Skip to main content

Adaptation of the simulated annealing optimization algorithm to achieve improved near-optimum objective function values and computation times for multiple component manufacture

The International Journal of Advanced Manufacturing Technology volume 60, pages 437–451 (2012)Cite this article

Abstract

The paper concerns the development of the Simulated Annealing Algorithm (SAA) for the sequencing of cutter tool movement in machine tools capable of manufacturing many components, located on a box-like jig/pallet, in a single setting using a multiple tool magazine. The objective of the SAA is to minimise the total machine tool residence time. The general SAA has been enhanced, to achieve lower values of the objective function during the iterative scheme, and hence improve solution accuracy; and, to reduce computation time by cessation of the iterative scheme when no further improvement in the objective function occurs. The reconfigured SAA has been evaluated using a number of case studies. The results show that a reduction in the objective function value can be achieved in up to 6%, with far less computational effort. In addition, it is shown that the computation time can be reduced by a factor of between 20% and 72%. The improvement in the objective function value and the computational speed depends on the complexity of the problem posed to the SAA software.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

References

  1. Yueh-Jaw L, Mahabaleshwarkar R, Massina E (2001) CAD based CMM dimensional inspection path planning—a generic algorithm’. Robotica 19(2):137–148

    Google Scholar 

  2. Saravanan R, Siva Sankar R, Asokan P, Vijayakumar K, Prabhaharan G (2005) Optimization of cutting conditions during continuous finished profile machining using non-traditional techniques. Int J Adv Manuf Technol 26(1–2):30–40

    Article  Google Scholar 

  3. Zamani R (2010) A parallel complete anytime procedure for project scheduling under multiple resource constraints. Int J Adv Manuf Technol 50(1–4):353–362

    Article  Google Scholar 

  4. 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 

  5. Bachlaus M, Tiwari MK, Shankar R (2008) Sequencing of parts on single-stage multifunctional machining systems using chaos-embedded simulated annealing algorithm. Int J Prod Res 46(12):3387–3413

    Article  MATH  Google Scholar 

  6. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH (1953) Equation of state calculation by fast computing machine. J Chem Phys 21:1087–1092

    Article  Google Scholar 

  7. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    MathSciNet  Article  MATH  Google Scholar 

  8. Cerny V (1985) Thermodynamical approach to the travelling salesman problem: an efficient simulated annealing algorithm. J Optim Theor Appl 45:41–51

    MathSciNet  Article  MATH  Google Scholar 

  9. Jahangirian M, Eldabi T, Naseer A, Stergioulas LK, Young T (2010) Simulation in manufacturing and business: a review. Eur J Oper Res 203(1):1–13

    Article  Google Scholar 

  10. Varadharajan TK, Rajendran C (2005) A multi-objective simulated-annealing algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs. Eur J Oper Res 167(3):772–795

    MathSciNet  Article  MATH  Google Scholar 

  11. Liu C, Wu J (1993) Machine cell formation: using the simulated annealing algorithm. Int J Comput Integrated Manuf 6:335–349

    Article  Google Scholar 

  12. Vakharia AJ, Chang Y (1990) A simulated annealing approach to scheduling manufacturing cell. Nav Res Logist 37:559–577

    MathSciNet  Article  MATH  Google Scholar 

  13. Tam KY (1992) A simulated annealing algorithm for allocating space to manufacturing cells. Int J Prod Res 30:63–87

    Article  MATH  Google Scholar 

  14. Laarhoven PJM, Aarts EHL, Lenstra JK (1992) Job shop scheduling by simulated annealing. Oper Res 40(1):113–125

    MathSciNet  Article  MATH  Google Scholar 

  15. Peyrol E, Floquet P, Pibouleau L, Domenech S (1993) Scheduling and simulated annealing application to a semiconductor circuit fabrication plant. Comput Chem Eng 17(Supplement):S39–S44

    Google Scholar 

  16. Ben-Arieh D, Maimon O (1992) Annealing method for PCB assembly scheduling on two sequential machines. Int J Comput Integrated Manuf 5:361–367

    Article  Google Scholar 

  17. Zhang D, Liu Y, M’Hallah R, Leung SCH (2010) A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems. Eur J Oper Res 203(3):550–558

    Article  MATH  Google Scholar 

  18. Pedamallu CS, Ozdamar L (2008) Investigating a hybrid simulated annealing and local search algorithm for constrained optimization. Eur J Oper Res 185(3):1230–1245

    MathSciNet  Article  MATH  Google Scholar 

  19. Bard JF, Feo TA (1989) The Cutting and Tool Selection problem in computer aided process planning. J Manuf Syst 8:17–26

    Article  Google Scholar 

  20. Afifi AA, Hayhurst DR, Khan WA (2011) Non-productive tool path optimization of multi-tool part program. Int J Ad Man Tech 55:1007–1023

    Article  Google Scholar 

  21. Khan WA, Hayhurst DR (1991) Computer-aided part program segmentation and reconstruction for minimization of machine tool residence time. Int J Comput Integrated Manuf 4:300–314

    Article  Google Scholar 

  22. Afifi AA, Hayhurst DR (1995) Computer aided part program optimization of multi-component pallet residence time in a machining centre for canned cycles and cutter tool compensation. Int J Comput Integrated Manuf 8(1):1–20

    Article  Google Scholar 

  23. Afifi AA, Hayhurst DR, Khan WA (2011) Non-productive tool path optimization for four axis milling using the Simulated Annealing Algorithm. Int J Prod Res 49(17):5277–5302

    Article  Google Scholar 

  24. Lundy M (1985) Application of annealing algorithm to combinatorial problems in statistics. Biometrika 72:191–198

    Article  Google Scholar 

  25. Lundy M, Mees A (1986) Convergence of the annealing algorithm. Math Program 34:11–124

    MathSciNet  Article  Google Scholar 

  26. Matsuo H, Suh CJ, Sullivan RS (1989) A controlled search simulated annealing method for the single machine weighted tardiness problem. Ann Oper Res 21:85–108

    MathSciNet  Article  MATH  Google Scholar 

  27. Johnson DS, Aragon CR, McGeough LA, Schevon C (1989) Optimization by simulated annealing: an experimental evaluation: part 1, graph partitioning. Oper Res 37:865–891

    Article  MATH  Google Scholar 

  28. Sridhar J, Rajendran C (1993) Scheduling in a cellular manufacturing system: a simulated annealing approach. Int J Prod Res 31:2927–2945

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. German University in Cairo, Main entrance Al Tagamoa Al Khames, Al Khames, New Cairo, Egypt

    Atef Afifi Afifi

  2. Institute of Business Administration, City Campus, Garden Road, Karachi, 74400, Pakistan

    Wasim Ahmed Khan

  3. School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, George Begg Building C-004, Sackville Street, Manchester, M13 19PL, UK

    David R. Hayhurst

Authors
  1. Atef Afifi Afifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wasim Ahmed Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David R. Hayhurst
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David R. Hayhurst.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Afifi, A.A., Khan, W.A. & Hayhurst, D.R. Adaptation of the simulated annealing optimization algorithm to achieve improved near-optimum objective function values and computation times for multiple component manufacture. Int J Adv Manuf Technol 60, 437–451 (2012). https://doi.org/10.1007/s00170-011-3620-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3620-z

Keywords

  • Simulated annealing algorithm
  • Computationally efficient
  • Optimization
  • Multi-component manufacture