Optimization of Project Scheduling Using Graph Theory

Abstract

This thesis investigates graph theory to improve project scheduling. CPM and PERT don't always function effectively in complex and changing project environments. The research uses graph-theoretical methods, particularly spectral analysis and graph energy, to improve scheduling accuracy and efficiency. These antiquated methods have many issues.

A mixed-method approach employing MATLAB and literature review. We made 2 case studies. Excel and Gantt charts were utilized for planning, whereas Petersen graphs and DAG structures were used for graph theory. We pretended to "Open a Retail Shop" to test the two plans.

Graph theory helps us comprehend how activities are connected, uncover important connections independently, and evaluate network complexity using graph energy. Regular approaches would have taken 22 days, whereas the graph-based method predicted 12 days. It also adjusted well.

Scientists found that graph theory improves project planning, especially for large or changing projects. Data-driven, quantitative strategies can help project managers and planners improve initiatives.