access icon free Adaptive quantum genetic algorithm for task sequence planning of complex assembly systems

Task sequence planning (TSP) is the key factor to the efficiency, stableness, and cost of a complex assembly system. To address the issue, an adaptive quantum genetic algorithm based on artificial potential field and gradient of object function is proposed to optimise the solving process, and to obtain the optimal TSP scheme. The simulation results indicate that the proposed algorithm can perform higher efficiency and stableness than the previously reported methods.

Inspec keywords: assembling; planning; genetic algorithms

Other keywords: complex assembly system; optimal TSP scheme; adaptive quantum genetic algorithm; task sequence planning

Subjects: Assembling; Production engineering computing; Optimisation techniques; Industrial applications of IT; Optimisation; Planning

References

    1. 1)
      • 3. Tan, D.P., Chen, S.T., Bao, G.J., et al: ‘An embedded lightweight GUI component library and the ergonomics optimization method for industry process monitoring’, Front. Inf. Technol. Electr. Eng., 2017, doi: 10.1631/FITEE.1601660.
    2. 2)
    3. 3)
      • 2. Tan, D.P., Li, L., Zhu, Y.L., et al: ‘An embedded cloud database service method for distributed industry monitoring’, Trans. Ind. Inf., 2017, pp. 11, doi: 10.1109/TII.2017.2773644.
    4. 4)
    5. 5)
    6. 6)
      • 4. Chen, S.T., Tan, D.P.: ‘A SA-ANN-based modeling method for human cognition mechanism and the PSACO cognition algorithm’, Complexity, 2018, 2018, p. 6264124, doi: 10.1155/2018/6264124.
    7. 7)
    8. 8)
      • 5. Tan, D.P., Zhang, L.B., Ai, Q.L.: ‘An embedded self-adapting network service framework for networked manufacturing system’, J. Intell. Manuf., 2016, pp. 118, doi: 10.1007/s10845-016-1265-3.
    9. 9)
    10. 10)
      • 10. Li, S.Y., Li, P.C.: ‘Quantum genetic algorithm based on real encoding and gradient information of object function’, J. Harbin Inst. Technol., 2006, 38, (8), pp. 12161223, doi: 10.3321/j.issn:0367-6234.2006.08.002.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.0609
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