Clustering surgical procedures for master surgical scheduling

The sound management of operating rooms is a very important task in each hospital. To use this crucial resource efficiently, cyclic master surgery schedules are often developed. To derive sensible schedules, high-quality input data are necessary. In this paper, we focus on the (elective) surgical procedures stochastic durations to determine reasonable, cyclically scheduled surgical clusters. Therefore, we adapt the approach of van Oostrum et al (2008), which was specifically designed for clustering surgical procedures for master surgical scheduling, and present a two-stage solution approach that consists of a new construction heuristic and an improvement heuristic. We conducted a numerical study based on real-world data from a German hospital. The results reveal clusters with considerably reduced variability compared to those of van Oostrum et al(2008)

Decision support for rehabilitation hospital scheduling

We present a detailed analysis of the patient and resource scheduling problem in rehabilitation hospitals. In practice, the predominantly therapeutical treatments and activities which are prescribed for the patients are typically scheduled manually. This leads to rigid and inefficient schedules which can have negative effects on the quality of care and the patients' satisfaction. We outline the conceptual framework of a decision support system for the scheduling process that is based on formal optimization models. To this end, we first develop a large-scale monolithic optimization model. Then we derive a numerically tractable hierarchical model system in order to deal with problem instances of realistic sizes. We report numerical results with respect to solution times, model sizes and solution quality.rehabilitation hospital scheduling, decision support, decomposition, mathematical programming

Using linear programming to analyze and optimize stochastic flow lines

This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines.Flow lines, random processing times, performance evaluation, buffer allocation, linear programming, simulation.

Analyzing the relationship between physicians' experience and surgery duration

To construct good quality plans or planning systems in hospitals, such as capacity planning, case mix planning, master surgery scheduling, and shift scheduling, knowing details about the duration of surgeries is paramount. Furthermore, the operating room is one of a hospital’s main cost drivers, thus making surgery duration a key to achieving cost effectiveness. To gain a better understanding of the interdependencies of determining surgery durations, we investigate the influence physicians have on the duration of a surgery. Since physician experience is a very generalizable factor across a heterogeneous group of hospitals, it is the most obvious influencing factor to analyze. Accordingly, we utilize information regarding a physician’s level of experience and examine its impact on surgery durations using data from a German hospital. Although we are forced to use aggregate data for privacy and labor law reasons, a combination of linear and quantile regression analysis allows us to derive several important insights. First, on average, an increase in a physician’s experience leads to a decrease in the duration of a surgery. Second, the effect of the first insight depends on the composition of the surgical team and diminishes in the case of teaching activities. Third, the relationship between experience level and surgery duration varies across the distribution of durations, i.e., the relationship is strongest for short surgeries and weakens as the duration of a surgery increases

Using linear programming to analyze and optimize stochastic flow lines

This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines