Using Discrete Event Simulation (DES) to manage theatre operations in healthcare: An audit-based case study

This paper discusses the application of Discrete Event Simulation (DES) in modelling the complex relationship between patient types, case-mix and operating theatre allocation in a large National Health Service (NHS) Trust in London. The simulation model that was constructed described the main features of nine theatres, focusing on operational processes and patient throughput times. The model was used to test three scenarios of case-mix and to demonstrate the potential of using simulation modelling as a cost effective method for understanding the issues of healthcare operations management and the role of simulation techniques in problem solving. The results indicated that removing all day cases will reduce patient throughput by 23.3% and the utilization of the orthopaedic theatre in particular by 6.5%. This represents a case example of how DES can be used by healthcare managers to inform decision making. © 2008 IEEE.

Application of a discrete event simulation for robust system design of a melt facility

This paper presents a methodology to identify robust operating regions through the selection of controllable factory variables, using discrete event simulation. A casting plant melt facility was used as an industrial test bed to develop these techniques. A robust system design was determined by response surface analysis of key production parameters. Furthermore, robust operating policies that maximise throughput, while minimizing work-in-progress and thus energy consumption were identified.

Linking discrete event simulation models using HLA

The increasing usage of discrete event simulation packages for modeling and analyzing manufacturing and logistics has led to a need for connecting simulation models together at runtime. One such methodology for linking discrete event simulation models together has been developed for this research and this paper demonstrates the usage of this linking method. A unified simulation model is developed from two submodels developed using different simulation packages.

Optimising discrete event simulation models using a reinforcement learning agent

A reinforcement learning agent has been developed to determine optimal operating policies in a multi-part serial line. The agent interacts with a discrete event simulation model of a stochastic production facility. This study identifies issues important to the simulation developer who wishes to optimise a complex simulation or develop a robust operating policy. Critical parameters pertinent to 'tuning' an agent quickly and enabling it to rapidly learn the system were investigated.