Particle swarm optimization-based planning and scheduling for a laminar-flow operating room with downstream resources

This paper focuses on finding a satisfactory surgery scheduling to patients and efficiently managing scarce medical resources in laminar-flow operating theaters, which have distinct flow process and characteristics relative to general operating theaters and are widely used in China. The problem is solved in two phases. The first phase involves determining whether patients can be operated upon within the planning period and, if so, determining their surgery date with the objective of maximizing overall patient satisfaction. In the second phase, the surgery schedule consists of the surgery sequence and the corresponding operating room; with regard to the post-anesthesia care unit, a downstream resource, the daily scheduling problem is modeled as a two-stage no-wait hybrid flow-shop problem with the objective of minimizing the hospital’s operating costs, which includes the fixed costs of opening operating rooms, overtime costs and recovery costs. A discrete particle swarm optimization algorithm combined with heuristic rules is proposed. The results of experiments of our approach with realistic data show that our algorithm produced results of nearly the same quality as CPLEX but with a much less computation time. This approach can find the optimal number of daily functional operating rooms and recovery beds by varying parameters in the experiment, which gives management insights into reducing the operating costs of the hospital.