The Future of Project Scheduling: Leveraging Machine Learning for Precision Planning

Abstract

Scheduling a project is a core element of project management, directly influencing efficiency in terms of time, cost, and
resources. Conventional scheduling methods, such as the Critical Path Method (CPM) and Program Evaluation and Review
Technique (PERT), offer a procedural approach. However, they are inflexible in the face of changes and uncertainties that
arise in real-time project conditions. When more data about the projects is available and computational capabilities have
matured, a new method of enhanced forecast accuracy and intelligently scheduled projects was presented in the form
of machine learning (ML). The following paper discusses how ML techniques can be used to enhance the precision and
timeliness of project schedules, specifically through regressions, classifications, time-series analysis, and reinforcement.
This paper reviews modern scholarly literature and actual practice in the fields of construction, software development, and
infrastructure management to demonstrate how ML can provide better, more effective results in predictive modeling of
delays, resource adjustment, and risk management compared to traditional methods. It is suggested to introduce a modular
approach to incorporating ML into the existing processes of project management that would provide an opportunity to
make decisions based on data and strike the right balance between humans and technology. The paper also addresses
crucial issues, including the quality of data, model explainability, integration with other systems, and ethics. The outcomes
confirm the premise that ML can transform project scheduling, making it smarter and more proactive. The next steps in
research will involve creating explainable AI, real-time scheduling software, and area-specific transfer learning frameworks
to enhance scale and credibility further.