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On the end-of-life state oriented multi-objective disassembly line balancing problem

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Abstract

The biggest difference between a disassembly line and an assembly line is that there are many uncertainties in structure and quality of the disassembled products in a disassembly line. The disassembly line balancing problem, considering the effect of end-of-life states caused by the uncertainty of the structure or the quality of the disassembled products, is addressed in this paper. A multi-objective mathematical model for the addressed problem is built with three optimization goals: minimizing the number of workstations, minimizing the idle index and minimizing the number of resources. Then a multi-objective hybrid migrating birds optimization algorithm is proposed, which uses a greedy random search operation based on embedding mechanism to generate neighborhood individuals. To avoid the problem of easily being trapped into a local optimum by a basic migrating birds optimization algorithm, a reset mechanism based on simulated annealing operation is set up to accept other solutions with a certain probability, so that the algorithm can escape out of a local optimum. By solving disassembly examples of different scales in the literature and comparing with the existing algorithms, the effectiveness and superiority of the proposed multi-objective hybrid migrating birds optimization algorithm is validated. Finally, the proposed model and algorithm are applied to solving two disassembly instances, and the solving results are compared with the single-objective optimal solution solved by LINGO 11.0 solver and the basic migrating birds optimization algorithm to further identify the performance of the proposed algorithm.

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References

  • Altekin, F. T. (2016). A piecewise linear model for stochastic disassembly line balancing. Ifac Papersonline,49(12), 932–937.

    Google Scholar 

  • Altekin, F. T. (2017). A comparison of piecewise linear programming formulations for stochastic disassembly line balancing. International Journal of Production Research,55(24), 7412–7434.

    Google Scholar 

  • Altekin, F. T., & Akkan, C. (2012). Task-failure-driven rebalancing of disassembly lines. International Journal of Production Research,50(18), 4955–4976.

    Google Scholar 

  • Altekin, F. T., Kandiller, L., & Ozdemirel, N. E. (2008). Profit-oriented disassembly-line balancing. International Journal of Production Research,46(10), 2675–2693.

    Google Scholar 

  • Avikal, S., Jain, R., & Mishra, P. K. (2014a). A Kano model, AHP and M-TOPSIS method-based technique for disassembly line balancing under fuzzy environment. Applied Soft Computing,25, 519–529.

    Google Scholar 

  • Avikal, S., Mishra, P. K., & Jain, R. (2014b). A Fuzzy AHP and PROMETHEE method-based heuristic for disassembly line balancing problems. International Journal of Production Research,52(5), 1306–1317.

    Google Scholar 

  • Aydemir-Karadag, A., & Turkbey, O. (2013). Multi-objective optimization of stochastic disassembly line balancing with station paralleling. Computers & Industrial Engineering,65(3), 413–425.

    Google Scholar 

  • Bentaha, M. L., Battaia, O., & Dolgui, A. (2014a). Disassembly line balancing and sequencing under uncertainty. In T. K. Lien (Ed.), 21st Cirp conference on life cycle engineering (pp. 239–244). Amsterdam: Elsevier.

    Google Scholar 

  • Bentaha, M. L., Battaia, O., & Dolgui, A. (2014b). Lagrangian relaxation for stochastic disassembly line balancing problem. In H. ElMaraghy (Ed.), Variety management in manufacturing: Proceedings of the 47th Cirp conference on manufacturing systems (pp. 56–60). Amsterdam: Elsevier.

  • Bentaha, M. L., Battaia, O., & Dolgui, A. (2014c). A sample average approximation method for disassembly line balancing problem under uncertainty. Computers & Operations Research,51, 111–122.

    Google Scholar 

  • Bentaha, M. L., Battaïa, O., & Dolgui, A. (2015a). 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.

    Google Scholar 

  • Bentaha, M. L., Battaia, O., Dolgui, A., & Hu, S. J. (2014d). Dealing with uncertainty in disassembly line design. CIRP Annals-Manufacturing Technology,63(1), 21–24.

    Google Scholar 

  • Bentaha, M. L., Battaia, O., Dolgui, A., & Hu, S. J. (2015b). Second order conic approximation for disassembly line design with joint probabilistic constraints. European Journal of Operational Research,247(3), 957–967.

    Google Scholar 

  • Bentaha, M. L., Dolgui, A., Battaia, O., Riggs, R. J., & Hu, J. (2018). Profit-oriented partial disassembly line design: Dealing with hazardous parts and task processing times uncertainty. International Journal of Production Research,56(24), 7220–7242.

