1545 4. CONCLUSION This paper presents an adaptive machine learning–based Automatic Operator Guidance System (AOGS) designed to optimize haul truck productivity in open-pit coal mining operations. The study integrates real-time telematics, GPS, and operational data to model the relationship between operator behavior, road conditions, and haulage performance. A physics-informed adaptive learning framework is developed to generate optimal speed recommendations and predict cycle times under dynamic operational conditions. The system incorporates a rule-based guidance engine that translates model outputs into realtime, actionable feedback for operators, improving driving consistency and safety. Field validation demonstrates significant operational benefits, including reduced fuel consumption, decreased braking events, and improved cycle-time stability. The implementation resulted in an overall productivity increase of approximately 6%, highlighting the effectiveness of personalized, data-driven operator guidance. The findings confirm that adaptive machine learning integrated with fleet management systems provides a scalable and practical solution for enhancing efficiency, safety, and decision-making in large-scale mining operations. REFERENCES [1] Najor J and Hagan P 2004 Mine Production Scheduling within Capacity Constraints (Sydney: The University of New South Wales) [2] Komatsu 2009 Specifications and Application Handbook (Japan: Komatsu Ltd) [3] Chaowasakoo P et al 2017 Digitalization of mine operations: Scenarios to benefit in real-time truck dispatching International Journal of Mining Science and Technology 27 229–236 [4] Smith, J., “Machine Learning in Mining Operations,” Mining Engineering Journal, 2022. [5] Stevenson W J and Ozgur C 2007 Chapter 13. Waiting-line models. Introduction to management science with spreadsheets. (London: McGraw-Hill Irwin) [6] Gross D and Harris C M 1998 Fundamentals of Queueing Theory (New York: John Wiley & Sons, Inc) [7] Giffin W C 1978 Queueing: Basic Theory and Applications (Columbus, Ohio: Grid, Inc) [8] Caterpillar, Haul Road Design Guidelines, CAT Global Mining, 2019 [9] Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2006.
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