Muhammet Enes AKPINAR, Mehmet Ali ILGIN, Hüseyin AKTAŞ

Disassembly Line Balancing by Using Simulation Optimization

Öz Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.

Anahtar Kelimeler:

Line Balancing, Disassembly, Genetic Algorithms, Simulated Annealing, Simulation

PDF

___

Agrawal, S., & Tiwari, M. K. (2008). A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem. International Journal of Production Research. https://doi.org/10.1080/00207540600943985

Altekin, F. T. (2017). A comparison of piecewise linear programming formulations for stochastic disassembly line balancing. International Journal of Production Research. https://doi.org/10.1080/00207543.2017.1351639

Altekin, F. T., & Akkan, C. (2012). Task-failure-driven rebalancing of disassembly lines. International Journal of Production Research. https://doi.org/10.1080/00207543.2011.616915

Altekin, F. T., Bayındır, Z. P., & Gümüşkaya, V. (2016). Remedial actions for disassembly lines with stochastic task times. Computers and Industrial Engineering. https://doi.org/10.1016/j.cie.2016.06.027

Altekin, F. T., Kandiller, L., & Ozdemirel, N. E. (2008). Profit-oriented disassembly-line balancing. International Journal of Production Research. https://doi.org/10.1080/00207540601137207

Ameli, M., Mansour, S., & Ahmadi-Javid, A. (2019). A simulation-optimization model for sustainable product design and efficient end-of-life management based on individual producer responsibility. Resources, Conservation and Recycling. https://doi.org/10.1016/j.resconrec.2018.02.031

Amiri, M., & Mohtashami, A. (2012). Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-011-3802-8

Avikal, S., Jain, R., & Mishra, P. K. (2014). A Kano model, AHP and M-TOPSIS method-based technique for disassembly line balancing under fuzzy environment. Applied Soft Computing Journal. https://doi.org/10.1016/j.asoc.2014.08.002

Avikal, S., Mishra, P. K., & Jain, R. (2013). An AHP and PROMETHEE methods-based environment friendly heuristic for disassembly line balancing problems. Interdisciplinary Environmental Review. https://doi.org/10.1504/ier.2013.054125

Avikal, S., Mishra, P. K., & Jain, R. (2014). A Fuzzy AHP and PROMETHEE method-based heuristic for disassembly line balancing problems. International Journal of Production Research. https://doi.org/10.1080/00207543.2013.831999

Avikal, S., Mishra, P. K., Jain, R., & Yadav, H. C. (2013). A PROMETHEE Method Based Heuristic for Disassembly Line Balancing Problem. Industrial Engineering and Management Systems. https://doi.org/10.7232/iems.2013.12.3.254

Azadivar, F., & Wang, J. (2000). Facility layout optimization using simulation and genetic algorithms. International Journal of Production Research. https://doi.org/10.1080/00207540050205154

Bentaha, M. L., Battaiä, O., & Dolgui, A. (2015). An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times. International Journal of Production Research. https://doi.org/10.1080/00207543.2014.961212

Bentaha, M. L., Battaïa, O., & Dolgui, A. (2014). A sample average approximation method for disassembly line balancing problem under uncertainty. Computers and Operations Research. https://doi.org/10.1016/j.cor.2014.05.006

Bentaha, M. L., Battaïa, O., Dolgui, A., & Hu, S. J. (2015). Second order conic approximation for disassembly line design with joint probabilistic constraints. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2015.06.019

Deniz, N., & Ozcelik, F. (2019). An extended review on disassembly line balancing with bibliometric & social network and future study realization analysis. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2019.03.188

Ding, H., Benyoucef, L., & Xie, X. (2003). Simulation Optimization in Manufacturing Analysis: A Simulation-Optimization Approach Using Genetic Search for Supplier Selection. Proceedings of the 35th Conference on Winter Simulation: Driving Innovation.

