Kesin ve bulanık hedeflerle tehlikeli parçaları dikkate alan demontaj hatlarının dengelenmesi

Yıl 2022, , 92 - 108, 14.01.2022

Seda Hezer , Yakup Kara

https://doi.org/10.28948/ngumuh.975730

Öz

Demontaj, geri kazanım faaliyetlerinin adımlarından biridir. Pahalı süreçler içermesi nedeniyle, demontajın etkin ve verimli çıktılar üreten sistemlerde gerçekleştirilmesi gerekmektedir. Bir demontaj hattı, ürünlerin demontajı için en uygun sistemdir. Demontaj hattı dengeleme problemi (DHDP) belirli kısıtların sağlanması koşuluyla bir ya da daha fazla hedefe ulaşmak için görevlerin ardışık olarak sıralanmış istasyonlara atanmasıdır. Bu çalışmada negatif bölge kısıtına göre, birbirleriyle çelişen hedeflerin optimize edilmesine odaklanan DHDP (DHDP-Z) önerilmiştir. Negatif bölge kısıtı tehlikeli parçalarla ilgilidir. Eğer bir üründe çıkarılması gereken tehlikeli parça/parçalar varsa, bu parçaların diğer parçalara ve sisteme zarar vermemesi amacıyla farklı bir istasyonda çıkarılmaları gerekmektedir. Birbirleriyle çelişen hedefler toplam net gerikazanım karı, geri dönüştürülecek parçaların sayısı ve çevrim zamanıdır. İlgili hedeflerin en iyilenmesi için hedef programlama (HP) ve bulanık hedef programlama (BHP) yaklaşımları önerilmiştir. Küçük boyutlu bir örnekle, yaklaşımların geçerli ve faydalı olduğu gösterilmiştir. DHDP literatürü gözlemlendiğinde, DHDP-Z’nin çözümü için çok kriterli karar verme (ÇKKV) yaklaşımlarının uygulanmadığı gözlemlenmiştir.

Anahtar Kelimeler

Bulanık hedefler, Demontaj, Demontaj hatları, Hat dengeleme, Hedef programlama

Balancing disassembly line under hazardous parts with precise and fuzzy goals

Öz

Disassembly is one of the steps of the recovery activities. Since it includes expensive processes, disassembly shoul be performed with the system that provides efficient and effective outputs. A disassembly line is the most suitable system for disassembly of the returned products. A disassembly line balancing problem (DLBP) is assigning disassembly tasks to consecutive workstations by satisfying a series of constraints and optimizing one or more than one goal. In this paper, the DLBP with multiple conflicting goals which takes into account the negative zone (DLBP-Z) constraints has been proposed. Negative zone constraint is related to hazardous parts. If there are hazardous part/parts in the product and they need to be removed, they may damage the othetr parts and disassembly line. Therefore, these parts must be assigned to different stations from the other parts. Goal programming (GP) and fuzzy goal programming (FGP) approaches have been proposed in order to optimize three conflicting goals, namely total net recovery profit value, the number of parts to be removed for recycling and cycle time. Through a numerical example, the proposed approaches have been tested and goal programming formulations have been shown to be valid and useful. To the best of the authors knowledge, the proposed GP and FGP models are the first multi citeria decision making (MCDM) approaches for DLBP-Z.