    Google Scholar 

  • Ding, L. P., Tan, J. R., Feng, Y. X., & Gao, Y. C. (2009). Multiobjective optimization for disassembly line balancing based on Pareto ant colony algorithm. Computer Integrated Manufacturing Systems,15(7), 1406–1413+1429.

    Google Scholar 

  • Duman, E., Uysal, M., & Alkaya, A. F. (2012). Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem. Information Sciences,217, 65–77.

    Google Scholar 

  • Fang, Y., Liu, Q., Li, M., Laili, Y., & Duc Truong, P. (2019a). Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations. European Journal of Operational Research,276(1), 160–174.

    Google Scholar 

  • Fang, Y., Ming, H., Li, M., Liu, Q., & Duc Truong, P. (2019b). Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1602290.

    Article  Google Scholar 

  • Gao, Y., Wang, Q., Feng, Y., Zheng, H., Zheng, B., & Tan, J. (2018). An energy-saving optimization method of dynamic scheduling for disassembly line. Energies,11(5), 1261–1278.

    Google Scholar 

  • Gungor, A., & Gupta, S. M. (2001). A solution approach to the disassembly line balancing problem in the presence of task failures. International Journal of Production Research,39(7), 1427–1467.

    Google Scholar 

  • Gungor, A., & Gupta, S. M. (2002). Disassembly line in product recovery. International Journal of Production Research,40(11), 2569–2589.

    Google Scholar 

  • Gupta, S. M., Pochampally, K., & Kamarthi, S. V. (2001). Complications in disassembly line balancing. In S. M. Gupta (Ed.), Environmentally Conscious Manufacturing (pp. 289–298). Bellingham: SPIE.

    Google Scholar 

  • Hezer, S., & Kara, Y. (2015). A network-based shortest route model for parallel disassembly line balancing problem. International Journal of Production Research,53(6), 1849–1865.

    Google Scholar 

  • Hummel, D., & Beukenberg, M. (1989). Aerodynamic interference effects in formation flight of birds. Journal Für Ornithologie,130(1), 15–24.

    Google Scholar 

  • Ilgin, M. A. (2019). A DEMATEL-based disassembly line balancing heuristic. Journal of Manufacturing Science and Engineering-Transactions of the Asme,141(2), 021002.

    Google Scholar 

  • Ilgin, M. A., Akçay, H., & Araz, C. (2017). Disassembly line balancing using linear physical programming. International Journal of Production Research,55(20), 6108–6119.

    Google Scholar 

  • Kalayci, C. B., & Gupta, S. M. (2013a). Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem. Expert Systems with Applications,40(18), 7231–7241.

    Google Scholar 

  • Kalayci, C. B., & Gupta, S. M. (2013b). 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.

    Google Scholar 

  • Kalayci, C. B., & Gupta, S. M. (2014). A tabu search algorithm for balancing a sequence-dependent disassembly line. Production Planning & Control,25(2), 149–160.

    Google Scholar 

  • Kalayci, C. B., Hancilar, A., Gungor, A., & Gupta, S. M. (2015a). Multi-objective fuzzy disassembly line balancing using a hybrid discrete artificial bee colony algorithm. Journal of Manufacturing Systems,37, 672–682.

    Google Scholar 

  • Kalayci, C. B., Polat, O., & Gupta, S. M. (2015b). A variable neighbourhood search algorithm for disassembly lines. Journal of Manufacturing Technology Management,26(2), 182–194.

    Google Scholar 

  • Kalayci, C. B., Polat, O., & Gupta, S. M. (2016). A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem. Annals of Operations Research,242(2), 321–354.

    Google Scholar 

  • Kazancoglu, Y., & Ozturkoglu, Y. (2018). Integrated framework of disassembly line balancing with Green and business objectives using a mixed MCDM. Journal of Cleaner Production,191, 179–191.

    Google Scholar 

  • 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,41(10), 866–881.

    Google Scholar 

  • Li, Z., Cil, Z. A., Mete, S., & Kucukkoc, I. (2019). A fast branch, bound and remember algorithm for disassembly line balancing problem. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1630774.

    Article  Google Scholar 

  • Li, L., Zhang, Z., Zhu, L., & Zou, B. (2018). Modeling and optimizing for multi-objective partial disassembly line balancing problem. Journal of Mechanical Engineering,54(3), 125–136.

    Google Scholar 

  • Lissaman, P. B., & Shollenberger, C. A. (1970). Formation flight of birds. Science (New York, N.Y.),168(3934), 1003–1005.

    Google Scholar 

  • Liu, J., & Wang, S. (2017). Balancing disassembly line in product recovery to promote the coordinated development of economy and environment. Sustainability,9(2), 1–15.

