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Disassembly Line Balancing Problem Under The Effects of Sum-of-Logarithm-Processing-Time-Based Learning and Job Deterioration

Yıl 2023, Cilt: 14 Sayı: 4, 683 - 695, 31.12.2023
https://doi.org/10.24012/dumf.1366117

Öz

Product recovery or remanufacturing has recently received a lot of attention as a result of rising environmental consciousness, economic worries, and laws and regulations. Disassembly is one of the most important processes in the recovery phase of end-of-life products. It's crucial to develop efficient and balanced disassembly lines. In this study, disassembly line balancing (DLB) problems are investigated under the effects of simultaneous sum-of-logarithm-processing-time-based learning and job deterioration. In the DLB literature, it is the first time to study simultaneous sum-of-logarithm-processing-time-based learning and job deterioration. Any job's processing time is influenced by the logarithmic sum of the processing times of its predecessors, in the station's order. Job deterioration delays the job start time. While learning reduces the processing time of jobs, deterioration increases. In this study, the objective function is the minimization of the number of opened stations. A hybrid PSO-GA algorithm has been developed to solve the DLB problem under the effects of learning and deterioration. Results for different learning and deterioration rates were obtained and comparisons were made. When the learning and deterioration effects were taken into account in DLB problems, improvements were observed in the objective function value.

