AI-Powered Optimization Solutions in The Logistics and Transportation Industry
Soland, Tom (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202505069411
https://urn.fi/URN:NBN:fi:amk-202505069411
Tiivistelmä
The development and increased application of Artificial Intelligence (AI) in ground transportation has had significant impacts on efficiency, cost effectiveness and sustainability. As global supply chains become more complex, AI-powered optimization tools are seeing increased use for functions such as route planning, fleet management and predictive decision making. This desktop research study aims to evaluate how AI-driven optimization solutions affect transit times, delivery accuracy, resource utilization and sustainability implications in ground transportation.
Analytical frameworks such as optimization theory, the Vehicle Routing Problem (VRP), dynamic routing algorithms, cost benefit analysis, as well as environmental models are used to support the investigation. These frameworks provide a structured lens through which the effects of AI implementation in logistics will be examined. Building on this foundation the key results derived from investigative research into real-world AI use cases in logistics have demonstrated that integrating AI into logistics processes can create measurable improvements. AI optimization solutions have shown to reduce transit time and fuel usage, directly translating to financial savings, reduced environmental impacts and customer satisfaction. Real world case examples such as those of UPS and Uber Freight support these findings.
The results of this thesis, highlight the potential of AI in streamlining logistics processes and bolstering sustainability initiatives. The limitation of research using solely secondary data, under-lines the importance for further research using primary data such as interviews, company re-ports and further operational performance metrics to obtain a deeper understanding of AI’s role in supply chain transformation and introduce the concept to industry professionals.
Analytical frameworks such as optimization theory, the Vehicle Routing Problem (VRP), dynamic routing algorithms, cost benefit analysis, as well as environmental models are used to support the investigation. These frameworks provide a structured lens through which the effects of AI implementation in logistics will be examined. Building on this foundation the key results derived from investigative research into real-world AI use cases in logistics have demonstrated that integrating AI into logistics processes can create measurable improvements. AI optimization solutions have shown to reduce transit time and fuel usage, directly translating to financial savings, reduced environmental impacts and customer satisfaction. Real world case examples such as those of UPS and Uber Freight support these findings.
The results of this thesis, highlight the potential of AI in streamlining logistics processes and bolstering sustainability initiatives. The limitation of research using solely secondary data, under-lines the importance for further research using primary data such as interviews, company re-ports and further operational performance metrics to obtain a deeper understanding of AI’s role in supply chain transformation and introduce the concept to industry professionals.