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Multi-layered coding-based study on optimization algorithms for automobile production logistics scheduling

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  • Yue, Guo
  • Tailai, Guo
  • Dan, Wei

Abstract

With the acceleration of economic globalization, competition among manufacturing industries has become increasingly fierce. Automobile manufacturing has always been a critical investment and development industry in different countries. For the automobile manufacturing industry, the logistics scheduling problem of automobile production is affects automobile manufacturing enterprises’ ability to compete. This paper discusses disruptive technologies, such as AI, IoT, Big data, etc., to solve production problems. Therefore, production logistics systems research is essential to automobile manufacturing enterprises, to improve production efficiency, reduce production costs, and increase enterprises’ economic benefits. We present three kinds of mathematical models designed and calculated by a genetic algorithm, aimed at the Pareto solution set to solve multi-objective optimization, as well as designs for a new contrast flow, which can quickly find the optimal solution and simulate the algorithm.

Suggested Citation

  • Yue, Guo & Tailai, Guo & Dan, Wei, 2021. "Multi-layered coding-based study on optimization algorithms for automobile production logistics scheduling," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:tefoso:v:170:y:2021:i:c:s0040162521003218
    DOI: 10.1016/j.techfore.2021.120889
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Shengjia & Wang, Junhao & Xu, Bing, 2022. "Innovative coupling and coordination: Automobile and digital industries," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    2. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.

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