A Model for Optimizing the Portfolio of Hospital District Development Projects Based on a Genetic Algorithm

The article analyzes the healthcare sector's project management tools. It has been found that traditional optimization methods have limited effectiveness in solving complex multicriteria problems that are typical for managing a portfolio of projects to develop hospital districts. The expediency of developing a project portfolio optimization model based on a genetic algorithm is substantiated. The proposed model makes it possible to consider the main criteria, constraints, and goals underlying the achievement of maximum value. Based on the proposed optimization model, a decision support system (DSS) was developed. It allows us to effectively determine a rational scenario for the implementation of a portfolio of hospital district development projects. This system includes tools for analyzing, evaluating, and optimizing projects, taking into account various components of the project environment. Implementation of the SPDP can significantly improve the efficiency of management decision-making to optimize the scenario for the implementation of the hospital district development project portfolio, ensuring maximum value for stakeholders and sustainable development of the region's medical infrastructure.

Authors

Institutions

Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1109/csit65290.2024.10982562
Citations
1
Primary Topic
Technology and Human Factors in Education and Health
Type
article
Field-Weighted Citation Impact
0.54
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Model for Optimizing the Portfolio of Hospital District Development Projects Based on a Genetic Algorithm

Аnatoliy M. Тryhuba, Inna Тryhuba, Oksana Malanchuk, Dmytro Seleznov et al.
1 citations
Technology and Human Factors in Education and Health
0.54
article

A Model for Optimizing the Portfolio of Hospital District Development Projects Based on a Genetic Algorithm

Аnatoliy M. Тryhuba, Inna Тryhuba, Oksana Malanchuk, Dmytro Seleznov, Roksolana Sholudko
article en
1 citations

Abstract

The article analyzes the healthcare sector's project management tools. It has been found that traditional optimization methods have limited effectiveness in solving complex multicriteria problems that are typical for managing a portfolio of projects to develop hospital districts. The expediency of developing a project portfolio optimization model based on a genetic algorithm is substantiated. The proposed model makes it possible to consider the main criteria, constraints, and goals underlying the achievement of maximum value. Based on the proposed optimization model, a decision support system (DSS) was developed. It allows us to effectively determine a rational scenario for the implementation of a portfolio of hospital district development projects. This system includes tools for analyzing, evaluating, and optimizing projects, taking into account various components of the project environment. Implementation of the SPDP can significantly improve the efficiency of management decision-making to optimize the scenario for the implementation of the hospital district development project portfolio, ensuring maximum value for stakeholders and sustainable development of the region's medical infrastructure.

Lutsk National Technical University (UA), Danylo Halytsky Lviv National Medical University (UA), Lviv State University of Life Safety (UA)
Partnerships for the goals
Openalex Percentile: Top 25%
Technology and Human Factors in Education and Health
0.54
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.