Minimizing bed occupancy variance by scheduling patients under uncertainty

Abstract

In this paper we consider the problem of scheduling patients in allocated surgery blocks in a Master Surgical Schedule. We pay attention to both the available surgery blocks and the bed occupancy in the hospital wards. More specifically, large probabilities of overtime in each surgery block are undesirable and costly, while large fluctuations in the number of used beds requires extra buffer capacity and makes the staff planning more challenging. The stochastic nature of surgery durations and length of stay on a ward hinders the use of classical techniques. Transforming the stochastic problem into a deterministic problem does not result into practically feasible solutions. In this paper we develop a technique to solve the stochastic scheduling problem, whose primary objective it to minimize variation in the necessary bed capacity, while maximizing the number of patients operated, and minimizing the maximum waiting time, and guaranteeing a small probability of overtime in surgery blocks. The method starts with solving an Integer Linear Programming (ILP) formulation of the problem, and then simulation and local search techniques are applied to guarantee small probabilities of overtime and to improve upon the ILP solution. Numerical experiments applied to a Dutch hospital show promising results.

Last-minute cancellation of adult patients scheduled for cardiothoracic surgery in a large Dutch tertiary care centre

“…The high frequency of process-related cancellations emphasizes the need for an improved scheduling method using patient and treatment characteristics. Innovations in scheduling may help predict the expected duration of treatment in the operating room and length of stay in downstream departments like the ICU and the ward [ 15 ], resulting in a reduction of capacity problems due to lack of ICU beds or overrun of a preceding surgery.…”

Operating room planning and scheduling for outpatients and inpatients: A review and future research

“…Lognormal [6,20,41,63,68,69,74,88,100,101,112,113,117,119,122,132,133,139,147,148,167,169,173,175,176,179,182,183,188,192,201,203,204,[206][207][208][209] Poisson [2,13,36,52,53,63,74,80,88,101,103,109,118,121,130,…”

An Improved Saturation Degree-Based Constructive Heuristic for Master Surgery Scheduling Problem: Case Study

“…𝑆𝐶1, described in (2), rewards a unit each time it is assigned to its preferred slots (preferred OT or preferred days). Meanwhile, 𝑆𝐶2, expressed in (3), penalizes the objective value for each unit assigned to a day exceeding the available quantity of movable equipment [14,80]. 𝑆𝐶3 presented in (4) rewards the objective value for not using all quantities of equipment on each day [14,80].…”