Synergizing Global and Local Strategies for Dynamic Project Management: An Advanced Machine Learning-Enhanced Framework

Abstract

In this study, we introduce a versatile and scalable optimization tool designed to address several critical project management needs. Our aim is to provide project managers with a robust decision support system that enhances and streamlines decision-making processes. Building upon our previously proposed global scheme—which optimizes project schedules by adjusting dates to match each task’s optimal period—we introduce a novel local scheme. This innovative addition leverages a Machine Learning pipeline, specifically utilizing the Silverkite algorithm, to facilitate long-horizon forecasting. By synergistically combining global and local optimization strategies, we elevate project management efficiency, maximizing potential benefits. This tool is equipped to handle a wide array of variables, offering real-time, consultative support throughout the project’s lifecycle. Through the demonstration of various scenarios, we showcase the effectiveness and adaptability of our optimization tool, underscoring its value in contemporary project management contexts.

Keywords

• Artificial intelligence in project management
• decision support systems
• resource allocation optimization
• dynamic project management