Hongying Fei

A planning and scheduling problem for an operating theatre using an open scheduling strategy

The objective of this paper is to design a weekly surgery schedule in an operating theatre where time blocks are reserved for surgeons rather than specialities. Both operating rooms and places in the recovery room are assumed to be multifunctional, and the objectives are to maximise the utilisation of the operating rooms, to minimise the overtime cost in the operating theatre, and to minimise the unexpected idle time between surgical cases. This weekly operating theatre planning and scheduling problem is solved in two phases. First, the planning problem is solved to give the date of surgery for each patient, allowing for the availability of operating rooms and surgeons. Then a daily scheduling problem is devised to determine the sequence of operations in each operating room in each day, taking into account the availability of recovery beds. The planning problem is described as a set-partitioning integer-programming model and is solved by a column-generation-based heuristic (CGBH) procedure. The daily scheduling problem, based on the results obtained in the planning phase, is treated as a two-stage hybrid flow-shop problem and solved by a hybrid genetic algorithm (HGA). Our results are compared with several actual surgery schedules in a Belgian university hospital, where time blocks have been assigned to either specific surgeons or specialities several months in advance. According to the comparison results, surgery schedules obtained by the proposed method have less idle time between surgical cases, much higher utilisation of operating rooms and produce less overtime.

An operating theatre planning and scheduling problem in the case of a "block scheduling" strategy

Operating theatre is always the most important and expensive sector of the hospital, and its surgical process management problem is always regarded as the kernel. In this paper, we focus on one of the surgical process management problems: block scheduling problem. An efficient weekly operating program is built for an operating theatre through two phases: at first the operating theatre weekly planning problem is solved with a heuristic procedure based on column generation procedure; then the operating theatre daily scheduling problem, based on the results from the first phase, is solved with a hybrid genetic algorithm. In the end, the proposed problem is tested and validated with randomly generated data, and then the numerical results are provided

ENDOSCOPIES SCHEDULING PROBLEM: A CASE STUDY

The efficient management of an operating theatre involves the problems of planning and scheduling, and this is the same case for endoscopy center. The research aims at building a feasible and efficient operating program within one week for an endoscopy unit composed of two specialized operating rooms, with an objective of both maximizing the utilization of operating rooms and minimizing their overtime cost. At the planning stage, a tactical planning model for one week is built and is solved by a column-generation based heuristic (CGBH) procedure. The solution of the planning stage assigns each operating room to a set of surgical cases on each day. Afterwards, a daily scheduling problem is built at the scheduling stage in order to finally schedule the surgical cases assigned at the planning stage. This daily scheduling model is firstly simplified by a group technology into an open shop model and then solved by the Gonzalez-Sahni algorithm. As a result, a final operating program is ob

Evaluating alternative surgery plans with discrete-event simulation model

Simulation modeling has become a well-established tool to aid healthcare decision makers in evaluating alternative system design in order to improve hospital performances. A simulation model is developed in this study to evaluate an alternative planning strategy for an operating theatre of a medium-size university hospital in Belgium. Real data are used in this model to derive the simulation parameters and are collected from a one-year observation period where a ¿modified block scheduling¿ strategy is currently implemented. This paper is concluded with some suggestions for improving the hospital service efficiency.

Clustering of patient trajectories with an auto-stopped bisecting K-Medoids algorithm

Abstract Nowadays, more and more hospitals seek to employ well-developed process management and simulation tools in the healthcare organizations to develop the overall patient pathways. Since it is extremely important to define suitable patient groups for constructing process or simulation models, we proposed a sequence mining method, an auto-stopped Bisecting K-Medoids clustering algorithm, to classify patients into groups with homogeneous trajectories within two stages. At the first stage, patients are classified according to the complexity of the care process. Afterwards, groups obtained at the first stage are further classified with the similarity of the trajectories. The proposed approach was executed with a real data set from a medium-size hospital. According to the experimental results, this method can be used to classify patients into manageable groups, where the most frequent trajectories might be extracted to validate a process modelling technique. In addition, data extracted from those groups could be used to feed our simulation models.