Articles

The swift changes in the complexity of the project, scarcity of resources, and pressure on the organization have demonstrated the necessity of more intelligent and data-driven planning and scheduling methods. Conventional methods tend to be less effective in the tasks of uncertainty management, dynamic environment, and dynamism of the project ecosystems of the modern world. The application of machine learning (ML) in the planning and scheduling of projects is thoroughly reviewed in this paper, and it is evident that ML may help to improve the accuracy of forecasts, optimize resource distribution, and make decisions. This paper offers an analysis of the basic concepts of project management, evidence-based practices, and how supervised and unsupervised learning, as well as reinforcement learning concepts, can be combined in the scheduling process. Furthermore, the new literatures are examined to bring out new techniques, including predictive modelling, optimization algorithms, simulation-based planning and adaptive scheduling models. The review determines the main strengths, weaknesses, and gaps in the research that impact the adoption of ML-driven approaches. All in all, the paper describes the ways in which machine learning technologies can change the traditional way of project management by creating efficient, resilient, and proactive planning systems.