Cheng Cheng has had her PhD thesis approved at the University of Melbourne. Her thesis is titled “Optimisation of Disaster Waste Management Systems” and proposes models and algorithms for planning waste removal after disasters. Among other results, Cheng analysed the effect of using intermediate waste processing centers in the efficiency of the operations.
Congratulations to Cheng!
Beatriz Brito Oliveira has defended her PhD thesis at the University of Porto. Her thesis is titled “Fleet and revenue management in car rental: quantitative approaches for optimization under uncertainty” and was supervised by Prof. Maria Antònia Carravilla and Prof. José Fernando Oliveira.
Beatriz spent three months with our research group in Melbourne. During this time she worked in the last chapter of her thesis, finalising the development of a matheuristic for the stochastic version of the car rental planning problem she tackled.
Congratulations to Beatriz, Maria Antònia and José Fernando for this strong piece of research!
Our paper A Sequential Stochastic Mixed Integer Programming Model For Tactical Master Surgery Scheduling, by Ashwani Kumar, Mark Fackrell, Peter Taylor and Alysson M. Costa is now available online.
In this paper, we develop a stochastic mixed integer programming model to optimise the tactical master surgery schedule (MSS) in order to achieve a better patient flow under downstream capacity constraints. We optimise the process over several scheduling periods and we use various sequences of randomly generated patients’ length of stay scenario realisations to model the uncertainty in the process. This model has the particularity that the scenarios are chronologically sequential, not parallel. We use a very simple approach to enhance the non-anticipative feature of the model, and we empirically demonstrate that our approach is useful in achieving the desired objective. We use simulation to show that the most frequently optimal schedule is the best schedule for implementation. Furthermore, we analyse the effect of varying the penalty factor, an input parameter that decides the trade-off between the number of cancellations and occupancy level, on the patient flow process. Finally, we develop a robust MSS to maximise the utilisation level while keeping the number of cancellations within acceptable limits.
Returning in 2018, AMSI Optimise is an annual networking and research-training event that aims to strengthen mathematical optimisation research engagement and its applications across industry.
This event will comprise a three-day industry-focused conference, followed by a two-day research workshop. The symposium features expert and end-user talks, international guest speakers, collaboration showcases, industry challenge sessions and tutorials. The themes of the 2018 conference are Decision Making Under Uncertainty and Humanitarian Applications.
AMSI Optimise is aimed at:
- anyone using optimisation, with opportunities to learn more about the current state of the art and to connect with others who have similar interests
- industry practitioners interested in exploring the benefits of engagement with optimisation research
- academics and postgraduate students wanting to better understand drivers and needs in this area
The event provides a forum to bring together people from the diverse companies and research disciplines that use and develop optimisation models and software. It provides a platform for research training and developing new collaborations in this vital area through PhD internships, research partnerships and postgraduate research projects.