Multi-Robotic Partial Disassembly Line Balancing with Robotic Efficiency Difference via HNSGA-II
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33030
Multi-Robotic Partial Disassembly Line Balancing with Robotic Efficiency Difference via HNSGA-II

Authors: Tao Yin, Zeqiang Zhang, Wei Liang, Yanqing Zeng, Yu Zhang

Abstract:

To accelerate the remanufacturing process of electronic waste products, this study designs a partial disassembly line with the multi-robotic station to effectively dispose of excessive wastes. The multi-robotic partial disassembly line is a technical upgrade to the existing manual disassembly line. Balancing optimization can make the disassembly line smoother and more efficient. For partial disassembly line balancing with the multi-robotic station (PDLBMRS), a mixed-integer programming model (MIPM) considering the robotic efficiency differences is established to minimize cycle time, energy consumption and hazard index and to calculate their optimal global values. Besides, an enhanced NSGA-II algorithm (HNSGA-II) is proposed to optimize PDLBMRS efficiently. Finally, MIPM and HNSGA-II are applied to an actual mixed disassembly case of two types of computers, the comparison of the results solved by GUROBI and HNSGA-II verifies the correctness of the model and excellent performance of the algorithm, and the obtained Pareto solution set provides multiple options for decision-makers.

Keywords: Waste disposal, disassembly line balancing, multi-robot station, robotic efficiency difference, HNSGA-II.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 501

References:


[1] G. Lowe and R. Bogue, “Design for disassembly: A critical twenty first century discipline,” Assem. Autom., vol. 27, no. 4, pp. 285–289, Oct.2007.
[2] K. Wang, X. Li, L. Gao, and P. Li, “Modeling and balancing for disassembly lines considering workers with different efficiencies,” IEEE Trans. Cybern., to be published.
[3] X. Guo, S. Liu, M. Zhou, and G. Tian, “Disassembly sequence optimization for large-scale products with multiresource constraints using scatter search and petri nets,” IEEE Trans. Cybern., vol. 46, no. 11, pp. 2435–2446, Nov. 2016.
[4] L. Zhu, Z. Zhang, Y. Wang, and N. Cai, “On the end-of-life state oriented multi-objective disassembly line balancing problem,” J. Intell. Manuf., vol. 31, no. 6, pp. 1403–1428, Dec. 2019.
[5] T. Yin, Z. Zhang, Y. Zhang, T. Wu, and W. Liang, “Mixed-integer programming model and hybrid driving algorithm for multi-product partial disassembly line balancing problem with multi-robot workstations,” Robot. Comput. Integr. Manuf., to be published.
[6] E. Cevikcan, D. Aslan, and F. B. Yeni, “Disassembly line design with multi-manned workstations: a novel heuristic optimisation approach,” Int. J. Prod. Res., vol. 58, no. 3, pp. 649–670, Mar. 2019.
[7] Y. Fang, H. Wei, Q. Liu, Y. Li, Z. Zhou, and D. T. Pham, “Minimizing energy consumption and line length of mixed-model multi-robotic disassembly line systems using multi-objective evolutionary optimization,” in Proc. 14th Int. Manuf. Sci. Eng. Conf. MSEC 2019, Erie, PA, USA, 2019, pp. 1–10.
[8] 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,” Eur. J. Oper. Res., vol. 276, no. 1, pp. 160–174, Dec. 2018.
[9] Q. Liu, Y. Li, Y. Fang, Y. Laili, P. Lou, and D. T. Pham, “Many-objective best-order-sort genetic algorithm for mixed-model multi-robotic disassembly line balancing,” in Proc. 11th CIRP Conf. IPS, Zhuhai, China, 2019, pp. 14–21.
[10] H. Ming, Q. Liu, and D. T. Pham, “Multi-robotic disassembly line balancing with uncertain processing time,” in Proc. 11th CIRP Conf. IPS, Zhuhai, China, 2019, pp. 71–76.
[11] L. Zhu, Z. Zhang, and C. Guan, “Multi-objective partial parallel disassembly line balancing problem using hybrid group neighbourhood search algorithm,” J. Manuf. Syst., vol. 56, no. 6, pp. 252–269, Jul. 2020.
[12] 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, Feb. 2019.
[13] 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.
[14] K. Igarashi, T. Yamada, S. M. Gupta, M. Inoue, and N. Itsubo, “Disassembly system modeling and design with parts selection for cost, recycling and CO2 saving rates using multi criteria optimization,” J. Manuf. Syst., vol. 38, pp. 151–164, Jan. 2016.
[15] M. L. Bentaha, O. Battaïa, A. Dolgui, and S. J. Hu, “Second order conic approximation for disassembly line design with joint probabilistic constraints,” Eur. J. Oper. Res., vol. 247, no. 3, pp. 957–967, Dec. 2015.
[16] T. Yin, Z. Zhang, and J. Jiang, “A Pareto-discrete hummingbird algorithm for partial sequence-dependent disassembly line balancing problem considering tool requirements,” J. Manuf. Syst., vol. 60, pp. 406–428, Jul. 2021.
[17] 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, Dec. 2018.
[18] Y. Zhang, Z. Zhang, C. Guan, and P. Xu, “Improved whale optimisation algorithm for two-sided disassembly line balancing problems considering part characteristic indexes,” Int. J. Prod. Res., to be published.
[19] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, Apr. 2002.
[20] 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, Sep. 2006.