Demand for healthcare is increasing rapidly. To meet demand, we must improve the efficiency of our public health services. We present a mixed integer programming (MIP) formulation that simultaneously tackles the integrated Master Surgical Schedule (MSS) and Surgical Case Assignment (SCA) problems. We consider volatile surgical durations and non-elective arrivals whilst applying a rolling horizon approach to adjust the schedule after cancellations, equipment failure, or new arrivals on the waiting list. A case study of an Australian public hospital with a large surgical department is the basis for the model. The formulation includes significant detail and provides practitioners with a globally implementable model. We produce good feasible solutions in short amounts of computational time with a constructive heuristic and two hyper metaheuristics. Using a rolling horizon schedule increases patient throughput and can help reduce waiting lists.

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Widening participation initiatives in higher education have grown overall student numbers while also increasing the diversity of student cohorts. Consequently, enhancing student experiences and outcomes has become increasingly challenging. This study implemented personalised emails in two first-year mathematics courses as a scalable strategy for supporting students with diverse needs. Impact on student experience and outcomes was assessed through surveying and statistical comparisons to previous cohorts. It was found that students perceived the personalised emails favourably and believed the intervention would contribute to them achieving better grades. This translated to a statistically significant improvement in both student experience and academic performance in one of the courses. The results imply that personalised emails are well-suited to courses taken in students’ first semesters of university study, aiding those transitioning to the higher education environment by fostering feelings of belonging, supporting effective engagement, and easing navigation of university systems and processes.

Analytical techniques are being implemented with increasing frequency to improve the management of surgical departments and to ensure that decisions are well informed. Often these analytical techniques rely on the validity of underlying statistical assumptions, including those around choice of distribution when modelling uncertainty. The aim of the present study was to determine a set of suitable statistical distributions and provide recommendations to assist hospital planning staff, based on three full years of historical data. Statistical analysis was performed to determine the most appropriate distributions and models in a variety of surgical contexts. Data from 2013 to 2015 were collected from the surgical department at a large Australian public hospital. A log-normal distribution approximation of the total duration of surgeries in an operating room is appropriate when considering probability of overtime. Surgical requests can be modelled as a Poisson process with rate dependent on urgency and day of the week. Individual cancellations could be modelled as Bernoulli trials, with the probability of patient-, staff- and resource-based cancellations provided herein. The analysis presented herein can be used to ensure that assumptions surrounding planning and scheduling in the surgical department are valid. Understanding the stochasticity in the surgical department may result in the implementation of more realistic decision models. Many surgical departments rely on crude estimates and general intuition to predict surgical duration, surgical requests (both elective and non-elective) and cancellations. This paper describes how statistical analysis can be performed to validate common assumptions surrounding surgical uncertainty. The paper also provides a set of recommended distributions and associated parameters that can be used to model uncertainty in a large public hospital's surgical department. The insights on surgical uncertainty provided here will prove valuable for administrative staff who want to incorporate uncertainty in their surgical planning and scheduling decisions.

In this paper, we address the multiple operating room (OR) surgical case sequencing problem (SCSP). The objective is to maximise total OR utilisation during standard opening hours. This work uses a case study of a large Australian public hospital with long surgical waiting lists and high levels of non-elective demand. Due to the complexity of the SCSP and the size of the instances considered herein, heuristic techniques are required to solve the problem. We present constructive heuristics based on both a modified block scheduling policy and an open scheduling policy. A number of real-time reactive strategies are presented that can be used to maintain schedule feasibility in the case of disruptions. Results of computational experiments show that this approach maintains schedule feasibility in real-time, whilst increasing operating theatre (OT) utilisation and throughput, and reducing the waiting time of non-elective patients.The framework presented here is applicable to the real-life scheduling of OT departments, and we provide recommendations regarding implementation of the approach.