Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments

作者：

Highlights：

• An agent-based simulation is presented to determine patients flow in emergency departments.

• The metamodel is created with an ensemble of ANFIS, feedforward neural network and recurrent neural network.

• The adaptive boosting (AdaBoost) ensemble algorithm is used to find the best ensemble of metamodels.

• A GA is presented to determine optimum resource allocation in EDs and decrease the average length of stay.

摘要

•An agent-based simulation is presented to determine patients flow in emergency departments.•The metamodel is created with an ensemble of ANFIS, feedforward neural network and recurrent neural network.•The adaptive boosting (AdaBoost) ensemble algorithm is used to find the best ensemble of metamodels.•A GA is presented to determine optimum resource allocation in EDs and decrease the average length of stay.

论文关键词：Simulation-based optimization,Decision support system,Adaboost ensemble metamodel,Chaotic genetic algorithm (GA)

论文评审过程：Received 23 December 2016, Revised 6 September 2017, Accepted 8 October 2017, Available online 18 October 2017, Version of Record 5 February 2018.

论文官网地址：https://doi.org/10.1016/j.artmed.2017.10.002