Special Issue "Dynamics under Uncertainty: Modeling Simulation and Complexity II"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Dynamical Systems".

Deadline for manuscript submissions: 28 April 2023 | Viewed by 4355

Special Issue Editors

Dr. Dragan Pamucar
grade E-Mail Website
Guest Editor
Department of logistics, University of Defence, Belgrade, Pavla Jurišića Šturma 33, 11000 Belgrade, Serbia
Interests: multi-criteria decision making problems; computational intelligence; sustainability neuro-fuzzy systems; fuzzy; rough and intuitionistic fuzzy set theory; neutrosophic theory
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dragan Marinkovic
E-Mail Website
Guest Editor
Department of Structural Analysis, Technical University of Berlin, Berlin, Germany
Interests: finite element analysis; nano materials; nano technology; materials science; modeling; mathematical modeling; experimentation; ansys; labview
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical state due to the effects of different types of uncertainties. In fact, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes, and impreciseness can be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc.

Generally, symmetry, asymmetry, and antisymmetry are basic characteristics of binary relations used when modeling dynamical systems. Moreover, the notion of symmetry has appeared in many articles about fuzzy sets, rough sets, Dempster–Shafer theory, etc., which are employed in dynamical systems. Hence, the behavior of dynamical systems with uncertain variables, parameters, and functions has recently attracted academic attention. Similarly, the study of the dynamics manifested in complex networks, or an interaction network of individuals, became popular in the last few decades. The study of collective dynamics in complex interaction networks has been proven to be useful to understand collective dynamic phenomena like the emergence of cooperation between rational agents, the synchronization of signals like a flashlight or fireflies, rumor spreading, conscious forming in a social network, etc. Different methods of statistical mechanics are also successfully applied to study such complex systems and to understand the emergence of different collective behavior. When randomness and imprecision coexist in a system, the system is called a hybrid uncertain system. In such a system, the overall uncertainty is an aggregation of both types of uncertainties. However, in the context of modeling the behavior of complex natural systems, it is extremely important to analyze the effect of appropriate uncertainty in order to understand the predictability of different phenomena. Examples of such uncertain dynamical systems could be cited in different levels of the universe ranging from the interaction of quantum particles to the complex interaction of biochemical molecules like signaling in the brain, or even complex social interactions like forming opinions.

This Special Issue will collect high-quality papers addressing uncertain dynamics, their modeling, and their simulation. Submitted papers should not have been previously published or be currently under consideration for publication elsewhere.

We invite authors to submit original research articles that propose novel modeling and simulation of uncertain complex systems in various fields of natural science.

A quick query of Scopus on uncertain dynamics clearly demonstrates a rapid and steady growth of the area as shown in Figure 1.

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Figure 1. Publications related to uncertain dynamics.

Potential topics include but are not limited to the following:

  • Stochastic dynamics, SPDE;
  • Random dynamical systems;
  • Rough path analysis, random matrix;
  • Uncertain dynamics, fuzzy dynamics, rough dynamics;
  • Network analysis of complex dynamics;
  • Hybrid uncertainty analysis;
  • Simulation and complexity of dynamics under uncertainty.

Prof. Dr. Dragan Pamucar
Prof. Dr. Dragan Marinkovic
Prof. Dr. Samarjit Kar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

