Paper Info
Title
Resource optimization for elective surgical procedures using quantum-inspired genetic algorithms
Abstract
ABSTRACTCurrently, Health Units in a large number of countries in the world present service demand that exceeds their real capacities. This problem causes the inevitable emergence of long waiting lists. The optimization of such waiting list is very challenging, due to the large number of resources that must be considered. This paper proposes a new model, based on a quantum-inspired evolutionary algorithm, to optimize the scheduling of for elective surgical procedures. The Quantum-Inspired Evolutionary Algorithm for Healthcare (QIEA-H) model, aims not only to designate the necessary resources to the patients in order to achieve the successful completion of the chirurgical procedure, but also to reduce the total time used to perform all surgeries and the number of surgeries out of term. For the validation of the proposed model, a waiting list of 2000 surgeries was created artificially and using a simulation tool also developed in this work. The model achieved a reduction in the time of all surgeries of up to 16.25% and the number of surgeries out of date of up to 13.04%.
Year
DOI
Venue
2019
10.1145/3321707.3321786
Genetic and Evolutionary Computation Conference
Keywords
Field
DocType
Quantum-Inspired Evolutionary Algorithm, Health, Scheduling, Optimization, Genetic Algorithms
Quantum,Computer science,Artificial intelligence,Genetic algorithm,Machine learning,Elective Surgical Procedure
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
René González Hernandez100.34
Marley M. B. R. Vellasco261.43
Karla Figueiredo3306.53