Simulation Optimization for Healthcare Emergency Departments

AbstractThis article presents an Agent-Based modeling (ABM) simulation to design a decision support system (DSS) for Healthcare Emergency Department (ED). This DSS aims to aid EDs heads in setting up management guidelines to improve the operation of EDs. This ongoing research is being performed by the Research Group in Individual Oriented Modeling (IoM) at the University Autonoma of Barcelona (UAB) with close collaboration of Hospital ED Staff Team. The objective of the proposed ABM procedure is to optimize the performance of such complex and dynamic Healthcare EDs, because worldwide most of them are overcrowded, and unable to provide ad hoc care, quality and service. Exhaustive search (ES) optimization is used to find out the optimal ED staff configuration, which includes doctors, triage nurses, and admission personnel, i.e., a multidimensional problem. An index is proposed to minimize patient length of stay in the ED. The results obtained by using an alternative pipeline scheme to ES are promising and a better understanding of the problem is achieved. The impact of the pipeline scheme to reduce the computational cost of exhaustive search is outlined

Agent based simulation to optimise emergency departments

Nowadays, many of the health care systems are large and complex environments and quite dynamic, specifically Emergency Departments, EDs. It is opened and working 24 hours per day throughout the year with limited resources, whereas it is overcrowded. Thus, is mandatory to simulate EDs to improve qualitatively and quantitatively their performance. This improvement can be achieved modelling and simulating EDs using Agent-Based Model, ABM and optimising many different staff scenarios. This work optimises the staff configuration of an ED. In order to do optimisation, objective functions to minimise or maximise have to be set. One of those objective functions is to find the best or optimum staff configuration that minimise patient waiting time. The staff configuration comprises: doctors, triage nurses, and admissions, the amount and sort of them. Staff configuration is a combinatorial problem, that can take a lot of time to be solved. HPC is used to run the experiments, and encouraging results were obtained. However, even with the basic ED used in this work the search space is very large, thus, when the problem size increases, it is going to need more resources of processing in order to obtain results in an acceptable time

Dynamic multi-objective optimisation using deep reinforcement learning::benchmark, algorithm and an application to identify vulnerable zones based on water quality

Dynamic multi-objective optimisation problem (DMOP) has brought a great challenge to the reinforcement learning (RL) research area due to its dynamic nature such as objective functions, constraints and problem parameters that may change over time. This study aims to identify the lacking in the existing benchmarks for multi-objective optimisation for the dynamic environment in the RL settings. Hence, a dynamic multi-objective testbed has been created which is a modified version of the conventional deep-sea treasure (DST) hunt testbed. This modified testbed fulfils the changing aspects of the dynamic environment in terms of the characteristics where the changes occur based on time. To the authors’ knowledge, this is the first dynamic multi-objective testbed for RL research, especially for deep reinforcement learning. In addition to that, a generic algorithm is proposed to solve the multi-objective optimisation problem in a dynamic constrained environment that maintains equilibrium by mapping different objectives simultaneously to provide the most compromised solution that closed to the true Pareto front (PF). As a proof of concept, the developed algorithm has been implemented to build an expert system for a real-world scenario using Markov decision process to identify the vulnerable zones based on water quality resilience in São Paulo, Brazil. The outcome of the implementation reveals that the proposed parity-Q deep Q network (PQDQN) algorithm is an efficient way to optimise the decision in a dynamic environment. Moreover, the result shows PQDQN algorithm performs better compared to the other state-of-the-art solutions both in the simulated and the real-world scenario

