y, Qian F, Tang S, Wang D (2011) Configuration selection for reconfigurable manufacturing systems by means of characteristic state space. Chin J Mech Eng 24(1):23CrossRefGoogle Scholar
  28. 28.
    Kashkoush M, ElMaraghy H (2014) Product family formation for reconfigurable assembly systems. Procedia CIRP 17:302–307CrossRefGoogle Scholar
  29. 29.
    Koren Y (2010) The global manufacturing revolution: product-process-business integration and reconfigurable systems, vol 80. Wiley, HobokenCrossRefGoogle Scholar
  30. 30.
    Koren Y, Shpitalni M (2010) Design of reconfigurable manufacturing systems. J Manuf Syst 29(4):130–141CrossRefGoogle Scholar
  31. 31.
    Koren Y, Wang W, Gu X (2017) Value creation through design for scalability of reconfigurable manufacturing systems. Int J Prod Res 55(5):1227–1242CrossRefGoogle Scholar
  32. 32.
    Koren Y, Gu X, Guo W (2018a) Reconfigurable manufacturing systems: principles, design, and future trends. Front Mech Eng 13(2):121–136CrossRefGoogle Scholar
  33. 33.
    Koren Y, Gu X, Guo W (2018b) Choosing the system configuration for high-volume manufacturing. Int J Prod Res 56(1–2):476–490CrossRefGoogle Scholar
  34. 34.
    Maganha I, Silva C, Ferreira LMD (2018) Understanding reconfigurability of manufacturing systems: an empirical analysis. J Manuf Syst 48:120–130CrossRefGoogle Scholar
  35. 35.
    Maniraj M, Pakkirisamy V, Parthiban P (2014) Optimisation of process plans in re-configurable manufacturing systems using ant colony technique. Int J Enterprise Netw Manag 6(2):125–138CrossRefGoogle Scholar
  36. 36.
    Musharavati F, Hamouda AMS (2012a) Enhanced simulated-annealing-based algorithms and their applications to process planning in reconfigurable manufacturing systems. Adv Eng Softw 45(1):80–90CrossRefGoogle Scholar
  37. 37.
    Musharavati F, Hamouda AMS (2012b) Simulated annealing with auxiliary knowledge for process planning optimization in reconfigurable manufacturing. Robot ComputIntegr Manuf 28(2):113–131Google Scholar
  38. 38.
    Nallakumarasamy G, Srinivasan PSS, Raja KV, Malayalamurthi R (2011) Optimization of operation sequencing in CAPP using superhybrid genetic algorithms-simulated annealing technique. ISRN Mech Eng 2011:1–7CrossRefGoogle Scholar
  39. 39.
    Pattanaik LN, Kumar V (2011) Product family formation for reconfigurable manufacturing using a bi-criterion evolutionary algorithm. Int J Ind Eng Theory Appl Pract 18(9):493–505Google Scholar
  40. 40.
    Rehman AU, Babu AS (2013) Reconfigurations of manufacturing systems—an empirical study on concepts, research, and applications. Int J Adv Manuf Technol 66:107–124CrossRefGoogle Scholar
  41. 41.
    Renzi C, Leali F, Cavazzuti M, Andrisano A (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
  42. 42.
    Saxena LK, Jain PK (2012) A model and optimisation approach for reconfigurable manufacturing system configuration design. Int J Prod Res 50(12):3359–3381CrossRefGoogle Scholar
  43. 43.
    Shabaka A, ElMaraghy HA (2007) Generation of machine configurations based on product features. Int J Comput Integr Manuf 20:355–369CrossRefGoogle Scholar
  44. 44.
    Sharma P, Singhal S (2016) Design and evaluation of layout alternatives to enhance the performance of industry. OPSEARCH 53(4):741–760CrossRefzbMATHGoogle Scholar
  45. 45.
    Singh SP, Sharma RR (2006) A review of different approaches to the facility layout problems. Int J Adv Manuf Technol 30(5–6):425–433CrossRefGoogle Scholar
  46. 46.
    Talbi EG (2009) Metaheuristics: from design to implementation, vol 74. Wiley, HobokenCrossRefzbMATHGoogle Scholar
  47. 47.
    Touzout FA, Benyoucef L (2018a) Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches. Int J Prod Res:1–17Google Scholar
  48. 48.
    Touzout FA, Benyoucef L (2018b) Sustainable multi-unit process plan generation in a reconfigurable manufacturing environment: a comparative study of three hybrid-meta-heuristics. In 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), vol 1. IEEE, pp 661–668Google Scholar
  49. 49.
    Wang GX, Huang SH, Shang XW, Yan Y, Du JJ (2016) Formation of part family for re-configurable manufacturing systems considering bypassing moves and idle machines. J Manuf Syst 41:120–129CrossRefGoogle Scholar
  50. 50.
    Wang W, Yoram K (2013) Design principles of scalable reconfigurable manufacturing systems. IFAC Proceedings Volumes 46(9):1411–1416Google Scholar
  51. 51.
    Yang T, Brett AP, Mingan T (2005) Layout design for flexible manufacturing systems considering single-loop directional flow patterns. Eur J Oper Res 164(2):440–455Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Aix-Marseille University, University of Toulon, CNRS, LISMarseilleFrance

Personalised recommendations