Ding, L. P., Feng, Y. X., Tan, J. R., & Gao, Y. C. (2010). A new multi-objective ant colony algorithm for solving the disassembly line balancing problem. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-009-2303-5

Dowsland, K. A., & Thompson, J. M. (2012). Simulated annealing. In Handbook of Natural Computing. https://doi.org/10.1007/978-3-540-92910-9_49

Edis, E. B., Ilgin, M. A., & Edis, R. S. (2019). Disassembly line balancing with sequencing decisions: A mixed integer linear programming model and extensions. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2019.117826

Eglese, R. W. (1990). Simulated annealing: A tool for operational research. European Journal of Operational Research. https://doi.org/10.1016/0377-2217(90)90001-R

Fang, Y., Liu, Q., Li, M., Laili, Y., & Pham, D. T. (2019). Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2018.12.035

Güçdemir, H., & Selim, H. (2017). Customer centric production planning and control in job shops: A simulation optimization approach. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2017.02.004

Gungor, A., & Gupta, S. M. (1999). Issues in environmentally conscious manufacturing and product recovery: A survey. Computers and Industrial Engineering. https://doi.org/10.1016/S0360-8352(99)00167-9

Güngör, Askiner, & Gupta, S. M. (2001). A solution approach to the disassembly line balancing problem in the presence of task failures. International Journal of Production Research. https://doi.org/10.1080/00207540110052157

Güngör, Aşkiner, & Gupta, S. M. (2002). Disassembly line in product recovery. International Journal of Production Research. https://doi.org/10.1080/00207540210135622

Ilgin, M. Ali, & Tunali, S. (2007). Joint optimization of spare parts inventory and maintenance policies using genetic algorithms. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-006-0618-z

Ilgin, Mehmet Ali. (2019). A DEMATEL-Based Disassembly Line Balancing Heuristic. Journal of Manufacturing Science and Engineering, Transactions of the ASME. https://doi.org/10.1115/1.4041925

Ilgin, Mehmet Ali, Akçay, H., & Araz, C. (2017). Disassembly line balancing using linear physical programming. International Journal of Production Research. https://doi.org/10.1080/00207543.2017.1324225

Ilgin, Mehmet Ali, & Gupta, S. M. (2010). Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2009.09.037

Ilgin, Mehmet Ali, & Taşoǧlu, G. T. (2016). Simultaneous Determination of Disassembly Sequence and Disassembly-to-Order Decisions Using Simulation Optimization. Journal of Manufacturing Science and Engineering, Transactions of the ASME. https://doi.org/10.1115/1.4033603

Kalayci, C. B., & Gupta, S. M. (2013a). A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-013-4990-1

Kalayci, C. B., & Gupta, S. M. (2013b). Ant colony optimization for sequence-dependent disassembly line balancing problem. Journal of Manufacturing Technology Management. https://doi.org/10.1108/17410381311318909

Kalayci, C. B., & Gupta, S. M. (2013c). Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2013.06.067

Kalayci, C. B., & Gupta, S. M. (2013d). Balancing a sequencedependent disassembly line using simulated annealing algorithm. Applications of Management Science. https://doi.org/10.1108/S0276-8976(2013)0000016008

Kalayci, C. B., & Gupta, S. M. (2014). A tabu search algorithm for balancing a sequence-dependent disassembly line. Production Planning and Control. https://doi.org/10.1080/09537287.2013.782949

Kalayci, C. B., Hancilar, A., Gungor, A., & Gupta, S. M. (2015). Multi-objective fuzzy disassembly line balancing using a hybrid discrete artificial bee colony algorithm. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2014.11.015

Kalayci, C. B., Polat, O., & Gupta, S. M. (2016). A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem. Annals of Operations Research. https://doi.org/10.1007/s10479-014-1641-3

Koc, A., Sabuncuoglu, I., & Erel, E. (2009). Two exact formulations for disassembly line balancing problems with task precedence diagram construction using an AND/OR graph. IIE Transactions (Institute of Industrial Engineers). https://doi.org/10.1080/07408170802510390

Li, Z., Çil, Z. A., Mete, S., & Kucukkoc, I. (2020). A fast branch, bound and remember algorithm for disassembly line balancing problem. International Journal of Production Research, 58(11), 3220-3234.