    Google Scholar 

  • McGovern, S. M., & Gupta, S. M. (2004). 2-opt heuristic for the disassembly line balancing problem. In S. M. Gupta (Ed.), Environmentally conscious manufacturing Iii (pp. 71–84). Bellingham: SPIE.

    Google Scholar 

  • McGovern, S. M., & Gupta, S. M. (2006). Ant colony optimization for disassembly sequencing with multiple objectives. International Journal of Advanced Manufacturing Technology,30(5–6), 481–496.

    Google Scholar 

  • McGovern, S. M., & Gupta, S. M. (2007a). A balancing method and genetic algorithm for disassembly line balancing. European Journal of Operational Research,179(3), 692–708.

    Google Scholar 

  • McGovern, S. M., & Gupta, S. M. (2007b). Combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem. International Journal of Production Research,45(18–19), 4485–4511.

    Google Scholar 

  • Meng, T., Pan, Q.-K., Li, J.-Q., & Sang, H.-Y. (2018a). An improved migrating birds optimization for an integrated lot-streaming flow shop scheduling problem. Swarm and Evolutionary Computation,38, 64–78.

    Google Scholar 

  • Meng, K., Qian, X., Lou, P., & Zhang, J. (2018b). Smart recovery decision-making of used industrial equipment for sustainable manufacturing: Belt lifter case study. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1439-2.

    Article  Google Scholar 

  • Mete, S., Cil, Z. A., Agpak, K., Ozceylan, E., & Dolgui, A. (2016a). A solution approach based on beam search algorithm for disassembly line balancing problem. Journal of Manufacturing Systems,41, 188–200.

    Google Scholar 

  • Mete, S., Cil, Z. A., Ozceylan, E., & Agpak, K. (2016b). Resource constrained disassembly line balancing problem. Ifac Papersonline,49(12), 921–925.

    Google Scholar 

  • Niroomand, S., Hadi-Vencheh, A., Sahin, R., & Vizvari, B. (2015). Modified migrating birds optimization algorithm for closed loop layout with exact distances in flexible manufacturing systems. Expert Systems with Applications,42(19), 6586–6597.

    Google Scholar 

  • Özceylan, E., Kalayci, C. B., Güngör, A., & Gupta, S. M. (2018). 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.

    Google Scholar 

  • Paksoy, T., Gungor, A., Ozceylan, E., & Hancilar, A. (2013). Mixed model disassembly line balancing problem with fuzzy goals. International Journal of Production Research,51(20), 6082–6096.

    Google Scholar 

  • Pistolesi, F., Lazzerini, B., Mura, M. D., & Dini, G. (2018). EMOGA: A hybrid genetic algorithm with extremal optimization core for multiobjective disassembly line balancing. IEEE Transactions on Industrial Informatics,14(3), 1089–1098.

    Google Scholar 

  • Rao, R. V., Rai, D. P., & Balic, J. (2019). Multi-objective optimization of abrasive waterjet machining process using Jaya algorithm and PROMETHEE Method. Journal of Intelligent Manufacturing,30(5), 2101–2127.

    Google Scholar 

  • Rayner, J. (1979). A new approach to animal flight mechanics. Journal of Experimental Biology,80(1), 17–54.

    Google Scholar 

  • Ren, Y., Yu, D., Zhang, C., Tian, G., Meng, L., & Zhou, X. (2017). An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem. International Journal of Production Research,55(24), 7302–7316.

    Google Scholar 

  • Ren, Y., Zhang, C., Zhao, F., Tian, G., Lin, W., Meng, L., et al. (2018). Disassembly line balancing problem using interdependent weights-based multi-criteria decision making and 2-Optimal algorithm. Journal of Cleaner Production,174, 1475–1486.

    Google Scholar 

  • Riggs, R. J., Battaïa, O., & Hu, S. J. (2015). Disassembly line balancing under high variety of end of life states using a joint precedence graph approach. Journal of Manufacturing Systems,37, 638–648.

    Google Scholar 

  • Sankararao, B., & Chang, K. Y. (2011). Development of a robust multiobjective simulated annealing algorithm for solving multiobjective optimization problems. Industrial and Engineering Chemistry Research,50(50), 6728–6742.

    Google Scholar 

  • 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,15(4), 364–373.

    Google Scholar 

  • Sioud, A., & Gagne, C. (2018). Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times. European Journal of Operational Research,264(1), 66–73.

    Google Scholar 

  • Soto, R., Crawford, B., Almonacid, B., & Paredes, F. (2015). A migrating birds optimization algorithm for machine-part cell formation problems. In G. Sidorov & S. N. GaliciaHaro (Eds.), Advances in artificial intelligence and soft computing, Micai 2015, Pt I (pp. 270–281). Berlin: Springer.

