Model to Optimize the Management of Strategic Projects Using Genetic Algorithms in a Public Organization

被引:1
|
作者
Izurieta, Richard Romero [1 ,2 ]
Toapanta Toapanta, Segundo Moises [3 ]
Caucha Morales, Luis Jhony [2 ]
Bano Hifong, Maria Mercedes [3 ]
Gomez Diaz, Eriannys Zharayth [4 ]
Mafla Gallegos, Luis Enrique [5 ]
Maciel Arellano, Ma Rocio [6 ]
Orizaga Trejo, Jose Antonio [6 ]
机构
[1] Univ Estatal Milagro UNEMI, Fac Educ Sci, Milagro 091051, Ecuador
[2] Univ Nacl Tumbes, Postgrad Sch, Tumbes 24001, Peru
[3] Univ Catolica Santiago Guayaquil UCSG, Postgrad Subsyst, Guayaquil 090615, Ecuador
[4] Inst Tecnol Super Ruminahui, Res Dept, Sangolqui 171103, Ecuador
[5] Escuela Politecn Nacl EPN, Fac Engn Syst, Quito 170525, Ecuador
[6] Univ Guadalajara, Informat Syst Dept CUCEA, Guadalajara 44100, Jalisco, Mexico
关键词
BPNN; multi-objective optimization; genetic algorithm; information security; mathematical model; NSGA-II; INFORMATION SECURITY; NSGA-II; PORTFOLIO; RISK; UNCERTAINTY; GOVERNANCE;
D O I
10.3390/info13110533
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Public organizations lack adequate models and methods to efficiently support and manage processes related to information security and IT investments. The objective is to optimize the management of strategic projects planned to improve the information security of a public organization and make efficient use of its available resources. The deductive method and exploratory research were used to review and analyze the available information. A mathematical model resulted that optimizes two objectives: (1) minimizing the costs of the strategic projects to be executed, and (2) maximizing the percentage of improvement in the organization's information security. According to the result of the simulation, a subset of planned strategic projects was obtained that allows improving the information security of a public organization from 84.64% to 92.20%, considering the budgetary limitations of the organization. It was concluded that the proposed model is efficient, practical and can be a support tool for the IT management of a public organization.
引用
收藏
页数:19
相关论文
共 50 条
  • [21] Using genetic algorithms to calibrate a water quality model
    Liu, Shuming
    Butler, David
    Brazier, Richard
    Heathwaite, Louise
    Khu, Soon-Thiam
    SCIENCE OF THE TOTAL ENVIRONMENT, 2007, 374 (2-3) : 260 - 272
  • [22] Case study of holistic energy management using genetic algorithms in a sliding window approach
    Minnerup K.
    Herrmann T.
    Steinstraeter M.
    Lienkamp M.
    World Electric Vehicle Journal, 2019, 10 (02):
  • [23] Nonlinear parametric model identification using genetic algorithms
    Pedroso-Rodriguez, LM
    Marrero, A
    de Arazoza, H
    ARTIFICIAL NEURAL NETS PROBLEM SOLVING METHODS, PT II, 2003, 2687 : 473 - 480
  • [24] A multistage value-based model for prioritization of distribution projects using a multiobjective genetic algorithm
    Mussoi F.L.R.
    Teive R.C.G.
    Journal of Control, Automation and Electrical Systems, 2013, 24 (05) : 623 - 637
  • [25] Extended Fuzzy C-Means and Genetic Algorithms to Optimize Power Flow Management in Hybrid Electric Vehicles
    Lucio Ippolito
    Vincenzo Loia
    Pierluigi Siano
    Fuzzy Optimization and Decision Making, 2003, 2 (4) : 359 - 374
  • [26] Extended fuzzy C-means and genetic algorithms to optimize power flow management in hybrid electric vehicles
    Ippolito, L
    Loia, V
    Siano, P
    CCA 2003: PROCEEDINGS OF 2003 IEEE CONFERENCE ON CONTROL APPLICATIONS, VOLS 1 AND 2, 2003, : 115 - 119
  • [27] Using genetic algorithms for developing amorphous silicon atomistic model
    El Hefnawy, SM
    PHYSICS AND SIMULATION OF OPTOELECTRONIC DEVICES XI, 2003, 4986 : 681 - 689
  • [28] A Supply Chain Network Oligopoly Model Using Genetic Algorithms
    Han, Ruijing
    Zhou, Yan
    Wang, Jingjing
    Jiang, Jinglong
    2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2013, : 1240 - 1243
  • [29] Identification of Preisach hysteresis model parameters using genetic algorithms
    Hergli, K.
    Marouani, H.
    Zidi, M.
    Fouad, Yasser
    Elshazly, Mohamed
    JOURNAL OF KING SAUD UNIVERSITY SCIENCE, 2019, 31 (04) : 746 - 752
  • [30] Using Genetic Algorithms for Optimizing and Modelling Time, Cost and Quality Trade Offs of Construction Projects
    Bragadin, Marco A.
    Ballabeni, Andrea
    Kahkonen, Kalle
    IN BO-RICERCHE E PROGETTI PER IL TERRITORIO LA CITTA E L ARCHITETTURA, 2018, 9 (13): : 200 - 207