Article
Structure Preserving Uncertainty Modelling and Robustness Analysis for Spatially Distributed Dissipative Dynamical Systems
Mathematics 2022, 10(12), 2125; https://doi.org/10.3390/math10122125 - 18 Jun 2022
Cited by 1 | Viewed by 715
Abstract
The paper deals with uncertainty modelling, robust stability and performance analysis of multi-input multi-output (MIMO) reduced order spatially distributed dissipative dynamical systems. While researching the topic of modern robust control of such systems, two key findings were discovered: (i) systematic modelling of the [...] Read more.
The paper deals with uncertainty modelling, robust stability and performance analysis of multi-input multi-output (MIMO) reduced order spatially distributed dissipative dynamical systems. While researching the topic of modern robust control of such systems, two key findings were discovered: (i) systematic modelling of the uncertainty and model order reduction (MOR) at the level of a subsystem gives both modelling freedom and the ability for obtaining less conservative uncertainties on the level of a subsystem; (ii) for a special class of interconnected dissipative dynamical systems, uncertainty conservatism at the subsystem level can be reduced—a novel, structure preserving algorithm employing subsystem partitioning and subsystem MOR by means of balanced truncation method (BTM) is used to obtain low-order robustly stable interconnected systems. Such systems are suitable for practical decentralized and distributed robust controller synthesis. Built upon a powerful framework of integral quadratic constraints (IQCs), this approach gives uncertainty modelling flexibility to perform robustness analysis of real world interconnected systems that are usually affected by multiple types of uncertainties at once. The proposed uncertainty modelling procedure and its practical application are presented on the numerical example. A spatially discretized vibration dynamical system comprised of a series of simply supported Euler beams mutually interconnected by springs and dampers is examined. Spatial discretization of the mathematical model is carried out using the finite element method (FEM). Full article
(This article belongs to the Special Issue Dynamics under Uncertainty: Modeling Simulation and Complexity II)
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Article
Topological Data Analysis with Spherical Fuzzy Soft AHP-TOPSIS for Environmental Mitigation System
Mathematics 2022, 10(11), 1826; https://doi.org/10.3390/math10111826 - 26 May 2022
Cited by 5 | Viewed by 968
Abstract
The idea of spherical fuzzy soft set (SFSS) is a new hybrid model of a soft set (SS) and spherical fuzzy set (SFS). An SFSS is a new approach for information analysis and information fusion, and fuzzy modeling. We define the concepts of [...] Read more.
The idea of spherical fuzzy soft set (SFSS) is a new hybrid model of a soft set (SS) and spherical fuzzy set (SFS). An SFSS is a new approach for information analysis and information fusion, and fuzzy modeling. We define the concepts of spherical-fuzzy-soft-set topology (SFSS-topology) and spherical-fuzzy-soft-set separation axioms. Several characteristics of SFSS-topology are investigated and related results are derived. We developed an extended choice value method (CVM) and the AHP-TOPSIS (analytical hierarchy process and technique for the order preference by similarity to ideal solution) for SFSSs, and presented their applications in multiple-criteria group decision making (MCGDM). Moreover, an application of the CVM is presented in a stock market investment problem and another application of the AHP-TOPSIS is presented for an environmental mitigation system. The suggested methods are efficiently applied to investigate MCGDM through case studies. Full article
(This article belongs to the Special Issue Dynamics under Uncertainty: Modeling Simulation and Complexity II)
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Article
Inventory Management of Perishable Goods with Overconfident Retailers
Mathematics 2022, 10(10), 1716; https://doi.org/10.3390/math10101716 - 17 May 2022
Cited by 1 | Viewed by 834
Abstract
In recent years, many retailers sell their products through not only offline but also online platforms. The sales of perishable goods on e-commerce platforms recorded phenomenal growth in 2020. However, some retailers are overconfident and order more products than the optimal ordering quantity, [...] Read more.
In recent years, many retailers sell their products through not only offline but also online platforms. The sales of perishable goods on e-commerce platforms recorded phenomenal growth in 2020. However, some retailers are overconfident and order more products than the optimal ordering quantity, resulting in great losses due to product decay. In this paper, we apply the newsvendor model to analyze the impacts of overconfident behavior on the retailer’s optimal pricing and order quantity decisions and profit. Our model provides the overconfident retailer with a feasible and effective method to adjust optimal ordering and pricing decisions. Through numerical studies, we examine the retailer’s optimal decisions under the scenarios of complete rationality, over-estimation, and over-precision. We find that the over-estimation retailer always orders more products than the optimal order quantity, and the over-precision retailer always orders fewer products than the optimal order quantity. Under some conditions, overconfidence hurts the retailer’s revenue to a large extent. Therefore, it is beneficial for the overconfident retailer to adjust its order quantity according to our research findings. Full article
(This article belongs to the Special Issue Dynamics under Uncertainty: Modeling Simulation and Complexity II)
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Article
Power Muirhead Mean Operators for Interval-Valued Linear Diophantine Fuzzy Sets and Their Application in Decision-Making Strategies
Mathematics 2022, 10(1), 70; https://doi.org/10.3390/math10010070 - 26 Dec 2021
Cited by 3 | Viewed by 1309
Abstract
It is quite beneficial for every company to have a strong decision-making technique at their disposal. Experts and managers involved in decision-making strategies would particularly benefit from such a technique in order to have a crucial impact on the strategy of their company. [...] Read more.
It is quite beneficial for every company to have a strong decision-making technique at their disposal. Experts and managers involved in decision-making strategies would particularly benefit from such a technique in order to have a crucial impact on the strategy of their company. This paper considers the interval-valued linear Diophantine fuzzy (IV-LDF) sets and uses their algebraic laws. Furthermore, by using the Muirhead mean (MM) operator and IV-LDF data, the IV-LDF power MM (IV-LDFPMM) and the IV-LDF weighted power MM (IV-LDFWPMM) operators are developed, and some special properties and results demonstrated. The decision-making technique relies on objective data that can be observed. Based on the multi-attribute decision-making (MADM) technique, which is the beneficial part of the decision-making strategy, examples are given to illustrate the development. To demonstrate the advantages of the developed tools, a comparative analysis and geometrical interpretations are also provided. Full article
(This article belongs to the Special Issue Dynamics under Uncertainty: Modeling Simulation and Complexity II)
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