A decision support system for dynamic construction planning and control as a foundation for developing a productivity culture

随着技术和基于计算机的工具的进步，研究人员已经引入了几个系统来衡量项目绩效并指导项目进展的控制。然而，由于规划的缺陷，建设项目仍然会遇到规划失败和生产力损失的问题。这引起了人们对现有控制机制是否有能力向规划者或从业人员发出警报并产生早期预警系统以指导未来行动的关切。这项研究旨在开发和应用基于精益原则和先进度量的现代生产计划决策支持系统，以解决目前部分衡量的几个项目绩效领域。预计开发的计划系统将更好地描述、监控和控制Last Planner系统中用于生产控制的施工计划的各个动态方面。拟议的决策支持系统可以提供问题的主动检测和项目状态的持续可见性，以便规划团队可以应用更早的控制措施来改进可靠的规划并减少里程碑延误。为了支持生产力文化，该项目还将开发生产力数据库跟踪和人工智能(AI)决策支持系统，以提高生产率，并在估算项目成本和建筑项目进度时增加确定性，这些都是项目规划和控制的基本要素。通过该项目开发的生产率数据库跟踪系统将为收集和存储生产率数据、测量和跟踪施工期间的生产率以及预测未来项目的生产率提供一种标准方法。通过该项目开发的人工智能决策支持系统将使用存储在数据库中的数据和专家知识来支持Ledcor Constructors Inc.(简称Ledcor)预测未来项目的生产率，并为正在进行和计划中的项目提出生产率改进策略。

With the advancement of technology and computer-based tools, researchers have introduced several systems to measure project performance and guide control as the project progresses. However, construction projects still encounter planning failures and productivity losses as a result of planning deficiencies. This raises concerns regarding the capability of the current control mechanisms in alarming planners or practitioners and generating an early warning system to guide future actions. This research study aims to develop and apply a modern production planning decision-support system based on lean principles and advanced metrics to address several project performance areas that are currently partially measured. The developed planning system is expected to better describe, monitor, and control the various dynamic aspects of construction planning within the Last Planner System for production control. The proposed decision support system can provide a proactive detection of issues and constant visibility of project status so that planning teams can apply earlier control measures to improve reliable planning and reduce milestone delays. To support a productivity culture, this project will also develop a productivity database tracking and an artificial intelligence (AI) decision support system for improving productivity and increasing certainty when estimating project costs and schedule on construction projects, which are essential elements in project planning and control. The productivity database tracking system developed through this project will provide a standard approach for collecting and storing productivity data, measuring and tracking productivity during construction, and predicting productivity for future projects. The artificial intelligence decision support system developed through this project will use the data and expert knowledge stored in the database to support Ledcor Constructors Inc. ("Ledcor") in predicting productivity for future projects and suggesting productivity improvement strategies for ongoing and planned projects.