Abstract

The increasing demand for global healthcare systems highlights the urgent need for innovative solutions. In response to this challenge, we uses advanced Stochastic Programming and Machine Learning methods to introduce significant improvements in appointment scheduling, operating room planning, and modeling and prediction of the COVID-19 pandemic.

In the first paper, we study the healthcare appointment scheduling problem. The main challenges in appointment scheduling are uncertainties in no-shows, unpunctuality, and service times. We propose a novel stochastic programming model that captures an exponential number of scenarios using a pseudo-polynomial number of variables and constraints without relying on sampling methods. The presented methodology is exact. We show that the generated schedules reduce total costs by 34% on average by incorporating patient-dependent service times, 12% by considering patient-and-time-dependent unpunctuality, and 67% by integrating patient-and-time-dependent no-shows. In addition, we show that personalized reminders have the potential to reduce total costs by 23%.

In the second paper, we study a stochastic operating room planning problem. The unpredictability of surgical durations poses a considerable challenge to efficient OR planning. Existing models often overlook this source of uncertainty. This paper introduces a novel stochastic programming model that effectively manages the uncertainty in surgical times. This model advances the literature by capturing an exponential number of scenarios in a weekly operating room planning problem without sampling, simplifications, or approximations. The results of the computational experiments revealed that our model obtains feasible solutions with an average optimality gap of 0.78% for instances with 80 surgeries and 1.48E+64 scenarios.

In the third, fourth and fifth papers, we focus on modeling and prediction of the COVID-19 pandemic and aim at developing methodologies that inform and guide public health decisions. In these three papers, we proposed a hybrid reinforcement learning based algorithm as well as two other evolutionary computation based algorithms to forecast the spread of the COVID-19 pandemic. By applying these methods to real-world data from Canada, Quebec, Ontario, France and the U.S., we aim to offer insights into effective pandemic response strategies. We predict the pandemic trajectory as well as the number of different cases with high accuracy.