Anahtar Kelimeler

Disassembly, Disassembly lines, Fuzzy goals, Goal programming, Line balancing

Kaynakça

• M. Thierry, M. Salomon, J. Van Nunen, and L. Van Wassenhove, Strategic Issues in Product Recovery Management: 37 (2), 114–135, 1995. https://doi.org/ 10.2307/ 41165792.
• A. Gungor, and S.M. Gupta, Issues in environmentally conscious manufacturing and product recovery: a survey. Computers & Industrial Engineering, 36 (4), 811–853, 1999. https://doi.org/10.1016/S0360-8352(99)00167-9.
• M.A. Ilgin, and S.M. Gupta, Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. Journal of Environmental Management, 91 (3), 563–591, 2010.https://doi.org/10.1016/J.JENVMAN.2009.09.037.
• S.M. GUPTA, and K.N. TALEB, Scheduling disassembly.32 (8), 1857–1866, 2007. https://doi.org/ 10.1080/00207549408957046.
• A. Koc, I. Sabuncuoglu, and E. Erel, Two exact formulations for disassembly line balancing problems with task precedence diagram construction using an AND/OR graph. IIE Transactions (Institute of Industrial Engineers), 41 (10), 866–881, 2009. https://doi.org/10.1080/07408170802510390.
• A. Güngör, and S.M. Gupta, Disassembly line in product recovery. International Journal of Production Research, 40 (11), 2569–2589, 2002. https://doi.org/ 10.1080/00207540210135622.
• Ö. Tozanlı, E. Kongar, and S.M. Gupta, Trade-in-to-upgrade as a marketing strategy in disassembly-to-order systems at the edge of blockchain technology. https://doi.org/10.1080/00207543.2020.1712489, 58 (23), 7183–7200, 2020. https://doi.org/10.1080/ 00207543.2020.1712489.
• S. Agrawal, and M.K. Tiwari, A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem. International Journal of Production Research, 46 (6), 1405–1429, 2008. https://doi.org/10.1080/ 00207540600943985.
• E. Kongar, and S.M. Gupta, Disassembly to order system under uncertainty. Omega, 34 (6), 550–561, 2006. https://doi.org/10.1016/J.OMEGA. 2005.01.006.
• K. Igarashi, T. Yamada, and M. Inoue, 2-Stage Optimal Design and Analysis for Disassembly System With Environmental and Economic Parts Selection Using the Recyclability Evaluation Method. Industrial Engineering and Management Systems, 13 (1), 52–66, 2014. https://doi.org/ 10.7232/iems.2014.13.1.052.
• A. Güngör, and S.M. Gupta, A solution approach to the disassembly line balancing problem in the presence of task failures. International Journal of Production Research, 39 (7), 1427–1467, 2001. https://doi.org/10.1080/00207540110052157.
• S.M. McGovern, and S.M. Gupta, Combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem. International Journal of Production Research, 45 (18–19), 4485–4511,2007. https://doi.org/10.1080/00207540701476 281.
• S.M. McGovern, and S.M. Gupta, A balancing method and genetic algorithm for disassembly line balancing. European Journal of Operational Research, 179 (3), 692–708, 2007. https://doi.org/10.1016/j.ejor.2005. 03.055.
• A. Aydemir-Karadag, and O. Turkbey, Multi-objective optimization of stochastic disassembly line balancing with station paralleling. Computers and Industrial Engineering, 65 (3), 413–425, 2013. https://doi.org/10.1016/j.cie.2013.03.014.
• S. Hezer, and Y. Kara, A network-based shortest route model for parallel disassembly line balancing problem. International Journal of Production Research, 53 (6), 1849–1865, 2015. https://doi.org/ 10.1080/00207543.2014.965348.
• Z. Li, I. Kucukkoc, and Z. Zhang, Iterated local search method and mathematical model for sequence-dependent U-shaped disassembly line balancing problem. Computers and Industrial Engineering, 137 (September), 106056, 2019. https://doi.org/ 10.1016/j.cie.2019.106056.
