Charting the intellectual evolution of a scientific discipline is crucial for identifying its core contributions, challenges, and future directions. The IISE Annual Conference proceedings offer a rich longitudinal archive of the Industrial and Systems Engineering (ISE) community's development, but the sheer volume of scholarship produced over two decades makes a holistic analysis difficult. Traditional reviews often fail to capture the full scale of thematic shifts and complex collaboration networks that define the community's growth. This paper presents a computational analysis of IISE proceedings from 2002 to 2025, drawing on an initial dataset of 9,350 titles from ProQuest for thematic analysis and 8,958 titles from Google Scholar for citation analysis, to deliver a cartography of the ISE field's intellectual history. Leveraging Large Language Models (LLMs) for domain-aware classification, Natural Language Processing, and Network Science, our study systematically maps thematic evolution to identify dominant, emerging, and receding research topics. We analyze citation data and co-authorship networks to uncover influential papers and authors, providing critical insights into knowledge diffusion and community structure. Through this comprehensive analysis, we establish a baseline for understanding the trajectory of ISE research and offer valuable insights for researchers, practitioners, and educators. The findings illuminate the field's intellectual assets and provide a data-informed map to guide the future of ISE. To foster reproducibility and further research, the curated dataset used in this study and the results will be made publicly available.

翻译：描绘一个科学学科的知识演进历程对于识别其核心贡献、挑战及未来方向至关重要。工业与系统工程学会（IISE）年会论文集为工业与系统工程（ISE）领域的发展提供了丰富的纵向档案，但过去二十年间产生的海量学术成果使得整体分析变得困难。传统的综述方法往往难以全面捕捉定义该领域发展的主题变迁与复杂合作网络。本文对2002年至2025年的IISE会议论文集进行了计算分析，基于ProQuest提供的9,350篇论文标题数据集进行主题分析，并利用Google Scholar的8,958篇标题数据进行引文分析，从而绘制出ISE领域知识发展史的图谱。通过运用大型语言模型（LLMs）进行领域感知分类、自然语言处理及网络科学方法，本研究系统性地描绘了主题演变轨迹，识别出主导性、新兴性及衰退性研究主题。我们通过分析引文数据与合著网络，揭示了具有影响力的论文与作者，为知识传播与学术共同体结构提供了关键见解。通过此项综合分析，我们为理解ISE研究的发展轨迹建立了基准，并为研究者、从业者及教育工作者提供了有价值的参考。研究结果阐明了该领域的知识资产，并提供了一份基于数据的路线图以指引ISE的未来发展。为促进可重复性及后续研究，本研究所使用的整理数据集及相关成果将公开共享。