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JACIII Vol.14 No.5 pp. 431-441
doi: 10.20965/jaciii.2010.p0431
(2010)

Paper:

Effective Cycle Time: A Real World Balancing Index for Paced Assembly Lines

Konstantinos N. Genikomsakis and Vassilios D. Tourassis

Department of Production Engineering and Management, School of Engineering, Democritus University of Thrace, GR-67100, Kimmeria, Xanthi, Greece

Received:
September 16, 2009
Accepted:
March 1, 2010
Published:
July 20, 2010
Keywords:
assembly line balancing, ALB index, manufacturing simulation, flexible manufacturing systems
Abstract

Assembly Line Balancing (ALB) aims at optimally assigning the work elements required to assemble a product to an ordered sequence of workstations, while satisfying precedence constraints. Notwithstanding the advances and developments in ALB over the years, recent and thorough surveys on this field reveal that only a small percentage of companies employ ALB procedures to configure their assembly lines. This paradox may be attributed, to some extent, to the fact that ALB is addressed mostly under ideal conditions. Despite the time variability inherent in manufacturing tasks, there is a strong research trend towards designing and implementing algorithms that consider ALB on a deterministic basis and focus on the optimality of the proposed task assignments according to existing ALB performance measures. In this paper, the need to assess the performance of the proposed solutions of various algorithms in the literature is corroborated through simulation experiments on a benchmark ALB problem under more realistic conditions. A novel ALB index, namely the Effective Cycle Time, ECT, is proposed to assess the quality of alternative assembly line configurations in paced assembly lines operating under task times variations.

