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

随着技术和计算机工具的进步，研究人员已经引入了一些系统来衡量项目绩效并指导项目进展的控制。然而，由于规划的不足，建设项目仍然会出现规划失败和生产力损失的情况。这引起了人们对现有控制机制在提醒规划人员或从业人员和建立早期预警系统以指导今后行动方面的能力的关注。本研究旨在开发和应用基于精益原则和先进指标的现代生产计划决策支持系统，以解决目前部分测量的几个项目绩效领域。开发的计划系统有望更好地描述、监测和控制生产控制的最后计划系统内施工计划的各个动态方面。建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，以便计划团队可以应用更早的控制措施来改进可靠的计划并减少里程碑延迟。

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.