Kaynakça

  • [1] S. Hezer and Y. Kara, “A network-based shortest route model for parallel disassembly line balancing problem,” Int. J. Prod. Res., vol. 53, no. 6, pp. 1849–1865, Mar. 2015.
  • [2] S. Agrawal and M. K. Tiwari, “A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem,” Int. J. Prod. Res., vol. 46, no. 6, pp. 1405–1429, Mar. 2008.
  • [3] Z. Li and M. N. Janardhanan, “Modelling and solving profit-oriented U-shaped partial disassembly line balancing problem,” Expert Syst. Appl., vol. 183, no. October 2019, p. 115431, 2021.
  • [4] K. Wang, X. Li, and L. Gao, “Modeling and optimization of multi-objective partial disassembly line balancing problem considering hazard and profit,” J. Clean. Prod., vol. 211, pp. 115–133, 2019.
  • [5] E. B. Edis, “Constraint programming approaches to disassembly line balancing problem with sequencing decisions,” Comput. Oper. Res., vol. 126, p. 105111, 2021.
  • [6] Z. Li, Z. A. Çil, S. Mete, and I. Kucukkoc, “A fast branch, bound and remember algorithm for disassembly line balancing problem,” Int. J. Prod. Res., vol. 58, no. 11, pp. 3220–3234, 2020.
  • [7] E. Goksoy Kalaycilar, S. Batun, and M. Azizoğlu, “A stochastic programming approach for the disassembly line balancing with hazardous task failures,” Int. J. Prod. Res., vol. 60, no. 10, pp. 3237–3262, 2022.
  • [8] S. M. McGovern and S. M. Gupta, “A balancing method and genetic algorithm for disassembly line balancing,” Eur. J. Oper. Res., vol. 179, no. 3, pp. 692–708, Jun. 2007.
  • [9] A. Gungor and S. M. Gupta, “Disassembly line balancing,” in Proceedings of the 1999 annual meeting of the northeast decision sciences institute, 1999, pp. 193–195.
  • [10] A. Güngör and S. M. Gupta, “A solution approach to the disassembly line balancing problem in the presence of task failures,” Int. J. Prod. Res., vol. 39, no. 7, pp. 1427–1467, May 2001.
  • [11] A. Güngör and S. M. Gupta, “Disassembly line in product recovery,” Int. J. Prod. Res., vol. 40, no. 11, pp. 2569–2589, Jul. 2002.
  • [12] S. M. McGovern and S. M. Gupta, “<Title>2-Opt Heuristic for the Disassembly Line Balancing Problem</Title>,” Environ. Conscious Manuf. III, vol. 5262, pp. 71–84, 2004.
  • [13] Y. Ren et al., “Disassembly line balancing problem using interdependent weights-based multi-criteria decision making and 2-Optimal algorithm,” J. Clean. Prod., vol. 174, pp. 1475–1486, 2018.
  • [14] S. Mete, Z. A. Çil, K. Ağpak, E. Özceylan, and A. Dolgui, “A solution approach based on beam search algorithm for disassembly line balancing problem,” J. Manuf. Syst., vol. 41, pp. 188–200, 2016.
  • [15] C. B. Kalayci, O. Polat, and S. M. Gupta, “A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem,” Ann. Oper. Res., vol. 242, no. 2, pp. 321–354, 2016.
  • [16] A. Aydemir-Karadag and O. Turkbey, “Multi-objective optimization of stochastic disassembly line balancing with station paralleling,” Comput. Ind. Eng., vol. 65, no. 3, pp. 413–425, 2013.
  • [17] C. B. Kalayci and S. M. Gupta, “Ant colony optimization for sequence-dependent disassembly line balancing problem,” J. Manuf. Technol. Manag., vol. 24, no. 3, pp. 413–427, 2013.
  • [18] L. P. Ding, Y. X. Feng, J. R. Tan, and Y. C. Gao, “A new multi-objective ant colony algorithm for solving the disassembly line balancing problem,” Int. J. Adv. Manuf. Technol., vol. 48, pp. 761–771, May 2010.
  • [19] C. B. Kalayci and S. M. Gupta, “Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem,” Expert Syst. Appl., vol. 40, no. 18, pp. 7231–7241, 2013.
  • [20] J. Liu and S. Wang, “Balancing disassembly line in product recovery to promote the coordinated development of economy and environment,” Sustain., vol. 9, no. 2, 2017.
  • [21] C. B. Kalayci and S. M. Gupta, “A tabu search algorithm for balancing a sequence-dependent disassembly line,” Prod. Plan. Control, vol. 25, no. 2, pp. 149–160, Jan. 2014.
  • [22] C. B. Kalayci and S. M. Gupta, “A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem,” Int. J. Adv. Manuf. Technol., vol. 69, pp. 197–209, 2013.
  • [23] S. Xiao, Y. Wang, H. Yu, and S. Nie, “An entropy-based adaptive hybrid particle swarm optimization for disassembly line balancing problems,” Entropy, vol. 19, no. 11, p. 596, 2017.
  • [24] Y. Ren, D. Yu, C. Zhang, G. Tian, L. Meng, and X. Zhou, “An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem,” Int. J. Prod. Res., vol. 55, no. 24, pp. 7302–7316, Jun. 2017.
  • [25] Z. Zhang, K. Wang, L. Zhu, and Y. Wang, “A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem,” Expert Syst. Appl., vol. 86, pp. 1339–1351, 2017.
  • [26] L. Zhu, Z. Zhang, and Y. Wang, “A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation,” Int. J. Prod. Res., vol. 56, no. 24, pp. 7354–7374, 2018.
  • [27] F. T. Altekin, L. Kandiller, and N. E. Ozdemirel, “Profit-oriented disassembly-line balancing,” Int. J. Prod. Res., vol. 46, no. 10, pp. 2675–2693, May 2008.
