Assigning inmates to correctional facilities is a complex challenge that requires balancing security, capacity, and individual risk, while promoting fairness and rehabilitation. Traditional manual methods are slow, inconsistent, and often perceived as unfair. Our research introduces an innovative Decision Support System (DSS) that automates inmate placement and integrates predictive analytics to enhance operational efficiency and ethical decision-making.

The system combines the Stable Marriage algorithm with rule-based procedures, ensuring fair, stable, and predictable allocations. By considering geographic proximity, it prioritizes placing inmates in facilities close to their families, reducing logistical costs and supporting social reintegration. Deterministic rules provide consistent, auditable decisions even in environments with limited historical data, while generating a reliable dataset for future predictive modeling.

Automating Inmate Assignment through Stable Matching and Predictive Analytics (PID070)

Speakers

João Domingues

Muhammad Luqman Jamil