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Streamlining Distribution Routes Using the Language Model of Artificial Intelligence

Author

Listed:
  • Kristína Kleinová

    (Institute of Logistics and Transport, Technical University of Kosice, 042 00 Kosice, Slovakia)

  • Martin Straka

    (Institute of Logistics and Transport, Technical University of Kosice, 042 00 Kosice, Slovakia)

Abstract

This article addresses the use of artificial intelligence for the needs of effective, sustainable development in logistics and its components. The subject of this article is to highlight the possibility of processing optimization methods using an artificial intelligence module. The goal is to determine whether the AI module can replicate the same, or at least have a similar result, as the traditional optimization methods used in practice. The challenge involves constantly identifying reserves in already highly sophisticated micro-logistics systems using modern commercial means of artificial intelligence. Applying artificial intelligence to elements of a company’s micro-logistics model is a new approach. This article aims to determine whether artificial intelligence can reduce costs through calculations in a specific area defined for it. By optimizing distribution routes using ChatGPT-3.5, we significantly reduced the total distance traveled, leading to substantial savings in transportation costs. This optimization led to a significant improvement in the efficiency of logistic processes and considerable cost savings. This result demonstrates that artificial intelligence can be an effective tool for solving complex logistic tasks. The possibilities of effectively sustainable logistics development with the help of artificial intelligence lie not only in the quality of the achieved outputs but also in the speed of the calculations and the procedures for solving defined project tasks. It follows from this definition that artificial intelligence will continue to play an essential role in the defined field of logistics in the future.

Suggested Citation

  • Kristína Kleinová & Martin Straka, 2024. "Streamlining Distribution Routes Using the Language Model of Artificial Intelligence," Sustainability, MDPI, vol. 16(16), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6890-:d:1454117
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    References listed on IDEAS

    as
    1. Avraham, Edison & Raviv, Tal, 2020. "The data-driven time-dependent traveling salesperson problem," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 25-40.
    2. Rashmi Ranjan Panigrahi & Avinash K. Shrivastava & Karishma M. Qureshi & Bhavesh G. Mewada & Saleh Yahya Alghamdi & Naif Almakayeel & Ali Saeed Almuflih & Mohamed Rafik N. Qureshi, 2023. "AI Chatbot Adoption in SMEs for Sustainable Manufacturing Supply Chain Performance: A Mediational Research in an Emerging Country," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
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