Comparative Analysis of Greedy, Gale-Shapley, and Score-Based Methods for Optimal Project Allocation

The project allocation problem is essential in various fields, including academic and organizational settings, where efficient, stable, and satisfying assignments are crucial for maximizing engagement and productivity. This paper presents a comparative analysis of three distinct algorithms for project allocation: the Greedy Algorithm, the Stable Matching Algorithm (Gale-Shapley), and a Score-Based Allocation Method. The Greedy Algorithm prioritizes computational efficiency, while the Gale-Shapley algorithm focuses on stability, and the Score-Based AllocationMethod allows flexibility in balancing multiple criteria. Our findings reveal that while the Stable Matching Algorithm ensures stable matches, the Score-Based Method optimizes for overall satisfaction based on different objectives, offering valuable insights into the trade-offs involved in choosing each approach.