About: Balancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization

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About: Balancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Balancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization
Creator	Chen, Xin Wang, Ling Gan, Tie-Er Sheng, Wei-Guo Zhang, Min-Xia Zheng, Yu-Jun
Source	ArXiv
abstract	Balancing common disease treatment and epidemic control is a key objective of medical supplies procurement in hospitals during a pandemic such as COVID-19. This problem can be formulated as a bi-objective optimization problem for simultaneously optimizing the effects of common disease treatment and epidemic control. However, due to the large number of supplies, difficulties in evaluating the effects, and the strict budget constraint, it is difficult for existing evolutionary multiobjective algorithms to efficiently approximate the Pareto front of the problem. In this paper, we present an approach that first transforms the original high-dimensional, constrained multiobjective optimization problem to a low-dimensional, unconstrained multiobjective optimization problem, and then evaluates each solution to the transformed problem by solving a set of simple single-objective optimization subproblems, such that the problem can be efficiently solved by existing evolutionary multiobjective algorithms. We applied the transform-and-divide evolutionary optimization approach to six hospitals in Zhejiang Province, China, during the peak of COVID-19. Results showed that the proposed approach exhibits significantly better performance than that of directly solving the original problem. Our study has also shown that transform-and-divide evolutionary optimization based on problem-specific knowledge can be an efficient solution approach to many other complex problems and, therefore, enlarge the application field of evolutionary algorithms.
has issue date	2020-08-02(xsd:dateTime)
has license	arxiv
sha1sum (hex)	7088c3e4a1576643a4839acc5075cc3bff65a0fa
resource representing a document's title	Balancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization
resource representing a document's body	covid:7088c3e4a1576643a4839acc5075cc3bff65a0fa#body_text
is schema:about of	named entity 'COVID-19' named entity 'BASED' named entity 'OBJECTIVE' named entity 'HIGH' named entity 'DIFFICULTIES' named entity 'ORIGINAL' named entity 'COMMON DISEASE' named entity 'CONTROL' named entity 'COVID-19' named entity 'MEDICAL' named entity 'BALANCING' named entity 'optimization problem' named entity 'COVID-19' named entity 'medical supplies' named entity 'Zhejiang Province' named entity 'COVID-19' named entity 'China' named entity 'epidemic' named entity 'Evolutionary Optimization' named entity 'NSGA-II' named entity 'Line 26' named entity 'NSGA-II' named entity 'distance measure' named entity 'optimization problems' named entity 'complex problems' named entity 'vectors' named entity 'optimization problems' named entity 'upper bound' named entity 'COVID-19' named entity 'tabu search' named entity 'normal saline' named entity 'heuristics' named entity 'solution to a problem' named entity 'pairwise comparison' named entity 'high-dimensional' named entity 'global optima' named entity 'penalty function' named entity 'China' named entity 'vector' named entity 'Algorithm' named entity 'NSGA-II' named entity 'algorithm' named entity 'treatment effects' named entity 'expected number' named entity 'time complexity' named entity 'multiobjective' named entity 'budget constraint' named entity 'treatment effects' named entity 'multiobjective optimization' named entity 'NSGA-II' named entity 'vector' named entity 'China' named entity 'time complexity' named entity 'RMB' named entity 'non-separability' named entity 'Algorithm' named entity 'evolutionary optimization' named entity 'algorithm' named entity 'low-dimensional' named entity 'computationally expensive' named entity 'infection' named entity 'multiobjective optimization' named entity 'COVID-19' named entity 'vector' named entity 'uniform distribution' named entity 'assignment problem' named entity 'multimodal optimization' named entity 'multiobjective' named entity 'infection' named entity 'gradient-based' named entity 'algorithm' named entity 'branch-and-bound algorithm' named entity 'historical data' named entity 'low-dimensional' named entity 'problem-solving' named entity 'algorithm' named entity 'Algorithm' named entity 'solving this problem' named entity 'low-dimensional' named entity 'multiobjective' named entity 'mn 1' named entity 'COVID-19'

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