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Hybrid Human–AI Collaboration for Optimized Fuel Delivery Management

Author

Listed:
  • Iouri Semenov

    (University WSB Merito in Poznań, ul. Powstańców Wielkopolskich 5, 61-895 Poznań, Poland
    The Royal Institution of Naval Architects, London WC2N 5DA, UK)

  • Marianna Jacyna

    (Faculty of Transport, Warsaw University of Technology, 00-662 Warsaw, Poland)

  • Izabela Auguściak

    (University WSB Merito in Poznań, ul. Powstańców Wielkopolskich 5, 61-895 Poznań, Poland)

  • Mariusz Wasiak

    (Faculty of Transport, Warsaw University of Technology, 00-662 Warsaw, Poland)

Abstract

This article deals with the analysis and exploration of the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The authors point out that the implementation of advanced AI technologies into already functioning and often complex systems, such as enterprise resource planning (ERP), presents significant technical challenges and requires a well-thought-out integration strategy. The complexity arises from the need to align new solutions with existing processes, resources, and data. Using the example of a fuel distribution system, the authors present the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The article presents a comprehensive analysis of the smart upgrade of fuel delivery management (FDM) architecture by incorporating an AI app to solve complex problems, such as predicting demand or traffic flows, as well as correctly detecting near-miss events. Technological convergence enables the mutual pursuit of improving the management process by developing soft skills and expanding knowledge managers. The authors’ findings show that an important factor for successful convergence is horizontal and vertical matching of the human knowledge and artificial intelligence cooperation for archive max positive synergy. Some recommendations could be useful for tank truck operators as a starting point to predict demand patterns, smart route planning, etc., where an AI app could be very successful.

Suggested Citation

  • Iouri Semenov & Marianna Jacyna & Izabela Auguściak & Mariusz Wasiak, 2025. "Hybrid Human–AI Collaboration for Optimized Fuel Delivery Management," Energies, MDPI, vol. 18(19), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:19:p:5203-:d:1761824
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    References listed on IDEAS

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    1. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    2. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    3. Wenwen Chen & Yangchongyi Men & Noelia Fuster & Celia Osorio & Angel A. Juan, 2024. "Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review," Sustainability, MDPI, vol. 16(21), pages 1-22, October.
    4. Michał Lasota & Aleksandra Zabielska & Marianna Jacyna & Piotr Gołębiowski & Renata Żochowska & Mariusz Wasiak, 2024. "Method for Delivery Planning in Urban Areas with Environmental Aspects," Sustainability, MDPI, vol. 16(4), pages 1-18, February.
    5. Alejandro Fernández Gil & Eduardo Lalla-Ruiz & Mariam Gómez Sánchez & Carlos Castro, 2025. "The cumulative vehicle routing problem with time windows: models and algorithm," Annals of Operations Research, Springer, vol. 350(1), pages 325-353, July.
    6. Mads Jepsen & Bjørn Petersen & Simon Spoorendonk & David Pisinger, 2008. "Subset-Row Inequalities Applied to the Vehicle-Routing Problem with Time Windows," Operations Research, INFORMS, vol. 56(2), pages 497-511, April.
    7. Izdebski, Mariusz & Jacyna-Gołda, Ilona & Gołda, Paweł, 2022. "Minimisation of the probability of serious road accidents in the transport of dangerous goods," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Farrow, Elissa, 2022. "Determining the human to AI workforce ratio – Exploring future organisational scenarios and the implications for anticipatory workforce planning," Technology in Society, Elsevier, vol. 68(C).
    9. Chen Qu & Eunyoung Kim, 2024. "Reviewing the Roles of AI-Integrated Technologies in Sustainable Supply Chain Management: Research Propositions and a Framework for Future Directions," Sustainability, MDPI, vol. 16(14), pages 1-27, July.
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