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，但二十年间产生的庞大学术成果使得整体分析变得困难。传统综述方法往往难以捕捉定义该领域发展的全部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，利用来自ProQuest的9,350条标题初始数据集进行主题分析，以及来自Google Scholar的8,958条标题进行引文分析，以绘制ISE领域知识史的图谱。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，本研究系统性地描绘了主题演变，识别了主导性、新兴及衰退的研究主题。我们分析了引文数据与合著网络，以揭示有影响力的论文和作者，为知识传播与社群结构提供关键见解。通过此项综合分析，我们为理解ISE研究的轨迹建立了基准，并为研究人员、从业者和教育者提供了宝贵的洞见。研究结果阐明了该领域的知识资产，并提供了一张基于数据的图谱以指引ISE的未来发展。为促进可重复性及进一步研究，本研究中使用的整理数据集及结果将公开提供。