Solving Impulsive Control Problems by Discrete-Time Dynamic Optimization Methods

Cardoso, Rodrigo T. N.; Takahashi, Ricardo H. C.

doi:10.5540/tema.2008.09.01.0021

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…Thus, epidemic models are used to describe a rapid outbreak that occurs within one year, while endemic models are used to study disease for a long period after which recovery occurs (including with the acquisition of temporary immunity). The spread of infectious diseases includes not only disease-related factors such as infectious agent, mode of transmission, latency, infectious period, susceptibility and persistence, but also a number of social, cultural, demographic, economic and geographical factors [125].…”

Section: бетоне ядроmentioning

confidence: 99%

Scientific foundations in research in Engineering

Vladlenov¹

2022

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All rights reserved. Printed in the United States of America. No part of this publication may be reproduced, distributed, or transmitted, in any form or by any means, or stored in a data base or retrieval system, without the prior written permission of the publisher. The content and reliability of the articles are the responsibility of the authors. When using and borrowing materials reference to the publication is required. Collection of scientific articles published is the scientific and practical publication, which contains scientific articles of students, graduate students, Candidates and Doctors of Sciences, research workers and practitioners from Europe and Ukraine. The articles contain the study, reflecting the processes and changes in the structure of modern science.

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Section: бетоне ядроmentioning

confidence: 99%

Scientific foundations in research in Engineering

Vladlenov¹

2022

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“…The system state at instance τk is denoted by x(τk) and the control action is denoted by u [k]. The instant of time that comes immediately after τk is τk+1, and is defined as a time instant "just after" the Pulse action in τk [5]. In short, the Pulse control problem is defined formally by T. Yang in [38] as follows:…”

Section: Pulse Vaccine Allocationmentioning

confidence: 99%

A Synthesis of Pulsed Influenza Vaccination Policies Using an Efficient Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA)

Alkhamis¹,

Hosny²

2022

Preprint

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: Seasonal influenza (a.k.a flu) is responsible for considerable morbidity and mortality across the globe. The three recognized pathogens that cause epidemics during the winter season are influenza A, B and C. The influenza virus is particularly dangerous due to its mutability. Vaccines are an effective tool in preventing seasonal influenza, and their formulas are updated yearly according to WHO recommendations. However, in order to facilitate decision-making in the planning of the intervention, policymakers need information on the projected costs and quantities related to introducing the influenza vaccine, in order to help governments obtain an optimal allocation of the vaccine each year. In this paper, an approach based on a Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA) model is introduced to optimize the allocation of influenza vaccination. A bi-objective model is formulated to control the infection volume, and reduce the unit cost of the vaccination campaign. An SIR (Susceptible–Infected–Recovered) model is employed for representing a potential epidemic. The model constraints are based on the epidemiological model, time management, and vaccine quantity. A two-phase optimization process is proposed: guardian control followed by contingent controls. The proposed approach is an evolutionary metaheuristic multi-objective optimization algorithm with a local search procedure based on a hash table. Moreover, in order to optimize the scheduling of a set of policies over a predetermined time to form a complete campaign, an extended CENSGA is introduced with a variable-length chromosome (VLC) along with mutation and crossover operations. To validate the applicability of the proposed CENSGA, it is compared with the classical Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results are analyzed using graphical and statistical comparisons in terms of cardinality, convergence, distribution and spread quality metrics, illustrating that the proposed CENSGA is effective and useful for determining the optimal vaccination allocation campaigns.

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“…This work is based on control by pulse vaccination, which means that in certain time steps, a given percentage of the susceptible individuals get vaccinated and becomes part of the recovered population, according to references [1,7,9,10,14]. This approach allows different sizes of pulse control actions at arbitrary time instants.…”

Section: Optimization Modelmentioning

confidence: 99%

“…To be precise, according to [7], it means that the time in the closed interval [0, T f ], being T f the final time, has to be partitioned in a set of instants previously fixed Γ = {τ 0 , ..., τ M }, such that:…”

Section: Applying Pulse Vaccinationmentioning

confidence: 99%

“…In previous works, some steps have been sketched toward the multiobjective optimization design of impulsive vaccination control of epidemics in a given time horizon. The work [7] proposed using non-fixed pulse vaccination in a deterministic SIR, via open-loop dynamic optimization methods. In a decision process, this can help to choose the specific policy to be implemented.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Vaccination Campaigns Using Stochastic SIR Model and Multiobjective Impulsive Control

Cardoso

Dusse²,

Adam³

2021

TCAM

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A multiobjective impulsive control scheme is proposed to give answers on how optimal vaccination campaigns should be implemented, regarding two conflicting targets: making the total number of infecteds small and the vaccination campaign as handy as possible. In this paper, a stochastic SIR model is used to better depict the characteristics of a disease in practical terms, where little influences may lead to sudden and unpredictable changes in the behavior of transmissions. This model is extended to analyze the effects of impulsive vaccinations in two phases: the transient regime control, taking into account the necessity to reduce the number of infected individuals to an acceptable level in a finite time, and the permanent regime control, that will act with fixed vaccinations to avoid another outbreak. A parallel version of NSGA-II is used as the multiobjective optimization machinery, considering both the probability of eradication and the vaccination campaign costs. The final result using the proposed framework nondominated policies that can guide for public managers to decide which is the best procedure to be taken depending on the present situation.

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Solving Impulsive Control Problems by Discrete-Time Dynamic Optimization Methods

Cited by 7 publications

References 14 publications

Scientific foundations in research in Engineering

Scientific foundations in research in Engineering

A Synthesis of Pulsed Influenza Vaccination Policies Using an Efficient Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA)

Optimal Vaccination Campaigns Using Stochastic SIR Model and Multiobjective Impulsive Control

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