This paper firstly analyzes the limitations of current operations research teaching, including over-reliance on mathematical derivation, disconnection between cases and practical problems, and weak practical ability of students. On this basis, it is proposed to integrate AI optimization methods such as machine learning, genetic algorithm and deep reinforcement learning into the curriculum system, and improve students’ optimization decision-making ability and innovative thinking through intelligent computing tools, dynamic case generation and adaptive learning platforms. In the teaching practice part, two typical cases are designed: (1) Intelligent logistics path optimization, combining traditional operations research model and AI algorithm to solve the real distribution problem; (2) Deep learning-driven supply chain forecasting, which cultivates students’ interdisciplinary application ability through project-based learning. In addition, the research proposes an innovative path combining heuristic teaching and AI technology, emphasizing problem guidance and algorithm correlation to help students systematically master knowledge. The research results show that the introduction of AI technology significantly improves the practicality and cutting-edge of operations research teaching. On the one hand, the intelligent algorithm simplifies the complex calculation process, making students more focused on model construction and decision analysis; On the other hand, dynamic cases and personalized learning enhance student engagement and problem-solving skills. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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