In construction, master schedules and look-ahead plans are created at different times (monthly vs. weekly), by different personas (planner vs. superintendent), with different software (scheduling solution vs. spreadsheet), and at different levels of granularity (milestones vs. production details). Their full-alignment is essential for project coordination, progress updating, and payment application reviews, and its absence may lead to costly litigation. This paper presents the first attempt to automate linking look-ahead planning tasks to master-schedule activities following an NLP-based multi-stage ranking formulation. Our model employs distance-based matching for candidate generation and a Transformer architecture for final matching.¹ Validation results from real-world projects demonstrate that the method helps planners match look-ahead planning tasks to master schedule activities by presenting a list of top-five matches with a precision of 76.5%. We also show that the method helps superintendents create look-ahead plans from a master schedule by generating lists of tasks based on activity descriptions.

在施工中，主进度计划和前瞻性计划是在不同时间（每月与每周）、由不同角色（计划员与主管）、使用不同软件（调度解决方案与电子表格）以及不同粒度级别创建的（里程碑与生产细节）。它们的完全一致对于项目协调、进度更新和支付应用程序审查至关重要，如果没有它，可能会导致代价高昂的诉讼。本文首次尝试在基于 NLP 的多阶段排名公式之后，自动将前瞻规划任务与主计划活动联系起来。我们的模型采用基于距离的匹配进行候选生成，并采用 Transformer 架构进行最终匹配。¹来自现实世界项目的验证结果表明，该方法通过以 76.5% 的精度呈现前五名匹配列表，帮助计划人员将前瞻性计划任务与主进度活动进行匹配。我们还表明，该方法通过根据活动描述生成任务列表，帮助管理者从主计划中创建前瞻性计划。