Skip to main content

Advertisement

Log in

Multiple parts process planning in serial-parallel flexible flow lines: part I—process plan modeling framework

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In recent years, integrated process planning and scheduling models have been proposed as solutions that can bridge the gap between practical process planning and production scheduling. However, most structures of these models have been algorithm-based and hence may not be very useful when a problem contains process and operational aspects that are difficult to capture in an algorithm template. In dynamic manufacturing environments, examples of such aspects include process and operational flexibilities that enable manufacturers to cope with unexpected variations in production and product mix. Appropriate process planning models that take cognizance of such aspects can be proven more useful to human process planners. In this paper, an innovative multiple parts process planning (MPPP) model for solving process planning problems with process and operational flexibilities is introduced. This model strikes a balance between process- and operations-related meta-data in a bid to capture process and operational flexibilities in the search for an optimal process planning solution. Merits of this model are discussed with reference to the operations of a typical serial-parallel flexible flow line. An illustrative example of the modeling framework is outlined. In seeking a feasible solution, a relative comparative analysis is carried out between; (a) a simulated annealing (SA) algorithm and (b) a simulated annealing algorithm that implements a mutation operator. Results show that the SA algorithm with a mutation operator outperforms the SA algorithm without a mutation operator.

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

Access this article

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

  1. Mehrabi MG, Ulsoy AG, Koren Y, Heytler P (2002) Trends and perspectives in flexible and reconfigurable manufacturing systems. J Intell Manuf 13(2):135–146

    Article  Google Scholar 

  2. ElMaraghy HA (2006) Flexible and reconfigurable manufacturing systems paradigms. Int J Flex Manuf Syst 17:261–276

    Article  MATH  Google Scholar 

  3. Mehrabi MG, Ulsoy AG, Koren Y (2000) Reconfigurable manufacturing system and their enabling technologies. Int J Manuf Technol Manag 1(1):113–130

    Google Scholar 

  4. Zhang YF, Nee AYC (2001) Applications of genetic algorithms and simulated annealing in process planning optimization. In: J. Wang and A. Kusiak (ed) Computational intelligence in manufacturing handbook. Boca Raton, Florida, pp 9-1–9-26

  5. Lee H, Kim SS (2001) Integration of process planning and scheduling using simulation based genetic algorithms. Int J Adv Manuf Technol 18(8):586–590

    Article  Google Scholar 

  6. Kim YK, Park K, Ko J (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Comput Oper Res 30:1151–1171

    Article  MATH  MathSciNet  Google Scholar 

  7. Ro I, Kim J (1990) Multi-criteria operational control rules in flexible manufacturing systems (FMSs). Int J Prod Res 28(1):47–63

    Article  Google Scholar 

  8. Musharavati F (2008) Process planning optimization for reconfigurable manufacturing systems. Boca Raton, Florida USA

  9. ElMaraghy HA (2007) Reconfigurable process plans for responsive manufacturing systems. In: Cunha P.F. and Maropulos P.G. (eds) Digital enterprise technology: perspectives and future challenges. Springer Science, pp 35–44

  10. Azab A, ElMaraghy HA (2007) Mathematical modeling for reconfigurable process planning. Ann CIRP 56(1):467–472

    Article  Google Scholar 

  11. Azab A, Perusi G, ElMaraghy H, Urbanic J (2007) Semi-generative process planning for reconfigurable manufacturing. In: Cunha P.F. and Maropulos P.G. (eds) Digital enterprise technology: perspectives and future challenges. Springer Science, pp 251–258

  12. Xinyu L, Liang G, Xiaoyu W (2012) Application of an efficient modified particle swarm optimization algorithm for process planning. Int J Adv Manuf Technol. doi:10.1007/s00170-012-4572-7

    Google Scholar 

  13. Kunlei L, Chaoyong Z, Xinyu S, Liang G (2012) Optimization of process planning with various flexibilities using an imperialist competitive algorithm. Int J Adv Manuf Technol 59:815–828

    Article  Google Scholar 

  14. Musharavati F, Hamouda ASM (2011) Enhanced simulated‐annealing‐based algorithms and their applications to process planning in reconfigurable manufacturing systems. Adv Eng Softw 45(1):80–90

    Article  Google Scholar 

  15. Musharavati F, Hamouda ASM (2012) Simulated annealing with auxiliary knowledge for process planning optimization in reconfigurable manufacturing. J Robot Comput-Integr Manuf 28(2):113–131

    Google Scholar 

  16. Rauschecker U, Ford JS, and Athanssopoulou N (2013) Developing a vision for multi-site manufacturing system of systems. In: M.F. Zaeh (ed.) 5th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2013), Munich, Germany, 79