Lin, Y. K., & Lin, H. C. (2015). Bicriteria scheduling problem for unrelated parallel machines with release dates. Computers and Operations Research. https://doi.org/10.1016/j.cor.2015.04.025

Liu, J., Zhou, Z., Pham, D. T., Xu, W., Yan, J., Liu, A., … Liu, Q. (2018). An improved multi-objective discrete bees algorithm for robotic disassembly line balancing problem in remanufacturing. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-018-2183-7

Mattila, V., & Virtanen, K. (2015). Ranking and selection for multiple performance measures using incomplete preference information. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2014.10.028

McGovern, S. M., & Gupta, S. M. (2007). Combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem. International Journal of Production Research. https://doi.org/10.1080/00207540701476281

McGovern, Seamus M., & Gupta, S. M. (2006). Ant colony optimization for disassembly sequencing with multiple objectives. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-005-0037-6

McGovern, Seamus M., & Gupta, S. M. (2007). A balancing method and genetic algorithm for disassembly line balancing. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2005.03.055

Ozcan, Y. A., Tànfani, E., & Testi, A. (2017). Improving the performance of surgery-based clinical pathways: a simulation-optimization approach. Health Care Management Science. https://doi.org/10.1007/s10729-016-9371-5

Özceylan, E., Kalayci, C. B., Güngör, A., & Gupta, S. M. (2019). Disassembly line balancing problem: a review of the state of the art and future directions. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1428775

Özceylan, E., & Paksoy, T. (2013). Reverse supply chain optimisation with disassembly line balancing. International Journal of Production Research. https://doi.org/10.1080/00207543.2013.784405

Özceylan, E., & Paksoy, T. (2014a). Fuzzy mathematical programming approaches for reverse supply chain optimization with disassembly line balancing problem. Journal of Intelligent and Fuzzy Systems. https://doi.org/10.3233/IFS-130875

Özceylan, E., & Paksoy, T. (2014b). Interactive fuzzy programming approaches to the strategic and tactical planning of a closed-loop supply chain under uncertainty. International Journal of Production Research. https://doi.org/10.1080/00207543.2013.865852

Paksoy, T., Güngör, A., Özceylan, E., & Hancilar, A. (2013). Mixed model disassembly line balancing problem with fuzzy goals. International Journal of Production Research. https://doi.org/10.1080/00207543.2013.795251

Seidi, M., & Saghari, S. (2016). The balancing of disassembly line of automobile engine using Genetic Algorithm (GA) in fuzzy environment. Industrial Engineering and Management Systems. https://doi.org/10.7232/iems.2016.15.4.364

Tasoglu, G., & Yildiz, G. (2019). Simulated annealing based simulation optimization method for solving integrated berth allocation and quay crane scheduling problems. Simulation Modelling Practice and Theory. https://doi.org/10.1016/j.simpat.2019.101948

Tuncel, E., Zeid, A., & Kamarthi, S. (2014). Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-012-0711-0

Wang, K., Li, X., & Gao, L. (2019). Modeling and optimization of multi-objective partial disassembly line balancing problem considering hazard and profit. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2018.11.114

Wang, S., Guo, X., & Liu, J. (2019). An efficient hybrid artificial bee colony algorithm for disassembly line balancing problem with sequence-dependent part removal times. Engineering Optimization. https://doi.org/10.1080/0305215X.2018.1564918

Xiao, S., Wang, Y., Yu, H., & Nie, S. (2017). An entropy-based adaptive hybrid particle swarm optimization for disassembly line balancing problems. Entropy. https://doi.org/10.3390/e19110596

Yin, P. Y., Wu, T. H., & Hsu, P. Y. (2017). Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D. Energy. https://doi.org/10.1016/j.energy.2017.09.103

Zeng, Q., Diabat, A., & Zhang, Q. (2015). A simulation optimization approach for solving the dual-cycling problem in container terminals. Maritime Policy and Management. https://doi.org/10.1080/03088839.2015.1043362

Zhang, Z., Wang, K., Zhu, L., & Wang, Y. (2017). A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2017.05.053

Zhou, Y., Guo, X., & Li, D. (2020). A dynamic programming approach to a multi-objective disassembly line balancing problem. Annals of Operations Research, 1-24.

Zhu, L., Zhang, Z., & Wang, Y. (2018). A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1471238

Wang, K., Li, X., Gao, L., Li, P., & Sutherland, J. W. (2021). A Discrete Artificial Bee Colony Algorithm for Multiobjective Disassembly Line Balancing of End-of-Life Products. IEEE Transactions on Cybernetics, (Basım süresince).