Research Article
BibTex RIS Cite
Year 2024, , 1359 - 1384, 01.09.2024
https://doi.org/10.35378/gujs.1344068

Abstract

References

  • [1] Gershwin, S. B., “The future of manufacturing systems engineering”, International Journal of Production Research, 56(1-2): 224-237, (2018).
  • [2] Dallery, Y., Gershwin, S. B., “Manufacturing flow line systems: a review of models and analytical results”, Queueing Systems, 12: 3-94, (1992).
  • [3] Glassey, C. R., Hong, Y., “Analysis of behaviour of an unreliable n-stage transfer line with (n−1) inter-stage storage buffers”, International Journal of Production Research, 31(3): 519-530, (1993).
  • [4] Zhang, Y., Zhao, M., Zhang, Y., Pan, R., and Cai, J., “Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers”, European Journal of Operational Research, 283(2): 491-510, (2020).
  • [5] Roser, C., Nakano, M., and Tanaka, M., “A practical bottleneck detection method”, Proceeding of the Winter Simulation Conference, 949-953, (2001).
  • [6] Staley, D. R., Kim, D. S., “Experimental results for the allocation of buffers in closed serial production lines”, International Journal of Production Economics, 137(2): 284-291, (2012).
  • [7] Gershwin, S. B., Schick, I. C., “Modeling and analysis of three-stage transfer lines with unreliable machines and finite buffers”, Operations Research, 31(2): 354-380, (1983).
  • [8] Weiss, S., Matta, A., and Stolletz, R., “Optimization of buffer allocations in flow lines with limited supply”, IISE Transactions, 50(3): 191-202, (2018).
  • [9] Weiss, S., Schwarz, J. A., and Stolletz, R., “The buffer allocation problem in production lines: Formulations, solution methods, and instances”, IISE Transactions, 51(5): 456-485, (2019).
  • [10] Buzacott, J. A., Hanifin, L. E., “Models of automatic transfer lines with inventory banks a review and comparison”, AIIE Transactions, 10(2): 197-207, (1978).
  • [11] Gershwin S. B., Berman, O., “Analysis of transfer lines consisting of two unreliable machines with random processing times and finite storage buffers”, AIIE Transactions, 13: 2-11, (1981).
  • [12] Altıok, T., “Approximate analysis of exponential tandem queues with blocking”, European Journal of Operational Research, 11(4): 390-398, (1982).
  • [13] Ho, Y. C., Cassandras, C., “A new approach to the analysis of discrete event dynamic systems”, Automatica, 19(2): 149-167, (1983).
  • [14] Gershwin, S. B., “An Efficient Decomposition Method for the Approximate Evaluation of Tandem Queues with Finite Storage Space and Blocking”, Operations Research, 35(2): 291-305, (1987).
  • [15] De Koster, M. B. M., “Estimation of line efficiency by aggregation”, International Journal of Production Research, 25: 615-626, (1987).
  • [16] Li, J., Meerkov, S. M., “Production Systems Engineering”, Springer, New York, (2009).
  • [17] Dallery, Y., David, R., and Xie, X. L., “An efficient algorithm for analysis of transfer lines with unreliable machines and finite buffers”, IIE Transactions, 20(3): 280-283, (1988).
  • [18] Dallery, Y., David, R., and Xie, X. L., “Approximate analysis of transfer lines with unreliable machines and finite buffers”, IEEE Transactions on Automatic Control, 34(9): 943-953, (1989).
  • [19] Lim, J. T., Meerkov, S. M., and Top, F., “Homogeneous, asymptotically reliable serial production lines: theory and a case study”, IEEE Transactions on Automatic Control, 35(5): 524-534, (1990).
  • [20] Burman, M. H., “New results in flow line analysis”, Ph.D. Thesis, MIT, Cambridge MA, (1995).
  • [21] Hanifin, L. E., “Increased Transfer Line Productivity Utilizing Systems Simulation”, Ph.D. Thesis, University of Detroit, Detroit, (1975).
  • [22] Le Bihan, H., Dallery, Y., “A robust decomposition method for the analysis of production lines with unreliable machines and finite buffers”, Annals of Operations Research 93: 265-297, (2000).
  • [23] Dallery, Y., Le Bihan, H., “An improved decomposition method for the analysis of production lines with unreliable machines and finite buffers”, International Journal of Production Research, 37(5): 1093-1117, (1999).
  • [24] Li, J., Blumenfeld, D. E., and Alden, J. M., “Comparisons of two-machine line models in throughput analysis”, International Journal of Production Research, 44(7): 1375-1398, (2006).
  • [25] Xia, B., Xi, L., and Zhou, B., “An improved decomposition method for evaluating the performance of transfer lines with unreliable machines and finite buffers”, International Journal of Production Research, 50(15): 4009-4024, (2012).
  • [26] Göttlich, S., Kühn, S., Schwarz, J. A., and Stolletz, R., “Approximations of time-dependent unreliable flow lines with finite buffers”, Mathematical Methods of Operations Research, 83: 295-323, (2016).
  • [27] Matta, A., Simone, F., “Analysis of two-machine lines with finite buffer, operation-dependent and time-dependent failure modes”, International Journal of Production Research, 54(6): 1850-1862, (2016).
  • [28] Li, L., Qian, Y., Du, K., and Yang, Y., “Analysis of approximately balanced production lines”, International Journal of Production Research, 54(3): 647-664, (2016).
  • [29] Adomian, G., “A review of the decomposition method in applied mathematics. Journal of Mathematical Analysis and Applications”, 135(2): 501-544, (1988).
  • [30] Demir, L., Tunali, S., and Løkketangen, A., “A tabu search approach for buffer allocation in production lines with unreliable machines”, Engineering Optimization, 43(2): 213-231, (2011).
  • [31] Gershwin, S. B., Schor, J., “Efficient algorithms for buffer space allocation”, Annals of Operations Research, 93(1-4): 117-144, (2000).
  • [32] Derrac, J., García, S., Molina, D., and Herrera, F., “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms”, Swarm and Evolutionary Computation, 1(1): 3-18, (2011).
  • [33] Koyuncuoğlu, M. U., Demir, L., “An adaptive hybrid variable-large neighborhood search algorithm for profit maximization problem in designing production lines”, Computers & Industrial Engineering, 175: 108871, (2023).
  • [34] Massim, Y., Yalaoui, F., Amodeo, L., Chatelet, E., and Zeblah, A., “Efficient combined immune-decomposition algorithm for optimal buffer allocation in production lines for throughput and profit maximization”, Computers and Operations Research, 37(4): 611-620, (2010).
  • [35] Shaaban, S., Romero-Silva, R., “Performance of merging lines with uneven buffer capacity allocation: the effects of unreliability under different inventory-related costs”, Central European Journal of Operations Research, 29(4): 1253-1288, (2021).