• S. Avikal, R. Jain, and P. Mishra, A heuristic for U-shaped disassembly line balancing problems. MIT International Journal of Mechanical Engineering, 3 (1), 51–56, 2013.
• K. Wang, L. Gao, and X. Li, A multi-objective algorithm for U-shaped disassembly line balancing with partial destructive mode. Neural Computing and Applications, 32 (16), 12715–12736, 2020. https://doi.org/10.1007/s00521-020-04721-0.
• F.T. Altekin, L. Kandiller, and N.E. Ozdemirel, Profit-oriented disassembly-line balancing. International Journal of Production Research, 46 (10), 2675–2693, 2008. https://doi.org/10.1080/ 00207540601137207.
• M.L. Bentaha, A. Dolgui, O. Battaïa, R.J. Riggs, and J. Hu, Profit-oriented partial disassembly line design: dealing with hazardous parts and task processing times uncertainty. International Journal of Production Research, 56 (24), 7220–7242, 2018. https://doi.org/10.1080/00207543.2017.1418987.
• 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. International Journal of Production Research, 55 (24), 7302–7316, 2017. https://doi.org/10.1080/00207543.2017.1341066.
• S. Parsa, and M. Saadat, Intelligent selective disassembly planning based on disassemblability characteristics of product components. International Journal of Advanced Manufacturing Technology, 104 (5–8), 1769–1783, 2019. https://doi.org/ 10.1007/ s00170-019-03857-1.
• K. Wang, X. Li, L. Gao, and A. Garg, Partial disassembly line balancing for energy consumption and profit under uncertainty. Robotics and Computer-Integrated Manufacturing, 59 (May), 235–251, 2019. https://doi.org/10.1016/ j.rcim.2019.04.014.
• Q. Xiao, X. Guo, and D. Li, Partial disassembly line balancing under uncertainty: robust optimisation models and an improved migrating birds optimisation algorithm. International Journal of Production Research, 59 (10), 2977–2995, 2021. https://doi.org/10.1080/00207543.2020.1744765.
• K. Wang, X. Li, and L. Gao, Modeling and optimization of multi-objective partial disassembly line balancing problem considering hazard and profit. Journal of Cleaner Production, 211, 115–133, 2019. https://doi.org/10.1016/j.jclepro.2018.11.114.
• S.M. McGovern, and S.M. Gupta, Local search heuristics and greedy algorithm for balancing a disassembly line. International Journal of Operations and Quantitative Management, 11 (2), 91–114, 2005.
• M.L. Bentaha, O. Battaiä, and A. Dolgui, An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times. International Journal of Production Research, 53 (6), 1807–1818, 2015. https://doi.org/10.1080/ 00207543.2014.961212.
• C.B. Kalayci, and S.M. Gupta, A tabu search algorithm for balancing a sequence-dependent disassembly line. Production Planning and Control, 25 (2), 149–160, 2014. https://doi.org/10.1080/ 09537287.2013.782949.
• F.T. Altekin, and C. Akkan, Task-failure-driven rebalancing of disassembly lines. International Journal of Production Research, 50 (18), 4955–4976, 2012. https://doi.org/10.1080/00207543.2011.616915.
• E.G. Kalaycilar, M. Azizoʇlu, and S. Yeralan, A disassembly line balancing problem with fixed number of workstations. European Journal of Operational Research, 249 (2), 592–604, 2016. https://doi.org/10.1016/j.ejor.2015.09.004.
• Y. Fang, Q. Liu, M. Li, Y. Laili, and D.T. Pham, Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations. European Journal of Operational Research, 276 (1), 160–174, 2019. https://doi.org/ 10.1016/j.ejor.2018.12.035.
• 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 Systems with Applications, 86 1339–1351, 2017. https://doi.org/10.1016/ j.eswa.2017.05.053.
