OPTIMIZING SOFTWARE PROJECT SCHEDULING THROUGH FUZZY LOGIC IN SDLC

Abstract

Effective project scheduling is a critical component of the Software Development Life Cycle (SDLC) to ensure timely delivery, optimal resource utilization, and quality outcomes. Traditional scheduling techniques, such as PERT and CPM, often struggle with handling uncertainties inherent in project management, such as task duration variability, resource availability, and changing priorities. This paper proposes the integration of fuzzy logic as a robust solution to address these challenges. By modelling uncertainties using fuzzy variables and applying fuzzy inference systems, project managers can make more informed and adaptive scheduling decisions. The proposed fuzzy logic-based scheduling framework defines inputs such as task complexity, team skill levels, and priority, and generates optimal schedules that balance efficiency and flexibility. Results indicate significant improvements in schedule accuracy, adaptability, and resource optimization. This study highlights the potential of fuzzy logic to revolutionize project scheduling in SDLC, paving the way for more resilient and intelligent project management methodologies.