  • [28] F. T. Altekin, “A comparison of piecewise linear programming formulations for stochastic disassembly line balancing,” Int. J. Prod. Res., vol. 55, no. 24, pp. 7412–7434, Jul. 2017.
  • [29] S. Mete, Z. Abidin Çil, E. Özceylan, and K. Ağpak, “Resource constrained disassembly line balancing problem,” IFAC-PapersOnLine, vol. 49, no. 12, pp. 921–925, 2016.
  • [30] M. A. Ilgin, H. Akçay, and C. Araz, “Disassembly line balancing using linear physical programming,” Int. J. Prod. Res., vol. 55, no. 20, pp. 6108–6119, Oct. 2017.
  • [31] T. Paksoy, A. Güngör, E. Özceylan, and A. Hancilar, “Mixed model disassembly line balancing problem with fuzzy goals,” Int. J. Prod. Res., vol. 51, no. 20, pp. 6082–6096, Oct. 2013.
  • [32] E. Özceylan and T. Paksoy, “Reverse supply chain optimisation with disassembly line balancing,” Int. J. Prod. Res., vol. 51, no. 20, pp. 5985–6001, 2013.
  • [33] E. Özceylan, C. B. Kalayci, A. Güngör, and S. M. Gupta, “Disassembly line balancing problem: a review of the state of the art and future directions,” International Journal of Production Research, vol. 57, no. 15–16. Taylor and Francis Ltd., pp. 4805–4827, 2019.
  • [34] Y. Laili, Y. Li, Y. Fang, D. T. Pham, and L. Zhang, “Model review and algorithm comparison on multi-objective disassembly line balancing,” J. Manuf. Syst., vol. 56, no. July, pp. 484–500, 2020.
  • [35] G. Mosheiov, “Scheduling problems with a learning effect,” Eur. J. Oper. Res., vol. 132, no. 3, pp. 687–693, 2001.
  • [36] T. P. Wright, “Factors Affecting the Cost of Engineering,” J. Aeronaut. Sci., vol. 3, no. 4, pp. 122–128, 1936.
  • [37] D. Biskup, “Single-machine scheduling with learning considerations,” Eur. J. Oper. Res., vol. 115, pp. 173–178, 1999.
  • [38] T. C. E. Cheng and G. Wang, “Single machine scheduling with learning effect considerations,” Ann. Oper. Res., vol. 98, pp. 273–290, 2000.
  • [39] M. D. Toksari, E. K. Aydogan, B. Atalay, and S. Sari, “Some scheduling problems with sum of logarithm processing times based learning effect and exponential past sequence dependent delivery times,” J. Ind. Manag. Optim., vol. 18, no. 3, pp. 1795–1807, 2022.
  • [40] T. C. E. Cheng, P. J. Lai, C. C. Wu, and W. C. Lee, “Single-machine scheduling with sum-of-logarithm-processing-times-based learning considerations,” Inf. Sci. (Ny)., vol. 179, no. 18, pp. 3127–3135, 2009.
  • [41] X. X. Liang, B. Zhang, J. B. Wang, N. Yin, and X. Huang, “Study on flow shop scheduling with sum-of-logarithm-processing-times-based learning effects,” J. Appl. Math. Comput., vol. 61, no. 1–2, pp. 373–388, 2019.
  • [42] J. B. Wang and Z. Q. Xia, “Flow-shop scheduling with a learning effect,” J. Oper. Res. Soc., vol. 56, no. 11, pp. 1325–1330, 2005.
  • [43] J. N. D. Gupta and S. K. Gupta, “Single facility scheduling with nonlinear processing times,” Comput. Ind. Eng., vol. 14, no. 4, pp. 387–393, 1988.
  • [44] M. M. Mazdeh, F. Zaerpour, and F. F. Jahantigh, “A fuzzy modeling for single machine scheduling problem with deteriorating jobs,” Int. J. Ind. Eng. Comput., vol. 1, no. 2, pp. 147–156, 2010.
  • [45] O. A. Arık and M. D. Toksarı, “Minimizing makespan with fuzzy processing times under job deterioration and learning effect,” J. Ind. Eng., vol. 31, no. 1, pp. 1–17, 2020.
  • [46] S. M. McGovern and S. M. Gupta, “Ant colony optimization for disassembly sequencing with multiple objectives,” Int. J. Adv. Manuf. Technol., vol. 30, no. 5–6, pp. 481–496, 2006.
  • [47] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in In Proceedings of ICNN’95-international conference on neural networks, 1995, pp. 1942–1948.
  • [48] J. M. Nilakantan and S. G. Ponnambalam, “Robotic U-shaped assembly line balancing using particle swarm optimization,” Eng. Optim., vol. 48, no. 2, pp. 231–252, 2016.
  • [49] H. Garg, “A hybrid PSO-GA algorithm for constrained optimization problems,” Applied Mathematics and Computation, vol. 274. pp. 292–305, 2016.
  • [50] S. M. Gupta, E. Erbis, and S. M. McGovern, “<title>Disassembly sequencing problem: a case study of a cell phone</title>,” Environ. Conscious Manuf. IV, vol. 5583, pp. 43–52, 2004.
  • [51] S. M. McGovern and S. M. Gupta, “Uninformed and probabilistic distributed agent combinatorial searches for the unary np-complete disassembly line balancing problem,” Environ. Conscious Manuf. V, vol. 5997, pp. 81–92, 2005.
  • [52] C. B. Kalayci, S. M. Gupta, and K. Nakashima, “A Simulated Annealing Algorithm for Balancing a Disassembly Line,” Des. Innov. Value Towar. a Sustain. Soc., no. 1, pp. 714–719, 2012.
  • [53] L. Duta, I. Caciula, and P. C. Patic, “Column generation approach for disassembly line balancing,” IFAC-PapersOnLine, vol. 49, no. 12, pp. 916–920, 2016.
  • [54] A. Scholl, “Data of assembly line balancing problems,” Schriften zur quantitativen Betriebswirtschaftslehre, vol. 93, no. 16. pp. 1–28, 1995.
  • [55] G. Mosheiov, “Λ-shaped policies to schedule deteriorating jobs,” Journal of the Operational Research Society, 47(9), pp.11841191,1996.