• L. Zhu, Z. Zhang, and Y. Wang, A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation. International Journal of Production Research, 56 (24), 7354–7374, 2018. https://doi.org/10.1080/ 00207543.2018.1471238.
• S. Wang, X. Guo, and J. Liu, An efficient hybrid artificial bee colony algorithm for disassembly line balancing problem with sequence-dependent part removal times. Engineering Optimization, 51 (11), 1920–1937, 2019. https://doi.org/10.1080/ 0305215X.2018.1564918.
• Y. Fang, H. Ming, M. Li, Q. Liu, and D.T. Pham, Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time. International Journal of Production Research, 58 (3), 846–862, 2020. https://doi.org/10.1080/ 00207543.2019.1602290.
• G. Bin Qin, X.W. Guo, M.C. Zhou, S.X. Liu, and L. Qi, Multi-Objective Discrete Migratory Bird Optimizer for Stochastic Disassembly Line Balancing Problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020-Octob 420–425, 2020. https://doi.org/10.1109/SMC42975. 2020.9283371.
• S.M. McGovern, and S.M. Gupta, Ant colony optimization for disassembly sequencing with multiple objectives. International Journal of Advanced Manufacturing Technology, 30 (5–6), 481–496, 2006. https://doi.org/10.1007/s00170-005-0037-6.
• 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. International Journal of Advanced Manufacturing Technology, 48 (5–8), 761–771, 2010. https://doi.org/10.1007/ s00170-009-2303-5.
• C.B. Kalayci, and S.M. Gupta, Ant colony optimization for sequence-dependent disassembly line balancing problem. Journal of Manufacturing Technology Management, 24 (3), 413–427, 2013. https://doi.org/10.1108/17410381311318909.
• C.B. Kalayci, and S.M. Gupta, A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem. International Journal of Advanced Manufacturing Technology, 69 (1–4), 197–209, 2013. https://doi.org/10.1007/s00170-013-4990-1.
• C.B. Kalayci, A. Hancilar, A. Gungor, and S.M. Gupta, Multi-objective fuzzy disassembly line balancing using a hybrid discrete artificial bee colony algorithm. Journal of Manufacturing Systems, 37 672–682, 2015. https://doi.org/10.1016/ j.jmsy.2014.11.015.
• M. Seidi, and S. Saghari, The balancing of disassembly line of automobile engine using Genetic Algorithm (GA) in fuzzy environment. Industrial Engineering and Management Systems, 15 (4), 364–373, 2016. https://doi.org/10.7232/ iems.2016.15.4.364.
• S. Xiao, Y. Wang, H. Yu, and S. Nie, An entropy-based adaptive hybrid particle swarm optimization for disassembly line balancing problems. Entropy, 19 (11), , 2017. https://doi.org/10.3390/e19110596.
• M.L. Bentaha, O. Battaïa, and A. Dolgui, A sample average approximation method for disassembly line balancing problem under uncertainty. Computers and Operations Research, 51 111–122, 2014. https://doi.org/10.1016/j.cor.2014.05.006.
• J. He, F. Chu, F. Zheng, M. Liu, and C. Chu, A multi-objective distribution-free model and method for stochastic disassembly line balancing problem. International Journal of Production Research, 58 (18), 5721–5737, 2020. https://doi.org/10.1080/ 00207543.2019.1656841.
• J. He, F. Chu, F. Zheng, and M. Liu, A green-oriented bi-objective disassembly line balancing problem with stochastic task processing times. Annals of Operations Research, 296 (1–2), 71–93, 2021. https://doi.org/ 10.1007/s10479-020-03558-z.
• K. Wang, X. Li, and L. Gao, A multi-objective discrete flower pollination algorithm for stochastic two-sided partial disassembly line balancing problem. Computers and Industrial Engineering, 130 (September 2018), 634–649, 2019. https://doi.org/ 10.1016/j.cie.2019.03.017.
• F.T. Altekin, Z.P. Bayındır, and V. Gümüşkaya, Remedial actions for disassembly lines with stochastic task times. Computers and Industrial Engineering, 99 78–96, 2016. https://doi.org/ 10.1016/j.cie.2016.06.027.