Addressing Resource Variability Through Resource-Driven Adaptation

Software systems execute tasks that depend on different types of resources. However, the variability of resources may interfere with the ability of software systems to execute important tasks. Resource variability can occur due to several reasons including unexpected hardware failures, excess workloads, or lack of materials. For example, in automated warehouses, malfunctioning robots could delay product deliveries causing customer dissatisfaction and, therefore, reducing an enterprise’s sales. Moreover, the unavailability of medical materials hinders the ability of hospitals to perform medically-critical operations causing loss of life. In this thesis, we propose to address the problem of resource variability through resource-driven adaptation, using task models as input for adaptation decisions. The thesis presents the following contributions: • SPARK: a framework for performing proactive and reactive resource-driven adaptation based on multiple task-related criteria. The framework supports different types of depletable and reusable resources that could face variability. SPARK assists with four types of adaptation, namely: (i) execution of a similar task that requires fewer resources, (ii) substitution of resources by alternative ones, (iii) execution of tasks in a different order, and (iv) cancellation of the execution of tasks. • SERIES: a task modelling notation and editor tool that enables software practitioners to create task models that serve as input for SPARK. SERIES supports the representation of task priorities, task variants, task execution types, resource types, and properties representing users’ feedback. SPARK was evaluated in terms of the percentage of executed critical task requests, the average criticality of the executed task requests in comparison to the non-executed ones, overhead, and scalability through two case studies concerned with a medicine consumption system and a manufacturing system. The results of the evaluation showed that SPARK increased the number of executed critical task requests during resource variability. Additionally, the results showed that the time it takes to prepare and apply adaptation plans does not add significant overhead that hinders the ability of software systems to execute tasks in a tolerable waiting time. Furthermore, SPARK was shown to be scalable since the abovementioned time increases polynomially relative to the input size (number of tasks and task variants). SERIES was evaluated through a user study with twenty software practitioners. The results showed that software practitioners performed very well when explaining and creating task models using SERIES. These results were reflected in the task modelling activities that the participants performed as well as in their positive feedback regarding the usability of SERIES and the clarity of its semantic constructs. Overall, we conclude that the research presented in the thesis contributes to addressing resource variability through resource-driven adaptation. We also provide suggestions for future work that can extend this research

Evolutionary multiobjective optimization for dynamic hospital resource management

Allocating resources to hospital units is a major managerial issue as the relationship between resources, utilization and patient flow of different patient groups is complex. Furthermore, the problem is dynamic as patient arrival and treatment processes are stochastic. In this paper we present a strategy optimization approach where the parameters of different strategies are optimized using a multiobjective EDA. The strategies were designed such that they enable dynamic resource allocation with an offline EDA. Also, the solutions are understandable to health care professionals. We show that these techniques can be applied to this real-world problem. The results are compared to allocation strategies used in hospital practice

Evolutionary multiobjective optimization for dynamic hospital resource management

Allocating resources to hospital units is a major managerial issue as the relationship between resources, utilization and patient flow of different patient groups is complex. Furthermore, the problem is dynamic as patient arrival and treatment processes are stochastic. In this paper we present a strategy optimization approach where the parameters of different strategies are optimized using a multiobjective EDA. The strategies were designed such that they enable dynamic resource allocation with an offline EDA. Also, the solutions are understandable to health care professionals. We show that these techniques can be applied to this real-world problem. The results are compared to allocation strategies used in hospital practice

Evolutionary multiobjective optimization for dynamic hospital resource management

Allocating resources to hospital units is a major managerial issue as the relationship between resources, utilization and patient flow of different patient groups is complex. Furthermore, the problem is dynamic as patient arrival and treatment processes are stochastic. In this paper we present a strategy optimization approach where the parameters of different strategies are optimized using a multiobjective EDA. The strategies were designed such that they enable dynamic resource allocation with an offline EDA. Also, the solutions are understandable to health care professionals. We show that these techniques can be applied to this real-world problem. The results are compared to allocation strategies used in hospital practice

Evolutionary multiobjective optimization for dynamic hospital resource management

Allocating resources to hospital units is a major managerial issue as the relationship between resources, utilization and patient flow of different patient groups is complex. Furthermore, the problem is dynamic as patient arrival and treatment processes are stochastic. In this paper we present a strategy optimization approach where the parameters of different strategies are optimized using a multiobjective EDA. The strategies were designed such that they enable dynamic resource allocation with an offline EDA. Also, the solutions are understandable to health care professionals. We show that these techniques can be applied to this real-world problem. The results are compared to allocation strategies used in hospital practice