multiobjective optimization
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2022 ◽  
Vol 20 (3) ◽  
pp. 386-394
Author(s):  
Adelicio Maximiano Sobrinho ◽  
Jose Roberto Camacho ◽  
Rafael Lima de Carvalho ◽  
Sergio Manuel Rivera Sanhueza ◽  
Stefani Carolline Leal de Freitas

2022 ◽  
Vol 172 ◽  
pp. 108840
Author(s):  
Han Huang ◽  
Xintao Yang ◽  
Qinghao Yan ◽  
Zhixin Xiang ◽  
Shucai Xu

2022 ◽  
Vol 148 (1) ◽  
Author(s):  
Jiangfeng Wang ◽  
Guanglin Liao ◽  
Qiyao Zuo ◽  
Yumin Guo ◽  
Pan Zhao ◽  
...  

2022 ◽  
Vol 54 (1) ◽  
pp. 181-194
Author(s):  
Babooshka Shavazipour ◽  
Bekir Afsar ◽  
Juhani Multanen ◽  
Kaisa Miettinen ◽  
Urho M. Kujala

Author(s):  
Firoz Ahmad ◽  
Ahmad Yusuf Adhami ◽  
Boby John ◽  
Amit Reza

Many decision-making problems can solve successfully by traditional optimization methods with a well-defined configuration.  The formulation of such optimization problems depends on crisply objective functions and a specific system of constraints.  Nevertheless, in reality, in any decision-making process, it is often observed that due to some doubt or hesitation, it is pretty tricky for decision-maker(s) to specify the precise/crisp value of any parameters and compelled to take opinions from different experts which leads towards a set of conflicting values regarding satisfaction level of decision-maker(s). Therefore the real decision-making problem cannot always be deterministic. Various types of uncertainties in parameters make it fuzzy.  This paper presents a practical mathematical framework to reflect the reality involved in any decision-making process. The proposed method has taken advantage of the hesitant fuzzy aggregation operator and presents a particular way to emerge in a decision-making process. For this purpose,  we have discussed a couple of different hesitant fuzzy aggregation operators and developed linear and hyperbolic membership functions under hesitant fuzziness, which contains the concept of hesitant degrees for different objectives.  Finally, an example based on a multiobjective optimization problem is presented to illustrate the validity and applicability of our proposed models.


2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Fouzia Amir ◽  
Ali Farajzadeh ◽  
Jehad Alzabut

Abstract Multiobjective optimization is the optimization with several conflicting objective functions. However, it is generally tough to find an optimal solution that satisfies all objectives from a mathematical frame of reference. The main objective of this article is to present an improved proximal method involving quasi-distance for constrained multiobjective optimization problems under the locally Lipschitz condition of the cost function. An instigation to study the proximal method with quasi distances is due to its widespread applications of the quasi distances in computer theory. To study the convergence result, Fritz John’s necessary optimality condition for weak Pareto solution is used. The suitable conditions to guarantee that the cluster points of the generated sequences are Pareto–Clarke critical points are provided.


2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Xiaocheng Li ◽  
Zhaoli Liu ◽  
Fangzhen Ge

It is a global issue to set up a practical, sensitive, and useful model to eradicate or mitigate the coronavirus disease 2019 (COVID-19). Taking Central China’s Hubei Province for example, three models were established. Firstly, a susceptible-probable-infectious-recovered (SPIR) model was proposed to predict the monthly number of confirmed and susceptible cases in each city. Next, an epidemic prefecture clustering model was set up to find proper vaccine delivery sites, according to the distance of each city. Finally, a dynamic material delivery optimization model was established for multiple epidemic prefectures, aiming to speed up vaccine production and storage in each delivery site.


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