Logaritmik İşlem Süreleri Toplamı Tabanlı Öğrenme ve İş Bozulması Etkileri Altında Demontaj Hattı Problemi

Yıl 2023, Cilt: 14 Sayı: 4, 683 - 695, 31.12.2023
https://doi.org/10.24012/dumf.1366117

Öz

Artan çevresel farkındalık, ekonomik kaygılar ve yasal kurallar sayesinde, son zamanlarda ürün geri kazanımı veya yeniden üretimi yoğun ilgi görmektedir. Demontaj, ömrünü tamamlamış ürünlerin geri kazanımı aşamasındaki en önemli süreçlerden biridir. Bu yüzden, etkin ve dengeli kurulmuş demontaj hatları önem taşımaktadır. Bu çalışmada, demontaj hattı dengeleme (DHD) problemleri, eş zamanlı logaritmik işlem süreleri toplamı tabanlı öğrenme ve iş bozulması etkileri altında incelenmiştir. DHD literatüründe, logaritmik işlem süreleri toplamı tabanlı öğrenme ve iş bozulması eş zamanlı olarak ilk kez çalışılmıştır. Herhangi bir iş, istasyondaki sırasına göre, kendisinden önceki işlerin işlem sürelerinin logaritmik toplamından etkilenmektedir. İş bozulması ise, işin işleme başlamasını geciktiren bozulmalardır. Öğrenme işlerin işlem sürelerinin azaltırken, bozulma artırmaktadır. Bu çalışmada amaç fonksiyonu, açılan istasyon sayısı minimizasyonudur. Öğrenme ve bozulma etkisi altındaki DHD probleminin çözümü için hibrit (Parçacık Sürüsü Optimizasyonu- Genetik Algoritma) PSO-GA algoritması geliştirilmiş. Farklı öğrenme ve bozulma oranları için sonuçlar elde edilip karşılaştırmalar yapılmıştır. Öğrenme ve bozulma etkisinin, DHD problemlerinde dikkate alındığında amaç fonksiyonu değerinde iyileşmeler görülmüştür.

