Clear Skies Ahead: Optimizing Operations Through Large Language Models and AI to Reduce Emissions and Costs for a Regional NOC

Abstract

This manuscript presents an industrial case study and analysis leveraging Artificial Intelligence (AI), Large Language Models (LLMs) and advanced analytics to optimize offshore operations for a regional NOC while reducing the emission footprint and costs. The scope of this study also included a detailed analysis of potential challenges and benefits of using LLMs. Along with industrial data, this case study includes a comprehensive literature review on helicopter transportation, safety, and environmental impact, as well as explores strategies to improve overall operations, and to reduce GHG emissions. In conjunction with analysis of relevant data sources, data on GHG emissions from helicopter transportation were also collected and analyzed. The potential benefits of schedule optimization were evaluated, including leveraging the capabilities of LLMs for reductions in manpower, flight time, fuel consumption, and GHG emissions. Various optimization algorithms for schedule were also reviewed and compared. Results from the study indicate that implementation of the presented strategies including LLM models not only improve productivity & safety, but also reduce emissions and fuel consumption resulting in cost savings for helicopter operators. For instance, LLMs assisted in making bookings and querying schedules within minimal intervention resulting in cost savings due to reduced reliance on human labour; increased efficiency through automation; improved accuracy through elimination of manual data entry and automated data validation; coupled with enhanced data analysis to provide valuable insights for real-time decision making. Further reductions were also achieved through modifying the helicopter schedule to decrease ground idle time, enhancing flight routing, and optimizing the speed and altitude of the helicopter. The industrial case study indicates that these strategies could potentially reduce CO2 emissions by up to 18% per flight while reducing the overall cost by 24%. The conclusion drawn from the analysis is that such optimizations are a promising approach to reduction in costs and emissions with increased efficiency and accuracy. This research offers novel insights into the potential application of multi-layered AI and LLMs to optimize helicopter operations without compromising on sustainable practices. This study offers valuable information for the aviation industry looking to enhance operations sustainably through a comprehensive evaluation of the environmental impact of practices in place and examining the efficacy of optimization measures. The study's conclusions have relevance for anyone working in the aviation sector since they show that adopting sustainable techniques to lessen their influence on the environment is both feasible and beneficial. By highlighting the potential of multi-layered AI and LLMs to optimize operations including offshore transportation, this paper offers a valuable contribution to the ongoing effort to improve current practices and sustainability through digital technologies.