• F.T. Altekin, A comparison of piecewise linear programming formulations for stochastic disassembly line balancing. International Journal of Production Research, 55 (24), 7412–7434, 2017. https://doi.org/ 10.1080/00207543.2017.1351639.
• F. Zheng, J. He, F. Chu, and M. Liu, A new distribution-free model for disassembly line balancing problem with stochastic task processing times. International Journal of Production Research, 56 (24), 7341–7353, 2018. https://doi.org/10.1080/ 00207543.2018.1430909.
• K. Liu, and Z.H. Zhang, Capacitated disassembly scheduling under stochastic yield and demand. European Journal of Operational Research, 269 (1), 244–257, 2018. https://doi.org/10.1016/ j.ejor.2017.08.032.
• S. Avikal, P.K. Mishra, and R. Jain, A Fuzzy AHP and PROMETHEE method-based heuristic for disassembly line balancing problems. International Journal of Production Research, 52 (5), 1306–1317, 2014. https://doi.org/10.1080/00207543. 2013.831999.
• Y. Yang, G. Yuan, Q. Zhuang, and G. Tian, Multi-objective low-carbon disassembly line balancing for agricultural machinery using MDFOA and fuzzy AHP. Journal of Cleaner Production, 233 1465–1474, 2019. https://doi.org/10.1016/j.jclepro. 2019.06.035.
• T. Paksoy, A. Güngör, E. Özceylan, and A. Hancilar, Mixed model disassembly line balancing problem with fuzzy goals. International Journal of Production Research, 51 (20), 6082–6096, 2013. https://doi.org/10.1080/00207543.2013.795251.
• S. Avikal, R. Jain, and P.K. Mishra, A Kano model, AHP and M-TOPSIS method-based technique for disassembly line balancing under fuzzy environment. Applied Soft Computing Journal, 25 519–529, 2014. https://doi.org/10.1016/j.asoc. 2014.08.002.
• J. Liu, Z. Zhou, D.T. Pham, W. Xu, J. Yan, A. Liu, C. Ji, and Q. Liu, An improved multi-objective discrete bees algorithm for robotic disassembly line balancing problem in remanufacturing. International Journal of Advanced Manufacturing Technology, 97 (9–12), 3937–3962, 2018. https://doi.org/10.1007/ s00170-018-2183-7.
• Z.A. Çil, S. Mete, and F. Serin, Robotic disassembly line balancing problem: A mathematical model and ant colony optimization approach. Applied Mathematical Modelling, 86 335–348, 2020. https://doi.org/ 10.1016/j.apm.2020.05.006.
• Y. Laili, F. Tao, D.T. Pham, Y. Wang, and L. Zhang, Robotic disassembly re-planning using a two-pointer detection strategy and a super-fast bees algorithm. Robotics and Computer-Integrated Manufacturing, 59 (December 2018), 130–142, 2019. https://doi.org/ 10.1016/j.rcim.2019.04.003.
• J. Liu, Z. Zhou, D.T. Pham, W. Xu, C. Ji, and Q. Liu, Collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem using improved discrete Bees algorithm in remanufacturing. Robotics and Computer-Integrated Manufacturing, 61 (February 2018), 101829, 2020. https://doi.org/10.1016/ j.rcim.2019.101829.
• L. Zhang, X. Zhao, Q. Ke, W. Dong, and Y. Zhong, Disassembly Line Balancing Optimization Method for High Efficiency and Low Carbon Emission. International Journal of Precision Engineering and Manufacturing - Green Technology, 8 (1), 233–247, 2021. https://doi.org/10.1007/s40684-019-00140-2.
• K. Wang, X. Li, L. Gao, and P. Li, Energy consumption and profit-oriented disassembly line balancing for waste electrical and electronic equipment. Journal of Cleaner Production, 265 121829, 2020. https://doi.org/10.1016/ j.jclepro.2020.121829.
• K. Wang, X. Li, L. Gao, and P. Li, Modeling and Balancing for Green Disassembly Line Using Associated Parts Precedence Graph and Multi-objective Genetic Simulated Annealing. International Journal of Precision Engineering and Manufacturing - Green Technology, (0123456789), , 2020. https://doi.org/10.1007/s40684-020-00259-7.
