Dynamic Project Allocation Approach For Ani’s Incentive System

This paper presents a decision support tool for the dynamic allocation of projects to human resources under the Portuguese Innovation Agency’s (ANI) Incentive System. The approach emphasizes workload balancing and introduces a constructive heuristic, implemented in Python and tested across multiple operational scenarios. The heuristic assigns resources dynamically as projects are completed and new ones arise, reflecting the real-time decision-making needs of large-scale incentive management. The performance of this method was compared against mathematical models based on Mixed-Integer Linear Programming, enabling a comprehensive evaluation of efficiency and solution quality. Performance was assessed using multiple metrics, including workload amplitude, maximum and minimum values, average distribution, and solver execution time. Results show that the heuristic achieves comparable quality with significantly lower computational cost, making it well-suited for operational contexts where fast responses are required. Overall, the simplicity, efficiency, and ease of implementation of the developed heuristic demonstrate its potential as a robust and scalable tool to support balanced workload allocation under dynamic conditions.