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Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line

Author

Listed:
  • Ullah Saif

    (Huazhong University of Science and Technology
    University of Engineering and Technology)

  • Zailin Guan

    (Huazhong University of Science and Technology)

  • Li Zhang

    (Huazhong University of Science and Technology)

  • Fei Zhang

    (Huazhong University of Science and Technology)

  • Baoxi Wang

    (Huazhong University of Science and Technology)

  • Jahanzaib Mirza

    (University of Engineering and Technology)

Abstract

In multi-mixed model assembly lines, customer orders with different demand of models and due dates make it critical to decide the sequencing of different models and balancing of lines. Therefore, current research, first time, investigated an order oriented simultaneous sequencing and balancing problem of multi-mixed model assembly lines with an aim to minimize the variation in material usage, minimize the maximum makespan among the multi-lines and minimize the penalty cost of the late delivery models from different orders simultaneously. Moreover, a new mix-minimum part sequencing method is developed and a multi-objective artificial bee colony (MABC) algorithm is proposed to get the solution for the considered problem. Experiments are performed on standard assembly line data taken from operations library (OR) to test the performance of the proposed MABC algorithm against a famous multi-objective algorithm (Strength Pareto Evolutionary Algorithm i.e. SPEA 2) in literature. Moreover, the proposed MABC algorithm is also tested on the data taken from a well reputed manufacturing company in China against the famous algorithm in literature (i.e. SPEA 2). End results indicate that the proposed MABC outperforms SPEA 2 algorithm for both standard data and company data problems.

Suggested Citation

  • Ullah Saif & Zailin Guan & Li Zhang & Fei Zhang & Baoxi Wang & Jahanzaib Mirza, 2019. "Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1195-1220, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1316-4
    DOI: 10.1007/s10845-017-1316-4
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    References listed on IDEAS

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    1. Scholl, Armin, 1995. "Balancing and sequencing of assembly lines," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 9690, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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    Cited by:

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    2. Hashemi-Petroodi, S. Ehsan & Thevenin, Simon & Kovalev, Sergey & Dolgui, Alexandre, 2023. "Markov decision process for multi-manned mixed-model assembly lines with walking workers," International Journal of Production Economics, Elsevier, vol. 255(C).
    3. Maximilian Stauder & Niklas Kühl, 2022. "AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 709-747, September.
    4. Andrea Maria Zanchettin, 2022. "Robust scheduling and dispatching rules for high-mix collaborative manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 293-316, June.
    5. Zixiang Li & Mukund Nilakantan Janardhanan & S. G. Ponnambalam, 2021. "Cost-oriented robotic assembly line balancing problem with setup times: multi-objective algorithms," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 989-1007, April.
    6. Daniele Marini & Jonathan R. Corney, 2021. "Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 611-631, February.
    7. Yicong Gao & Shanhe Lou & Hao Zheng & Jianrong Tan, 2023. "A data-driven method of selective disassembly planning at end-of-life under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 565-585, February.
    8. Yongjian Jiang & Dongyun Wang & Wenjun Xia & Wencai Li, 2022. "Optimisation of the Logistics System in an Electric Motor Assembly Flowshop by Integrating the Taguchi Approach and Discrete Event Simulation," Sustainability, MDPI, vol. 14(24), pages 1-15, December.

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