Background and objective: Predicting the duration of surgical procedures is an important step in scheduling operating rooms. Many factors have been shown to influence the duration of a procedure, in this research we aim to use medical ontological information to improve the predictions. Methods: This paper presents two methods for incorporating the medical information about a surgical procedure into the prediction of the duration of the procedure. The first method uses the Systematised Nomenclature of Medicine Clinical Terms to relate different procedures to each other. The second uses simple text fragments. The relationships between types of procedures are included in a regression model for the procedure duration. These methods are applied to data from New Zealand healthcare facilities and the accuracy of the estimations of the durations is compared. In addition a simulation of scheduling the procedures in an operating room is performed. Results: It is shown that both of the methods provide an improvement in the prediction of procedure durations. When compared to a traditional categorical encoding, the ontological information provides an improvement in the continuous ranked probability scores of the prediction of procedure durations from 18.4 min to 17.1 min, and from 25.3 to 21.3 min for types of procedures that are not performed very often. Conclusions: Different methods for encoding medical ontological information in surgery procedure duration predictions are presented, and show an improvement over traditional models. The improvement in duration prediction is shown to improve the efficiency of scheduling in a simple simulation.

Traditional objective functions for scheduling surgeries, such as maximising utilisation of operating rooms, maximising throughput of surgeries, or minimising cost, can lead to inequitable outcomes for patients in terms of the time waiting for their operation. Using a simple mixed integer program and historical data from a surgical centre we compare two objective functions (a risk neutral and a risk averse objective) intended to reduce the lateness of the latest-performed operations. Further, the effects of other model parameters on the surgical schedules are compared. By applying the model to a case study at a surgical centre, it is demonstrated that, with the appropriate values of the other model parameters, the risk neutral objective can achieve similar schedules to the risk averse objective, and results in problems that are easier to solve.

Simulation facilitates the understanding and improvement of complex systems. Conceptual modelling is a key step in simulation studies. It has gained recognition because it may both increase engagement with stakeholders and decrease the time to implement a simulation. This scoping review’s objective is to highlight approaches and platforms for electronically representing models from 1999 to 2020. The motivation is that electronic representations facilitate the sharing of conceptual models. The contribution from the review to the research of conceptual modelling and simulation is to show that conceptual models are electronically represented by broadly speaking either General-Purpose or Domain-Specific Modelling Languages. There is a slight trend towards the latter in order to better deal with application specificities and improve unambiguity in model representations, though. Thus, we identify modelling approaches, platforms, and features for electronically representing conceptual models with the potential to fill the gap between conceptual models and their corresponding simulation implementations.

In this paper, we present a new model averaging technique that can be applied in medical research. The dataset is first partitioned by the values of its categorical explanatory variables. Then for each partition, a model average is determined by minimising some form of squared errors, which could be the leave-one-out cross-validation errors. From our asymptotic optimality study and the results of simulations, we demonstrate under several high-level assumptions and modelling conditions that this model averaging procedure may outperform jackknife model averaging, which is a well-established technique. We also present an example where a cross-validation procedure does not work (that is, a zero-valued cross-validation error is obtained) when determining the weights for model averaging.

Cloud service providers use load balancing algorithms in order to avoid Service Level Agreement Violations (SLAVs) and wasted energy consumption due to host over- and under-utilization, respectively. Load balancing algorithms migrate VMs between hosts in order to balance host loads. Any Virtual Machines (VMs) that are migrated experience performance degradation which results in lower Quality of Service (QoS) and can possibly result in SLAVs. Hence, an optimal load balancing method should reduce the number of over- and under-utilized hosts with a minimal number of VM migrations. One of the metrics used previously in the literature for evaluating load balancing stated that it equally considered SLAVs caused by both over-utilized hosts and migrations. However, in this paper, we show that, in fact, this metric values keeping the number of migrations low at the expense of an increased number of over-utilized hosts. This disparity is demonstrated by simulation of Google, PlanetLab and Azure data sets in CloudSim. This metric may suit public cloud providers which are focused on minimizing SLAVs and keeping energy costs low, but does not consider the QoS of customer VMs. We propose an alternative metric that considers QoS for the VMs. This alternative metric considers not only performance loss during migration, but also performance degradation due to host over-utilization. Private cloud providers, e.g., IT services within large organizations, often value the performance of their “customer” VMs, i.e., the QoS their organization receives, as well as traditional cloud provider costs, i.e., energy and SLAV costs. Hence, our alternative metric would be more appropriate in these scenarios. We compare and contrast load balancing methods using both the existing, biased metric and our new alternative metric.