A Computational Analysis of Long Transfer Line Behavior

Year 2024, , 1359 - 1384, 01.09.2024
https://doi.org/10.35378/gujs.1344068

Abstract

Meeting customer demands for order-based production and make‐to‐stock production policies against holding and non-holding costs are fundamental functions for businesses to ensure. For these policies, finite capacity buffers between machines is of great importance. WIP, production rate and profit values, the key performance indicators of the transfer line, affect the sustainable economics of companies. It is important to investigate how the production rate, one of the most important performance indicators, and its CPU time are affected by the reliability parameters of the machines, the convergence rate and the analytical methods applied. In this study, the theoretical computational convergence analysis of the Dallery-David-Xie (DDX) algorithm is conducted on balanced transfer lines consisting 20, 30 and 50-machines with four different reliability parameters, each having finite buffers. The results show that the performance of the DDX algorithm is very sensitive to the convergence rate. The CPU times spent based on the different convergence rates used in the applied DDX algorithm significantly differ from each other at a 95% confidence interval. Additionally, the study investigates uniformly, ascending order and descending order buffer distributions to maximize the profit value and minimize WIP in the transfer line. The initial buffer configuration affects the key performance indicators on balanced transfer lines with different reliability parameters.

References

  • [1] Gershwin, S. B., “The future of manufacturing systems engineering”, International Journal of Production Research, 56(1-2): 224-237, (2018).
  • [2] Dallery, Y., Gershwin, S. B., “Manufacturing flow line systems: a review of models and analytical results”, Queueing Systems, 12: 3-94, (1992).
  • [3] Glassey, C. R., Hong, Y., “Analysis of behaviour of an unreliable n-stage transfer line with (n−1) inter-stage storage buffers”, International Journal of Production Research, 31(3): 519-530, (1993).
  • [4] Zhang, Y., Zhao, M., Zhang, Y., Pan, R., and Cai, J., “Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers”, European Journal of Operational Research, 283(2): 491-510, (2020).
  • [5] Roser, C., Nakano, M., and Tanaka, M., “A practical bottleneck detection method”, Proceeding of the Winter Simulation Conference, 949-953, (2001).
  • [6] Staley, D. R., Kim, D. S., “Experimental results for the allocation of buffers in closed serial production lines”, International Journal of Production Economics, 137(2): 284-291, (2012).
  • [7] Gershwin, S. B., Schick, I. C., “Modeling and analysis of three-stage transfer lines with unreliable machines and finite buffers”, Operations Research, 31(2): 354-380, (1983).
  • [8] Weiss, S., Matta, A., and Stolletz, R., “Optimization of buffer allocations in flow lines with limited supply”, IISE Transactions, 50(3): 191-202, (2018).
  • [9] Weiss, S., Schwarz, J. A., and Stolletz, R., “The buffer allocation problem in production lines: Formulations, solution methods, and instances”, IISE Transactions, 51(5): 456-485, (2019).
  • [10] Buzacott, J. A., Hanifin, L. E., “Models of automatic transfer lines with inventory banks a review and comparison”, AIIE Transactions, 10(2): 197-207, (1978).
  • [11] Gershwin S. B., Berman, O., “Analysis of transfer lines consisting of two unreliable machines with random processing times and finite storage buffers”, AIIE Transactions, 13: 2-11, (1981).
  • [12] Altıok, T., “Approximate analysis of exponential tandem queues with blocking”, European Journal of Operational Research, 11(4): 390-398, (1982).
  • [13] Ho, Y. C., Cassandras, C., “A new approach to the analysis of discrete event dynamic systems”, Automatica, 19(2): 149-167, (1983).
  • [14] Gershwin, S. B., “An Efficient Decomposition Method for the Approximate Evaluation of Tandem Queues with Finite Storage Space and Blocking”, Operations Research, 35(2): 291-305, (1987).
  • [15] De Koster, M. B. M., “Estimation of line efficiency by aggregation”, International Journal of Production Research, 25: 615-626, (1987).
  • [16] Li, J., Meerkov, S. M., “Production Systems Engineering”, Springer, New York, (2009).
  • [17] Dallery, Y., David, R., and Xie, X. L., “An efficient algorithm for analysis of transfer lines with unreliable machines and finite buffers”, IIE Transactions, 20(3): 280-283, (1988).
  • [18] Dallery, Y., David, R., and Xie, X. L., “Approximate analysis of transfer lines with unreliable machines and finite buffers”, IEEE Transactions on Automatic Control, 34(9): 943-953, (1989).
  • [19] Lim, J. T., Meerkov, S. M., and Top, F., “Homogeneous, asymptotically reliable serial production lines: theory and a case study”, IEEE Transactions on Automatic Control, 35(5): 524-534, (1990).
  • [20] Burman, M. H., “New results in flow line analysis”, Ph.D. Thesis, MIT, Cambridge MA, (1995).
  • [21] Hanifin, L. E., “Increased Transfer Line Productivity Utilizing Systems Simulation”, Ph.D. Thesis, University of Detroit, Detroit, (1975).
  • [22] Le Bihan, H., Dallery, Y., “A robust decomposition method for the analysis of production lines with unreliable machines and finite buffers”, Annals of Operations Research 93: 265-297, (2000).
  • [23] Dallery, Y., Le Bihan, H., “An improved decomposition method for the analysis of production lines with unreliable machines and finite buffers”, International Journal of Production Research, 37(5): 1093-1117, (1999).
  • [24] Li, J., Blumenfeld, D. E., and Alden, J. M., “Comparisons of two-machine line models in throughput analysis”, International Journal of Production Research, 44(7): 1375-1398, (2006).
  • [25] Xia, B., Xi, L., and Zhou, B., “An improved decomposition method for evaluating the performance of transfer lines with unreliable machines and finite buffers”, International Journal of Production Research, 50(15): 4009-4024, (2012).
  • [26] Göttlich, S., Kühn, S., Schwarz, J. A., and Stolletz, R., “Approximations of time-dependent unreliable flow lines with finite buffers”, Mathematical Methods of Operations Research, 83: 295-323, (2016).
  • [27] Matta, A., Simone, F., “Analysis of two-machine lines with finite buffer, operation-dependent and time-dependent failure modes”, International Journal of Production Research, 54(6): 1850-1862, (2016).
  • [28] Li, L., Qian, Y., Du, K., and Yang, Y., “Analysis of approximately balanced production lines”, International Journal of Production Research, 54(3): 647-664, (2016).
  • [29] Adomian, G., “A review of the decomposition method in applied mathematics. Journal of Mathematical Analysis and Applications”, 135(2): 501-544, (1988).
  • [30] Demir, L., Tunali, S., and Løkketangen, A., “A tabu search approach for buffer allocation in production lines with unreliable machines”, Engineering Optimization, 43(2): 213-231, (2011).
  • [31] Gershwin, S. B., Schor, J., “Efficient algorithms for buffer space allocation”, Annals of Operations Research, 93(1-4): 117-144, (2000).
  • [32] Derrac, J., García, S., Molina, D., and Herrera, F., “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms”, Swarm and Evolutionary Computation, 1(1): 3-18, (2011).
  • [33] Koyuncuoğlu, M. U., Demir, L., “An adaptive hybrid variable-large neighborhood search algorithm for profit maximization problem in designing production lines”, Computers & Industrial Engineering, 175: 108871, (2023).
  • [34] Massim, Y., Yalaoui, F., Amodeo, L., Chatelet, E., and Zeblah, A., “Efficient combined immune-decomposition algorithm for optimal buffer allocation in production lines for throughput and profit maximization”, Computers and Operations Research, 37(4): 611-620, (2010).
  • [35] Shaaban, S., Romero-Silva, R., “Performance of merging lines with uneven buffer capacity allocation: the effects of unreliability under different inventory-related costs”, Central European Journal of Operations Research, 29(4): 1253-1288, (2021).
There are 35 citations in total.