Kaynakça

  • [1] S. Hezer and Y. Kara, “A network-based shortest route model for parallel disassembly line balancing problem,” Int. J. Prod. Res., vol. 53, no. 6, pp. 1849–1865, Mar. 2015.
  • [2] S. Agrawal and M. K. Tiwari, “A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem,” Int. J. Prod. Res., vol. 46, no. 6, pp. 1405–1429, Mar. 2008.
  • [3] Z. Li and M. N. Janardhanan, “Modelling and solving profit-oriented U-shaped partial disassembly line balancing problem,” Expert Syst. Appl., vol. 183, no. October 2019, p. 115431, 2021.
  • [4] K. Wang, X. Li, and L. Gao, “Modeling and optimization of multi-objective partial disassembly line balancing problem considering hazard and profit,” J. Clean. Prod., vol. 211, pp. 115–133, 2019.
  • [5] E. B. Edis, “Constraint programming approaches to disassembly line balancing problem with sequencing decisions,” Comput. Oper. Res., vol. 126, p. 105111, 2021.
  • [6] Z. Li, Z. A. Çil, S. Mete, and I. Kucukkoc, “A fast branch, bound and remember algorithm for disassembly line balancing problem,” Int. J. Prod. Res., vol. 58, no. 11, pp. 3220–3234, 2020.
  • [7] E. Goksoy Kalaycilar, S. Batun, and M. Azizoğlu, “A stochastic programming approach for the disassembly line balancing with hazardous task failures,” Int. J. Prod. Res., vol. 60, no. 10, pp. 3237–3262, 2022.
  • [8] S. M. McGovern and S. M. Gupta, “A balancing method and genetic algorithm for disassembly line balancing,” Eur. J. Oper. Res., vol. 179, no. 3, pp. 692–708, Jun. 2007.
  • [9] A. Gungor and S. M. Gupta, “Disassembly line balancing,” in Proceedings of the 1999 annual meeting of the northeast decision sciences institute, 1999, pp. 193–195.
  • [10] A. Güngör and S. M. Gupta, “A solution approach to the disassembly line balancing problem in the presence of task failures,” Int. J. Prod. Res., vol. 39, no. 7, pp. 1427–1467, May 2001.
  • [11] A. Güngör and S. M. Gupta, “Disassembly line in product recovery,” Int. J. Prod. Res., vol. 40, no. 11, pp. 2569–2589, Jul. 2002.
  • [12] S. M. McGovern and S. M. Gupta, “<Title>2-Opt Heuristic for the Disassembly Line Balancing Problem</Title>,” Environ. Conscious Manuf. III, vol. 5262, pp. 71–84, 2004.
  • [13] Y. Ren et al., “Disassembly line balancing problem using interdependent weights-based multi-criteria decision making and 2-Optimal algorithm,” J. Clean. Prod., vol. 174, pp. 1475–1486, 2018.
  • [14] S. Mete, Z. A. Çil, K. Ağpak, E. Özceylan, and A. Dolgui, “A solution approach based on beam search algorithm for disassembly line balancing problem,” J. Manuf. Syst., vol. 41, pp. 188–200, 2016.
  • [15] C. B. Kalayci, O. Polat, and S. M. Gupta, “A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem,” Ann. Oper. Res., vol. 242, no. 2, pp. 321–354, 2016.
  • [16] A. Aydemir-Karadag and O. Turkbey, “Multi-objective optimization of stochastic disassembly line balancing with station paralleling,” Comput. Ind. Eng., vol. 65, no. 3, pp. 413–425, 2013.
  • [17] C. B. Kalayci and S. M. Gupta, “Ant colony optimization for sequence-dependent disassembly line balancing problem,” J. Manuf. Technol. Manag., vol. 24, no. 3, pp. 413–427, 2013.
  • [18] L. P. Ding, Y. X. Feng, J. R. Tan, and Y. C. Gao, “A new multi-objective ant colony algorithm for solving the disassembly line balancing problem,” Int. J. Adv. Manuf. Technol., vol. 48, pp. 761–771, May 2010.
  • [19] C. B. Kalayci and S. M. Gupta, “Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem,” Expert Syst. Appl., vol. 40, no. 18, pp. 7231–7241, 2013.
