The global health landscape has been persistently challenged by the emergence and re-
emergence of infectious diseases. Traditional epidemiological models, rooted in the early …

The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare,
addressing critical challenges in securing electronic health records (EHRs), ensuring data …

This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics
compartmental model to address the resource allocation challenges of mitigating COVID-19 …

Hospital staff and resources allocation (HSRA) is a critical challenge in healthcare systems,
as it involves balancing the demands of patients, the availability of resources, and the need …

We present an integrated prediction-optimization (PredOpt) framework to efficiently solve
sequential decision-making problems by predicting the values of binary decision variables …

This paper presents strong scenario dominance cuts for effectively solving the multi-stage
stochastic mixed-integer programs (M-SMIPs), specifically focusing on the two most well …

In this study, we present a deep reinforcement learning framework for solving scenario-
based two-stage stochastic programming problems. Stochastic programs have numerous …

Integrating prior epidemiological knowledge embedded within mechanistic models with the
data-mining capabilities of artificial intelligence (AI) offers transformative potential for …

We present a non-anticipative learning-and scenario-based prediction-optimization
(ScenPredOpt) framework that combines deep learning, heuristics, and mathematical …

Developing smart cities is vital for ensuring sustainable development and improving human
well-being. One critical aspect of building smart cities is designing intelligent methods to …