A Continuous-Class Queueing Model With Proportional Hazards-Based Routing

Master, Neal; Reiman, Marty I.; Wang, Can; Wein, Lawrence M.

doi:10.2139/ssrn.3390476

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…Zhang, Shi, and Ward (2022) employ a theoretical queueing model to explore fairness and efficiency in routing customers in a two-stage service system motivated by incarceration diversion. Master et al (2018) analyze a jail network queueing model with a continuum of classes capturing recidivism risk, and propose a two-threshold control policy to optimize crime rate and mean jail population. However, none of these studies have investigated the impact of admission volume on program census using an ML-based outcome prediction model, or explored the trade-off between in-program revocation and long-term recidivism rate to determine optimal program size.…”

Section: Background and Related Workmentioning

confidence: 99%

Combining Machine Learning and Queueing Theory for Data-Driven Incarceration-Diversion Program Management

Li,

Castellanos,

Shi

et al. 2024

AAAI

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Incarceration-diversion programs have proven effective in reducing recidivism. Accurate prediction of the number of individuals with different characteristics in the program and their program outcomes based on given eligibility criteria is crucial for successful implementation, because this prediction serves as the foundation for determining the appropriate program size and the consequent staffing requirements. However, this task poses challenges due to the complexities arising from varied outcomes and lengths-of-stay for the diverse individuals in incarceration-diversion programs. In collaboration with an Illinois government agency, we develop a framework to address these issues. Our framework combines ML and queueing model simulation, providing accurate predictions for the program census and interpretable insights into program dynamics and the impact of different decisions in counterfactual scenarios. Additionally, we deploy a user-friendly web app beta-version that allows program managers to visualize census data by counties and race groups. We showcase two decision support use cases: Changing program admission criteria and launching similar programs in new counties.

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Section: Background and Related Workmentioning

confidence: 99%