  17. Mehdi G, Bouzouia B, Achour N (2014) An evolutionary simulation-optimization approach to product-driven manufacturing control. In: Service orientation in holonic and multi-agent manufacturing and robotics. Springer International Publishing, 283–294

  18. Paulo L, Barbosa J, Trentesaux D (2012) Bio-inspired multi-agent systems for reconfigurable manufacturing systems. Eng Appl Artif Intell 25(5):934–944

    Article  Google Scholar 

  19. Hollnagel E, Woods DD, Levesson N (eds) (2006) Resilience engineering: concepts and precepts. Ashgate, Hampshire

    Google Scholar 

  20. Zhang WJ, Van Luttervelt CA (2011) Toward a resilient manufacturing system. CIRP Annals-Manuf Technol 60(1):469–472

    Article  Google Scholar 

  21. Yao H (2013) The modeling, analysis and control of resilient manufacturing enterprises. Dissertation, University of Kentucky

  22. Engelke W D (1987) How to integrate CAD/CAM systems: management and technology, pp. 237–238. CRC Press

  23. Cay F, Chassapis C An IT view on perspectives of computer aided process planning research. Comput Ind 34 (3): 307–337

  24. Xu X, Wang L, Newman ST (2011) Computer-aided process planning—a critical review of recent developments and future trends. Int J Comput Integr Manuf 24(1–3):1–31

    Article  MATH  Google Scholar 

  25. Phanden RK, Jain A, Verma R (2011) Integration of process planning and scheduling: a state-of-the-art review. Int J Comput Integr Manuf 24(6):517–534

    Article  Google Scholar 

  26. Rembold U, Nnaji BO, Storr A (1993) Computer integrated manufacturing and engineering. Addison-Wesley Longman

  27. Scallan P (2003) Process planning: the design/manufacture interface. Butterworth and Heinemann, Boston Massachusetts

    Book  Google Scholar 

  28. Tsujimura Y, Gen M (1999) Parts loading scheduling in flexible forging machine using advanced genetic algorithm. J Intell Manuf 10:149–159

    Article  Google Scholar 

  29. Guo Y, Mileham AR, Owen GW, Li WD (2006) Operation sequencing optimization using a particle swarm optimization approach. Proc Inst Mech Eng Part B: J Eng Manuf 220:945–1958

    Article  Google Scholar 

  30. Ma GH, Zhang YF, Nee AYC (2000) A simulated annealing-based optimization algorithm for process planning. Int J Prod Res 38(12):2671–2687

    Article  Google Scholar 

  31. Zijm WHM (1995) The integration of process planning and shop floor scheduling in small batch part manufacturing. Ann CIRP 44(1):429–432

    Article  Google Scholar 

  32. Lim MK, Zhang Z (2003) A multi-agent based manufacturing control strategy for responsive manufacturing. J Mater Process Technol 139:379–384

    Article  Google Scholar 

  33. Lim MK, Zhang Z (2004) An integrated agent-based approach for responsive control of manufacturing resources. Comput Ind Eng 46:221–232

    Article  Google Scholar 

  34. Palmer GJ (1996) A simulated annealing approach to integrated production scheduling. J Intell Manuf 7(3):163–176

    Article  Google Scholar 

  35. Musharavati F (2012) Process planning with embedded scheduling for multi‐parts production using simulated annealing. Int J Res Eng Technol 1(3):129–138

    Google Scholar 

  36. Freiheit T, Shpitalni M, Hu SJ, Koren Y (2004) Productivity of synchronized serial production lines with flexible reserve capacity. Int J Prod Res 42:2009–2027

    Article  MATH  Google Scholar 

  37. Lian KL, Zhang CY, Gao L, Xu ST, Sun Y (2011) A cooperative simulated annealing algorithm for the optimization of process planning. Adv Mat Res 181:489–494

    Article  Google Scholar 

  38. Nallakumarasamy G, Srinivasan PSS, Raja KV, Malayalamurthi R (2011) Optimization of operation sequencing in CAPP using simulated annealing technique (SAT). Int J Adv Manuf Technol 54(5–8):721–728

    Article  Google Scholar 

  39. Zhang F, Zhang YF, Nee AYC (1997) Using genetic algorithms in process planning for job shop machining. IEEE Trans Evol Comput 1(4):278–289

    Article  Google Scholar 

  40. Li L, Fuh JYH, Zhang YF, Nee AYC (2005) Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments. J Robot Comput-Integr Manuf 21(6):568–578