Details

Primary Language English
Subjects Manufacturing Management, Stochastic (Probability ) Process, Optimization in Manufacturing
Journal Section Industrial Engineering
Authors

Mehmet Ulaş Koyuncuoğlu 0000-0002-5437-1865

Early Pub Date April 2, 2024
Publication Date September 1, 2024
Published in Issue Year 2024

Cite

APA Koyuncuoğlu, M. U. (2024). A Computational Analysis of Long Transfer Line Behavior. Gazi University Journal of Science, 37(3), 1359-1384. https://doi.org/10.35378/gujs.1344068
AMA Koyuncuoğlu MU. A Computational Analysis of Long Transfer Line Behavior. Gazi University Journal of Science. September 2024;37(3):1359-1384. doi:10.35378/gujs.1344068
Chicago Koyuncuoğlu, Mehmet Ulaş. “A Computational Analysis of Long Transfer Line Behavior”. Gazi University Journal of Science 37, no. 3 (September 2024): 1359-84. https://doi.org/10.35378/gujs.1344068.
EndNote Koyuncuoğlu MU (September 1, 2024) A Computational Analysis of Long Transfer Line Behavior. Gazi University Journal of Science 37 3 1359–1384.
IEEE M. U. Koyuncuoğlu, “A Computational Analysis of Long Transfer Line Behavior”, Gazi University Journal of Science, vol. 37, no. 3, pp. 1359–1384, 2024, doi: 10.35378/gujs.1344068.
ISNAD Koyuncuoğlu, Mehmet Ulaş. “A Computational Analysis of Long Transfer Line Behavior”. Gazi University Journal of Science 37/3 (September 2024), 1359-1384. https://doi.org/10.35378/gujs.1344068.
JAMA Koyuncuoğlu MU. A Computational Analysis of Long Transfer Line Behavior. Gazi University Journal of Science. 2024;37:1359–1384.
MLA Koyuncuoğlu, Mehmet Ulaş. “A Computational Analysis of Long Transfer Line Behavior”. Gazi University Journal of Science, vol. 37, no. 3, 2024, pp. 1359-84, doi:10.35378/gujs.1344068.
Vancouver Koyuncuoğlu MU. A Computational Analysis of Long Transfer Line Behavior. Gazi University Journal of Science. 2024;37(3):1359-84.