Hospital Bed Planning in a Single Department Based on Monte Carlo Simulation and Queuing Theory This paper investigates hospital bed planning problem in a single department, proposing a method combining Monte Carlo simulation and queuing theory to determine bed scale and allocation. Firstly, we establish a multitype patient mixed queuing system and simulate it via Monte Carlo simulation, obtaining major performances of the queue. Then, bed scale is determined based on average waiting time of patients as well as bed utilization constraint. Furthermore, a mathematical model is constructed to minimize the total waiting time of all patients, thereby solving the bed allocation problem. Finally, the model is validated on MATLAB using ophthalmology department as an example. This study shows that Monte Carlo simulation is an efficient method for determining and controlling the scale of hospital beds when considering complexities, such as different arrival rates of patients and length of hospital stay distributions. This work provides hospital managers with an effective approach for hospital bed planning.

A Benders Decomposition Approach for Appointment Scheduling of Unpunctual Patients in a MultiServer Setting Appointment patients usually arrive unpunctually, which significantly affects the daily operations of healthcare facilities. To mitigate the negative impact of unpunctuality, this paper attempts to optimize the appointment schedule by considering unpunctual arrivals and noshows in a multiserver setting. A twostage stochastic programming model is formulated with the objective of minimizing patient waiting cost and overtime cost. The appointment schedule is determined by a modified Benders decomposition with sample average approximation. The effectiveness of this algorithm is validated through the comparison with lower bounds. Extensive experiments show that the optimal appointment schedule exhibits a zigzag pattern rather than a dome pattern. The impacts of various factors are also explored by numerical experiments.