Potential applications of simulation modelling techniques in healthcare: lessons learned from aerospace and military

The Aerospace and Military areas are to do with complex missions and situations. Modelling and Simulation (M&S) has been applied in many areas of defence ranging from space sciences, satellite engineering to multi-warfare (air warfare, undersea warfare), air & missile defence, acquisition, tactical military trainings & exercises, national security analysis and strategic decision making & planning, etc. The application of simulation modelling techniques in healthcare would improve the provision of healthcare services; however, their application has been much relatively feeble in the healthcare sector as compared to the defence sector. This paper presents results from a systematic literature survey on applications of modelling simulation techniques in the Aerospace & Military. The knowledge gained or lessons learned from the survey were finally used to analyze the potential applications of the simulation modelling techniques to the healthcare sector. Results show that in the defence sector, Distributed Simulation has now become a widely adopted technique. However, System Dynamics (SD) and Discrete Event Simulation (DSE) have also gained relative attention. From this survey it becomes clear that various simulation modelling techniques are useful for specific purposes and have potential applications in the healthcare sector

Enhancing Care Transitions for Older People through Interprofessional Simulation: A Mixed Method Evaluation

Introduction: The educational needs of the health and social care workforce for delivering effective integrated care are important. This paper reports on the development, pilot and evaluation of an interprofessional simulation course, which aimed to support integrated care models for care transitions for older people from hospital to home. Theory and methods: The course development was informed by a literature review and a scoping exercise with the health and social care workforce. The course ran six times and was attended by health and social care professionals from hospital and community (n=49). The evaluation aimed to elicit staff perceptions of their learning about care transfers of older people and to explore application of learning into practice and perceived outcomes. The study used a sequential mixed method design with questionnaires completed pre (n=44) and post (n=47) course and interviews (n=9) 2-5 months later. Results:Participants evaluated interprofessional simulation as a successful strategy. Post-course, participants identified learning points and at the interviews, similar themes with examples of application in practice were: Understanding individual needs and empathy; Communicating with patients and families; Interprofessional working; Working across settings to achieve effective care transitions. Conclusions and discussion:An interprofessional simulation course successfully brought together health and social care professionals across settings to develop integrated care skills and improve care transitions for older people with complex needs from hospital to home

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Information support and interactive planning in the digital factory : approach and industry-driven evaluation

In the modern world we are continuously surrounded by information. The human brain has to analyse and interpret this information to transform into useable knowledge that is then used in decision making activities. The advent and implementation of Industry 4.0 will make it a requirement for systems within factories to interact and share large quantities of information with each other. This large volume of information will make it even more difficult for the human resources within the factory to sift through the large amount of information required since there is a limit to the information that our brains can cope with. Just in time information retrieval (JITIR) within the digital factory environment aims to provide support to the human stakeholders in the system by proactively yet non-intrusively providing the required information at the right time based on the users context. This paper will therefore provide an insight into the cognitive difficulties experienced by humans in the digital factory and how JITIR can tackle these challenges. By validating the JITIR concept, several industry scenarios have been evaluated: an exemplary model, concerning the machine tool industry, is presented in the paper. The results of this research are a set of guidelines for the development of a digital factory support tool.peer-reviewe

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities

Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, eps-MOEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications.Comment: 16 page