Analysis of Python Applications in Operations Research Teaching

Abstract

As a cross-disciplinary field focused on optimal decision-making, operations research teaching has long relied heavily on theoretical derivations and specialized commercial software (e.g., Lingo, CPLEX), leading to issues such as disconnection between theory and practice, weak hands-on skills among students, and high learning barriers. In recent years, Python, with its concise syntax, robust open-source ecosystem, and widespread application in big data and artificial intelligence, has emerged as an ideal tool for reforming operations research teaching. This paper systematically analyzes the advantages of Python in operations research teaching and elaborates on its implementation through typical teaching cases, including linear programming, integer programming, network optimization, and simulation. Analysis suggests that Python can transform abstract mathematical models into intuitive and executable code, effectively enhancing students' modeling capabilities, programming skills, and learning motivation. It serves as a key driver in advancing the transition of operations research teaching toward an integrated "theory-modeling-solving-analysis" paradigm. Finally, this paper also discusses the challenges and corresponding strategies of incorporating Python into teaching.