  • [20] J. Liu and S. Wang, “Balancing disassembly line in product recovery to promote the coordinated development of economy and environment,” Sustain., vol. 9, no. 2, 2017.
  • [21] C. B. Kalayci and S. M. Gupta, “A tabu search algorithm for balancing a sequence-dependent disassembly line,” Prod. Plan. Control, vol. 25, no. 2, pp. 149–160, Jan. 2014.
  • [22] C. B. Kalayci and S. M. Gupta, “A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem,” Int. J. Adv. Manuf. Technol., vol. 69, pp. 197–209, 2013.
  • [23] S. Xiao, Y. Wang, H. Yu, and S. Nie, “An entropy-based adaptive hybrid particle swarm optimization for disassembly line balancing problems,” Entropy, vol. 19, no. 11, p. 596, 2017.
  • [24] Y. Ren, D. Yu, C. Zhang, G. Tian, L. Meng, and X. Zhou, “An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem,” Int. J. Prod. Res., vol. 55, no. 24, pp. 7302–7316, Jun. 2017.
  • [25] Z. Zhang, K. Wang, L. Zhu, and Y. Wang, “A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem,” Expert Syst. Appl., vol. 86, pp. 1339–1351, 2017.
  • [26] L. Zhu, Z. Zhang, and Y. Wang, “A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation,” Int. J. Prod. Res., vol. 56, no. 24, pp. 7354–7374, 2018.
  • [27] F. T. Altekin, L. Kandiller, and N. E. Ozdemirel, “Profit-oriented disassembly-line balancing,” Int. J. Prod. Res., vol. 46, no. 10, pp. 2675–2693, May 2008.
  • [28] F. T. Altekin, “A comparison of piecewise linear programming formulations for stochastic disassembly line balancing,” Int. J. Prod. Res., vol. 55, no. 24, pp. 7412–7434, Jul. 2017.
  • [29] S. Mete, Z. Abidin Çil, E. Özceylan, and K. Ağpak, “Resource constrained disassembly line balancing problem,” IFAC-PapersOnLine, vol. 49, no. 12, pp. 921–925, 2016.
  • [30] M. A. Ilgin, H. Akçay, and C. Araz, “Disassembly line balancing using linear physical programming,” Int. J. Prod. Res., vol. 55, no. 20, pp. 6108–6119, Oct. 2017.
  • [31] T. Paksoy, A. Güngör, E. Özceylan, and A. Hancilar, “Mixed model disassembly line balancing problem with fuzzy goals,” Int. J. Prod. Res., vol. 51, no. 20, pp. 6082–6096, Oct. 2013.
  • [32] E. Özceylan and T. Paksoy, “Reverse supply chain optimisation with disassembly line balancing,” Int. J. Prod. Res., vol. 51, no. 20, pp. 5985–6001, 2013.
  • [33] E. Özceylan, C. B. Kalayci, A. Güngör, and S. M. Gupta, “Disassembly line balancing problem: a review of the state of the art and future directions,” International Journal of Production Research, vol. 57, no. 15–16. Taylor and Francis Ltd., pp. 4805–4827, 2019.
  • [34] Y. Laili, Y. Li, Y. Fang, D. T. Pham, and L. Zhang, “Model review and algorithm comparison on multi-objective disassembly line balancing,” J. Manuf. Syst., vol. 56, no. July, pp. 484–500, 2020.
  • [35] G. Mosheiov, “Scheduling problems with a learning effect,” Eur. J. Oper. Res., vol. 132, no. 3, pp. 687–693, 2001.
  • [36] T. P. Wright, “Factors Affecting the Cost of Engineering,” J. Aeronaut. Sci., vol. 3, no. 4, pp. 122–128, 1936.
  • [37] D. Biskup, “Single-machine scheduling with learning considerations,” Eur. J. Oper. Res., vol. 115, pp. 173–178, 1999.
  • [38] T. C. E. Cheng and G. Wang, “Single machine scheduling with learning effect considerations,” Ann. Oper. Res., vol. 98, pp. 273–290, 2000.
  • [39] M. D. Toksari, E. K. Aydogan, B. Atalay, and S. Sari, “Some scheduling problems with sum of logarithm processing times based learning effect and exponential past sequence dependent delivery times,” J. Ind. Manag. Optim., vol. 18, no. 3, pp. 1795–1807, 2022.