• A. Budak, Sustainable reverse logistics optimization with triple bottom line approach: An integration of disassembly line balancing. Journal of Cleaner Production, 270 , 2020. https://doi.org/ 10.1016/j.jclepro.2020.122475.
• Y. Kazancoglu, and Y.D. Ozkan-Ozen, Sustainable disassembly line balancing model based on triple bottom line. International Journal of Production Research, 58 (14), 4246–4266, 2020. https://doi.org/ 10.1080/00207543.2019.1651456.
• Y. Gao, Q. Wang, Y. Feng, H. Zheng, B. Zheng, and J. Tan, An energy-saving optimization method of dynamic scheduling for disassembly line. Energies, 11 (5), , 2018. https://doi.org/10.3390/en11051261.
• S. Smith, and P.Y. Hung, A novel selective parallel disassembly planning method for green design. Journal of Engineering Design, 26 (10–12), 283–301, 2015. https://doi.org/10.1080/09544828. 2015.1045841.
• Y.K. Hao, and S. Hasan, The improvement of line efficiency on disassembly line balancing problem: An HRRCD’s heuristic rule. ARPN Journal of Engineering and Applied Sciences, 11 (10), 6428–6433, 2016.
• F. Pistolesi, B. Lazzerini, M.D. Mura, and G. Dini, EMOGA: A Hybrid Genetic Algorithm with Extremal Optimization Core for Multiobjective Disassembly Line Balancing. IEEE Transactions on Industrial Informatics, 14 (3), 1089–1098, 2018. https://doi.org/10.1109/TII.2017.2778223.
• J. Li, X. Chen, Z. Zhu, C. Yang, and C. Chu, A branch, bound, and remember algorithm for the simple disassembly line balancing problem. Computers and Operations Research, 105 47–57, 2019. https://doi.org/10.1016/j.cor.2019.01.003.
• M. Colledani, and O. Battaïa, A decision support system to manage the quality of End-of-Life products in disassembly systems. CIRP Annals - Manufacturing Technology, 65 (1), 41–44, 2016. https://doi.org/10.1016/j.cirp.2016.04.121.
• J. Cao, X. Xia, L. Wang, Z. Zhang, and X. Liu, A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem. Sustainability (Switzerland), 11 (24), , 2019. https://doi.org/ 10.3390/su11246969.
• E.L. Hannan, Linear programming with multiple fuzzy goals. Fuzzy Sets and Systems, 6 (3), 235–248, 1981. https://doi.org/10.1016/0165-0114(81)90002-6.
• Y. Laili, Y. Li, Y. Fang, D.T. Pham, and L. Zhang, Model review and algorithm comparison on multi-objective disassembly line balancing. Journal of Manufacturing Systems, 56 (December 2019), 484–500, 2020. https://doi.org/10.1016/ j.jmsy.2020.07.015.
• J.P. Ignizio, Linear programming in single- & multiple-objective systemsPrentice-Hall, 1982.
• Y. Ren, C. Zhang, F. Zhao, G. Tian, W. Lin, L. Meng, and H. Li, Disassembly line balancing problem using interdependent weights-based multi-criteria decision making and 2-Optimal algorithm. Journal of Cleaner Production, 174 1475–1486, 2018. https://doi.org/10.1016/j.jclepro.2017.10.308.
• L. Li, Z. Zhang, L. Zhu, and B. Zou, Modeling and Optimizing for Multi-objective Partial Disassembly Line Balancing Problem. Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 54 (3), 125–136, 2018. https://doi.org/10.3901/ JME.2018.03.125.
• G. Yuan, Y. Yang, and D.T. Pham, Multiobjective Ecological Strategy Optimization for Two-Stage Disassembly Line Balancing with Constrained-Resource. IEEE Access, 8 88745–88758, 2020. https://doi.org/10.1109/ACCESS.2020.2994065.
• Abraham Charnes, and William W. Cooper, Management models and industrial applications of linear programming, Volume I, by Abraham Charnes and William W. Cooper. John Wiley and Sons, New York, 467 pp. 1961.