Cite this article as:
K. Genikomsakis and V. Tourassis, “Effective Cycle Time: A Real World Balancing Index for Paced Assembly Lines,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.5, pp. 431-441, 2010.
Data files:
References
  1. [1] N. Boysen, M. Fliedner, and A. Scholl, “Assembly line balancing: Which model to use when?,” Int. J. of Production Economics, Vol.111, pp. 509-528, 2008.
  2. [2] I. Baybars, “A survey of exact algorithms for the simple assembly line balancing,” Management Science, Vol.32, pp. 909-932, 1986.
  3. [3] N. Boysen, M. Fliedner, and A. Scholl, “A classification of assembly line balancing problems,” European J. of Operational Research, Vol.183, pp. 674-693, 2007.
  4. [4] A. Scholl and C. Becker, “State-of-the-art exact and heuristic solution procedures for simple assembly line balancing,” European J. of Operational Research, Vol.168, pp. 666-693, 2006.
  5. [5] B. Rekiek, A. Dolgui, A. Delchambre, and A. Bratcu, “State of art of optimization methods for assembly line design,” Annual Reviews in Control, Vol.26, pp. 163-174, 2002.
  6. [6] A. Costa, G. Celano, and S. Fichera, “Fuzzy scheduling of a flexible assembly line through evolutionary algorithms,” 2001 IEEE Int. Conf. on Systems, Man, and Cybernetics, October 7-10, 2001, Tucson, Arizona, pp. 2251-2256, 2001.
  7. [7] F. S. Hillier and R. W. Boling, “The effect of some design factors on the efficiency of production lines with variable operations times,” J. of Industrial Engineering, Vol.17, pp. 651-658, 1966.
  8. [8] F. S. Hillier and R. W. Boling, “On the optimal allocation of work in symmetrically unbalanced production line systems with variable operation times,” Management Science, Vol.25, pp. 721-728, 1979.
  9. [9] F. S. Hillier and K. C. So, “Some data for applying the bowl phenomenon to large production line systems,” Int. J. of Production Research, Vol.31, pp. 811-822, 1993.
  10. [10] T. L. Smunt and W. C. Perkins, “Stochastic unpaced line design: Review and further experimental results,” J. of Operations Management, Vol.5, pp. 351-373, 1985.
  11. [11] T. L. Smunt and W. C. Perkins, “Stochastic unpaced line design: A reply,” J. of Operations Management, Vol.8, pp. 55-62, 1989.
  12. [12] K. R. Karwan and P. R. Philipoom, “A note on “Stochastic unpaced line design: Review and further experimental results”,” J. of Operations Management, Vol.8, pp. 48-54, 1989.
  13. [13] F. S. Hillier and K. C. So, “On the robustness of the bowl phenomenon,” European J. of Operational Research, Vol.89, pp. 496-515, 1996.
  14. [14] K. N. Genikomsakis and V. D. Tourassis, “Using simulation to evaluate the assembly line balancing solutions proposed by heuristic algorithms under stochastic task times,” Proc. of the 5th Int. Conf. on the Management of Technological Changes, pp. 23-29, 2007.
  15. [15] K. N. Genikomsakis and V. D. Tourassis, “A Simulation-Based Assessment of Alternative Assembly Line Configurations,” Proc. of the 2008 IEEE Int. Conf. on Systems, Man and Cybernetics (SMC 2008), pp. 1626-1631, 2008.
  16. [16] M. Kilbridge and L. Wester, “The balance delay problem,” Management Science, Vol.8, pp. 69-84, 1961.
  17. [17] E. Erel and S. C. Sarin, “A survey of the assembly line balancing procedures,” Production Planning & Control, Vol.9, pp. 414-434, 1998.
  18. [18] S. G. Ponnambalam, P. Aravindan, and G. Mogileeswar Naidu, “A comparative evaluation of assembly line balancing heuristics,” Int. J. of Advanced Manufacturing Technology, Vol.15, pp. 577-586, 1999.
  19. [19] J. Driscoll and D. Thilakawardana, “The definition of assembly line balancing difficulty and evaluation of balance solution quality,” Robotics and Computer Integrated Manufacturing, Vol.17, pp. 81-86, 2001.
  20. [20] R. Rachamadugu and B. Talbot, “Improving the equality of workload assignments in assembly lines,” Int. J. of Production Research, Vol.29, pp. 619-633, 1991.
  21. [21] B. Rekiek, P. De Lit, and A. Delchambre, “Hybrid assembly line design and user’s preferences,” Int. J. of Production Research, Vol.40, pp. 1095-1111, 2002.
  22. [22] Y. J. Kim, Y. K. Kim, and Y. Cho, “A heuristic-based genetic algorithm for workload smoothing in assembly lines,” Computers and Operations Research, Vol.25, pp. 99-111, 1998.
  23. [23] A. L. Arcus, “COMSOAL: A computer method of sequencing operations for assembly lines,” Int. J. of Production Research, Vol.4, pp. 259-277, 1966.
  24. [24] R. V. Johnson, “A branch and bound algorithm for assembly line balancing problems with formulation irregularities,” Management Science, Vol.29, pp. 1309-1324, 1983.
  25. [25] C. Becker and A. Scholl, “A survey on problems and methods in generalized assembly line balancing,” European J. of Operational Research, Vol.168, pp. 694-715, 2006.
  26. [26] J. C. Carter and F. N. Silverman, “A cost-effective approach to stochastic line balancing with off-line repairs,” J. of Operations Management, Vol.4, pp. 145-157, 1984.
  27. [27] S. B. Liu, H. L. Ong, and H. C. Huang, “A bidirectional heuristic for stochastic assembly line balancing Type II problem,” Int. J. of Advanced Manufacturing Technology, Vol.25, pp. 71-77, 2005.
  28. [28] E. Erel, I. Sabuncuoglu, and H. Sekerci, “Stochastic assembly line balancing using beam search,” Int. J. of Production Research, Vol.43, pp. 1411-1426, 2005.
  29. [29] S. C. Sarin, E. Erel, and E. M. Dar-El, “A methodology for solving single-model, stochastic assembly line balancing problem,” Omega, Vol.27, pp. 525-535, 1999.
  30. [30] W. C. Chiang and T. L. Urban, “The stochastic U-line balancing problem: A heuristic procedure,” European J. of Operational Research, Vol.175, pp. 1767-1781, 2006.
  31. [31] Lanner Group, “WITNESS | Business Simulation Software System | Manufacturing and Production,”
    Site: http://www.lanner.com/en/witness.cfm ,
    Last access: September 2009.
  32. [32] M. Abramowitz and I. A. Stegun, “Handbook of mathematical functions with formulas, graphs, and mathematical tables,” New York: Wiley, 1972.

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Last updated on Dec. 05, 2019