  • [40] T. C. E. Cheng, P. J. Lai, C. C. Wu, and W. C. Lee, “Single-machine scheduling with sum-of-logarithm-processing-times-based learning considerations,” Inf. Sci. (Ny)., vol. 179, no. 18, pp. 3127–3135, 2009.
  • [41] X. X. Liang, B. Zhang, J. B. Wang, N. Yin, and X. Huang, “Study on flow shop scheduling with sum-of-logarithm-processing-times-based learning effects,” J. Appl. Math. Comput., vol. 61, no. 1–2, pp. 373–388, 2019.
  • [42] J. B. Wang and Z. Q. Xia, “Flow-shop scheduling with a learning effect,” J. Oper. Res. Soc., vol. 56, no. 11, pp. 1325–1330, 2005.
  • [43] J. N. D. Gupta and S. K. Gupta, “Single facility scheduling with nonlinear processing times,” Comput. Ind. Eng., vol. 14, no. 4, pp. 387–393, 1988.
  • [44] M. M. Mazdeh, F. Zaerpour, and F. F. Jahantigh, “A fuzzy modeling for single machine scheduling problem with deteriorating jobs,” Int. J. Ind. Eng. Comput., vol. 1, no. 2, pp. 147–156, 2010.
  • [45] O. A. Arık and M. D. Toksarı, “Minimizing makespan with fuzzy processing times under job deterioration and learning effect,” J. Ind. Eng., vol. 31, no. 1, pp. 1–17, 2020.
  • [46] S. M. McGovern and S. M. Gupta, “Ant colony optimization for disassembly sequencing with multiple objectives,” Int. J. Adv. Manuf. Technol., vol. 30, no. 5–6, pp. 481–496, 2006.
  • [47] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in In Proceedings of ICNN’95-international conference on neural networks, 1995, pp. 1942–1948.
  • [48] J. M. Nilakantan and S. G. Ponnambalam, “Robotic U-shaped assembly line balancing using particle swarm optimization,” Eng. Optim., vol. 48, no. 2, pp. 231–252, 2016.
  • [49] H. Garg, “A hybrid PSO-GA algorithm for constrained optimization problems,” Applied Mathematics and Computation, vol. 274. pp. 292–305, 2016.
  • [50] S. M. Gupta, E. Erbis, and S. M. McGovern, “<title>Disassembly sequencing problem: a case study of a cell phone</title>,” Environ. Conscious Manuf. IV, vol. 5583, pp. 43–52, 2004.
  • [51] S. M. McGovern and S. M. Gupta, “Uninformed and probabilistic distributed agent combinatorial searches for the unary np-complete disassembly line balancing problem,” Environ. Conscious Manuf. V, vol. 5997, pp. 81–92, 2005.
  • [52] C. B. Kalayci, S. M. Gupta, and K. Nakashima, “A Simulated Annealing Algorithm for Balancing a Disassembly Line,” Des. Innov. Value Towar. a Sustain. Soc., no. 1, pp. 714–719, 2012.
  • [53] L. Duta, I. Caciula, and P. C. Patic, “Column generation approach for disassembly line balancing,” IFAC-PapersOnLine, vol. 49, no. 12, pp. 916–920, 2016.
  • [54] A. Scholl, “Data of assembly line balancing problems,” Schriften zur quantitativen Betriebswirtschaftslehre, vol. 93, no. 16. pp. 1–28, 1995.
  • [55] G. Mosheiov, “Λ-shaped policies to schedule deteriorating jobs,” Journal of the Operational Research Society, 47(9), pp.11841191,1996.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Makine Mühendisliğinde Optimizasyon Teknikleri
Bölüm Makaleler
Yazarlar

Halime Somtürk 0000-0001-7329-495X

M. Duran Toksarı 0000-0001-9577-1956

Erken Görünüm Tarihi 31 Aralık 2023
Yayımlanma Tarihi 31 Aralık 2023
Gönderilme Tarihi 25 Eylül 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 14 Sayı: 4

Kaynak Göster

IEEE H. Somtürk ve M. D. Toksarı, “Logaritmik İşlem Süreleri Toplamı Tabanlı Öğrenme ve İş Bozulması Etkileri Altında Demontaj Hattı Problemi”, DÜMF MD, c. 14, sy. 4, ss. 683–695, 2023, doi: 10.24012/dumf.1366117.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456