• Y. Kara, H. Gökçen, and Y. Atasagun, Balancing parallel assembly lines with precise and fuzzy goals. International Journal of Production Research, 48 (6), 1685–1703, 2010. https://doi.org/10.1080/ 00207540802534715.
• H.J. Zimmermann, Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1 (1), 45–55, 1978. https://doi.org/10.1016/0165-0114(78)90031-3.
• R. Narasimhan, goal programmıng ın a fuzzy envıronment. Decision Sciences, 11 (2), 325–336, 1980. https://doi.org/10.1111/J.1540-5915.1980 .TB01142.X.
• 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, 57 (15–16), 4805–4827,2019. https://doi.org/10.1080/ 00207543.2018.1428775.
• N. Deniz, and F. Ozcelik, An extended review on disassembly line balancing with bibliometric & social network and future study realization analysis. Journal of Cleaner Production, 225 697–715, 2019. https://doi.org/10.1016/j.jclepro.2019.03.188.
• J. Liang, S. Guo, B. Du, Y. Li, J. Guo, Z. Yang, and S. Pang, Minimizing energy consumption in multi-objective two-sided disassembly line balancing problem with complex execution constraints using dual-individual simulated annealing algorithm. Journal of Cleaner Production, 284 125418, 2021. https://doi.org/10.1016/j.jclepro.2020.125418.
• S.K. Das, P. Yedlarajiah, and R. Narendra, An approach for estimating the end-of-life product disassembly effort and cost. International Journal of Production Research, 38 (3), 657–673, 2000. https://doi.org/10.1080/002075400189356.
• I. Kucukkoc, Z. Li, and Y. Li, Type-E disassembly line balancing problem with multi-manned workstations. Optimization and Engineering, 21 (2), 611–630, 2020. https://doi.org/10.1007/s11081-019-09465-y.
• E.B. Edis, M.A. Ilgin, and R.S. Edis, Disassembly line balancing with sequencing decisions: A mixed integer linear programming model and extensions. Journal of Cleaner Production, 238 117826, 2019. https://doi.org/10.1016/j.jclepro.2019.117826.
• M.L. Bentaha, O. Battaïa, A. Dolgui, and S.J. Hu, Second order conic approximation for disassembly line design with joint probabilistic constraints. European Journal of Operational Research, 247 (3), 957–967, 2015. https://doi.org/10.1016/j.ejor.2015. 06.019.
• S.K. Das, and S. Naik, Process planning for product disassembly. International Journal of Production Research, 40 (6), 1335–1355, 2002. https://doi.org/10.1080/00207540110102142.
• U. Özcan, and B. Toklu, Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models. Computers & Operations Research, 36 (6), 1955–1965, 2009. https://doi.org/10.1016/J.COR. 2008.06.009.
• R.N. Tiwari, S. Dharmar, and J.R. Rao, Fuzzy goal programming - An additive model. Fuzzy Sets and Systems, 24 (1), 27–34, 1987. https://doi.org/ 10.1016/0165-0114(87)90111-4.
• T. Yang, J.P. Ignizio, and H.J. Kim, Fuzzy programming with nonlinear membership functions: Piecewise linear approximation. Fuzzy Sets and Systems, 41 (1), 39–53, 1991. https://doi.org/ 10.1016/0165-0114(91)90156-K.
• S.S. Rao, K. Sundararaju, B.G. Prakash, and C. Balakrishna, Fuzzy goal programming approach for structural optimization. https://doi.org/10.2514/ 3.11079, 30 (5), 1425–1432, 2012. https://doi.org/ 10.2514/3.11079.
• J.R. Rao, R.N. Tiwari, and B.K. Mohanty, A preference structure on aspiration levels in a goal programming problem — A fuzzy approach. Fuzzy Sets and Systems, 25 (2), 175–182, 1988. https://doi.org/10.1016/0165-0114(88)90185-6.
• R.N. Tiwari, S. Dharmar, and J.R. Rao, Priority structure in fuzzy goal programming. Fuzzy Sets and Systems, 19 (3), 251–259, 1986. https://doi.org/ 10.1016/0165-0114(86)90054-0.
• C. Ter Chang, Binary fuzzy goal programming. European Journal of Operational Research, 180 (1), 29–37, 2007. https://doi.org/10.1016/J.EJOR. 2006.03.030.