    Article  Google Scholar 

  41. Shao X, Li X, Gao L, Zhang C (2009) Integration of process planning and scheduling—a modified genetic algorithm-based approach. Comput Oper Res 36(6):2082–2096

    Article  MATH  Google Scholar 

  42. Qiao L, Shengping L (2012) An improved genetic algorithm for integrated process planning and scheduling. Int J Adv Manuf Technol 58(5–8):727–740

    Google Scholar 

  43. Musharavati F, Hamouda AMS (2011) Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines. Expert Syst Appl 38(9):10770–10779

    Article  Google Scholar 

  44. Li WD, Ong SK, Nee AYC (2002) Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int J Prod Res 40(8):1899–1922

    Article  MATH  Google Scholar 

  45. Ganesh K, Punniyamoorthy M (2005) Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing. Int J Adv Manuf Technol 26(1–2):148–154

    Article  Google Scholar 

  46. Wang ZG, Wong YS, Rahman M (2004) Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing. Int J Adv Manuf Technol 24(9–10):727–732

    Article  Google Scholar 

  47. Preux P, Talbi EG (1999) Towards hybrid evolutionary algorithms. Int Trans Oper Res 6(6):557–570

    Article  MathSciNet  Google Scholar 

  48. Zhang WJ, Ouyang PR, Sun Z H (2010) A novel hybridization design principle for intelligent mechatronics systems. In: Proceedings of international conference on advanced mechatronics (ICAM2010): 4–6

  49. Ding L, Yue Y, Ahmet K, Jackson M, Parkin R (2005) Global optimization of a feature-based process sequence using GA and ANN techniques. Int J Prod Res 43:3247–3272

    Article  MATH  Google Scholar 

  50. Huang W, Hu Y, Cai L (2012) An effective hybrid graph and genetic algorithm approach to process planning optimization for prismatic parts. Int J Adv Manuf Technol 62:1219–1232

    Article  Google Scholar 

  51. Orouji H, Haddad OB, Fallah-Mehdipour E, Mariño MA (2013) Extraction of decision alternatives in project management: application of hybrid PSO-SFLA. J Manag Eng 30(1):50–59

    Article  Google Scholar 

  52. Cakir B, Altiparmak F, Dengiz B (2011) Multi-objective optimization of a stochastic assembly line balancing: a hybrid simulated annealing algorithm. Comput Ind Eng 60(3):376–384

    Article  Google Scholar 

  53. Bettemir ÖH, Sonmez R (2014) Hybrid genetic algorithm with simulated annealing for resource-constrained project scheduling. Journal of Management in Engineering

  54. Salehi M, Bahreininejad A (2011) Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining. J Intell Manuf 22(4):643–652

    Article  Google Scholar 

  55. Ong SK, Ding J, Nee AYC (2002) Hybrid GA and SA dynamic set-up planning optimization. Int J Prod Res 40(18):4697–4719

    Article  MATH  Google Scholar 

  56. Nallakumarasamy G, Srinivasan PSS, Raja KV, Malayalamurthi R (2011) Optimization of operation sequencing in CAPP using superhybrid genetic algorithms-simulated annealing technique. ISRN Mechanical Engineering, 2011

  57. Mahmudy W F, Marian R M, and Luong L H (2013) Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms-part 1: modelling and representation. In: 5th IEEE International Conference on Knowledge and Smart Technology (KST), 2013 January: 75–80

  58. Turban E, Aronson J E (2001) Decision support systems and intelligent systems. New Jersey: Prentice Hall, 6th ed

  59. Onwubolu GC (2002) Emerging optimization techniques in production planning and control. Imperial College Press, London

    Book  MATH  Google Scholar 

  60. Wang JW, Wang HF, Ip WH, Furuta K, Kanno T, Zhang WJ (2013) Predatory search strategy based on swarm intelligence for continuous optimization problems. Mathematical Problems in Engineering

  61. Luong LHS, Kazerooni M, Abhary K. (2001) Genetic algorithms in manufacturing systems design. In: J. Wang and A. Kusiak (eds) Computational intelligence in manufacturing handbook. Boca Raton, Florida USA, pp. 6-1–6-25

  62. Hallinan J (2001) Feature selection and classification in the diagnosis of cervical cancer. In: Chambers L (ed) The practical handbook of genetic algorithms. Chapman and Hall/CRC, Boca Raton, pp 167–202

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. M. S. Hamouda.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Musharavati, F., Hamouda, A.M.S. Multiple parts process planning in serial-parallel flexible flow lines: part I—process plan modeling framework. Int J Adv Manuf Technol 78, 115–137 (2015). https://doi.org/10.1007/s00170-014-6616-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6616-7

Keywords

Navigation