    Google Scholar 

  • 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,46(6), 1405–1429.

    Google Scholar 

  • Tongur, V., & Ulker, E. (2019). PSO-based improved multi-flocks migrating birds optimization (IMFMBO) algorithm for solution of discrete problems. Soft Computing,23(14), 5469–5484.

    Google Scholar 

  • Tuncel, E., Zeid, A., & Kamarthi, S. (2014). Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning. Journal of Intelligent Manufacturing,25(4), 647–659.

    Google Scholar 

  • Wang, K., Li, X., & Gao, L. (2019a). A multi-objective discrete flower pollination algorithm for stochastic two-sided partial disassembly line balancing problem. Computers & Industrial Engineering,130, 634–649.

    Google Scholar 

  • Wang, K., Li, X., Gao, L., & Garg, A. (2019b). Partial disassembly line balancing for energy consumption and profit under uncertainty. Robotics and Computer-Integrated Manufacturing,59, 235–251.

    Google Scholar 

  • Wang, W., Mo, D. Y., Wang, Y., & Tseng, M. M. (2019c). Assessing the cost structure of component reuse in a product family for remanufacturing. Journal of Intelligent Manufacturing,30(2), 575–587.

    Google Scholar 

  • Wang, K., Zhang, Z., Mao, L., & Li, L. (2017). Pareto artificial fish swarm algorithm for multi-objective disassembly line balancing problems. China Mechanical Engineering,28(2), 183–190.

    Google Scholar 

  • Xia, X., Liu, W., Zhang, Z., Wang, L., Cao, J., & Liu, X. (2019). A balancing method of mixed-model disassembly line in random working environment. Sustainability,11(8), 2304.

    Google Scholar 

  • Xiao, S., Wang, Y., Yu, H., & Nie, S. (2017). An entropy-based adaptive hybrid particle swarm optimization for disassembly line balancing problems. Entropy,19(11), 596.

    Google Scholar 

  • Xie, Z., Jia, Y., Zhang, C., Shao, X., & Li, D. (2015). Blocking flow shop scheduling problem based on migrating birds optimization. Computer Integrated Manufacturing Systems,21(8), 2099–2107.

    Google Scholar 

  • Yang, Y., Yuan, G., Zhuang, Q., & Tian, G. (2019). Multi-objective low-carbon disassembly line balancing for agricultural machinery using MDFOA and fuzzy AHP. Journal of Cleaner Production,233, 1465–1474.

    Google Scholar 

  • Zhang, B., Pan, Q.-K., Gao, L., Zhang, X.-L., Sang, H.-Y., & Li, J.-Q. (2017a). An effective modified migrating birds optimization for hybrid flowshop scheduling problem with lot streaming. Applied Soft Computing,52, 14–27.

    Google Scholar 

  • Zhang, Z., Tang, Q., Han, D., & Li, Z. (2019). Enhanced migrating birds optimization algorithm for U-shaped assembly line balancing problems with workers assignment. Neural Computing and Applications,31(11), 7501–7515.

    Google Scholar 

  • Zhang, Z., Wang, K., Zhu, L., & Wang, Y. (2017b). A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem. Expert Systems with Applications,86, 165–176.

    Google Scholar 

  • Zheng, F., He, J., Chu, F., & Liu, M. (2018). A new distribution-free model for disassembly line balancing problem with stochastic task processing times. International Journal of Production Research,56(24), 7341–7353.

    Google Scholar 

  • 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,56(24), 7354–7374.

    Google Scholar 

  • Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach. IEEE Transactions on Evolutionary Computation,3(4), 257–271.

    Google Scholar 

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Acknowledgements

This research was partially funded by the [National Natural Science Foundation of China] under Grant [Nos. 51205328, 51675450]; [Youth Foundation for Humanities and Social Sciences of Ministry of Education of China] under Grant [No. 18YJC630255]; and the [Sichuan Science and Technology Program] under Grant [No. 2019YFG0285].

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  1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Lixia Zhu, Zeqiang Zhang & Ning Cai

  2. Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu, 610031, China

    Lixia Zhu, Zeqiang Zhang & Ning Cai

  3. Department of Mathematics and Computer Science, Auburn University at Montgomery, Montgomery, AL, USA

    Yi Wang

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Zhu, L., Zhang, Z., Wang, Y. et al. On the end-of-life state oriented multi-objective disassembly line balancing problem. J Intell Manuf 31, 1403–1428 (2020). https://doi.org/10.1007/s10845-019-01519-3

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