Submit

Login Login

Register Register

Home / Journals / CMES / Special Issue / Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making
Journal Menu
Special Issues
Table of Content

All issues

Online First

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making

Submission Deadline: 15 December 2025 View: 3989 Submit to Special Issue

Guest Editors

Prof. Dr. Dragan Pamucar

Email: dragan.pamucar@fon.bg.ac.rs

Affiliation: Faculty of Organizational Sciences, University of Belgrade, Belgrade

Homepage:

Research Interests: Logistics & Transportation, Multiple Criteria Decision Making, Decision Support Systems, MCDM 

图片7.png


Dr. Samayan Narayanamoorthy

Email: snmphd@buc.edu.in

Affiliation: Department of Mathematics, Bharathiar University, Coimbatore

Homepage:

Research Interests: Fuzzy Logic, Decision Sciences, MCDM, Differential  Equation, Industrial Engineering

图片8.png


Dr. Daekook Kang

Email: dkkang@inje.ac.kr

Affiliation: Department of Industrial and Management Engineering, Inje University, Gimhae, 50834, South Korea

Homepage:

Research Interests: MCDM, Fuzzy set theory, Technology forecasting, Service engineering, Quality Management

图片9.png

Summary

Fuzzy optimization is an approach that combines conventional techniques for optimization with fuzzy set theory in order to provide effective fuzzy optimization methods for both theoretical and practical application of solving problems in manufacturing, management, and artificial intelligence. Fuzzy decision-making is the act of making decisions using fuzzy logic, which permits a more adaptable interpretation of the available data.


The objective of this special issue is to advance different research and improvements in fuzzy technology and soft-computing methodologies to improve the capacity to handle complex optimization and decision-making problems involving imprecision, uncertainty, and incomplete information that can determine fuzzy optimal decisions and solutions. We welcome research articles pertaining to the state-of-the-art computation algorithms, mathematical models, theoretical advancements, systems developments, and applications. In order to help foster fuzzy technologies should be better understood, developed, and applied in engineering, management, and societal problems. The invitation for high-quality research papers and review articles on topics including, but not limited to:


Algorithms and methods for fuzzy optimization

Hybrid fuzzy optimization and data-driven techniques

Fuzzy multi-criteria decision making (FMCDM)

Integrating fuzzy with other soft computing methods

Advanced artificial intelligence based machine learning models

Large-scale group decision making

Extension of fuzzy sets, operations, and measures on uncertain information

Applications on environmental and financial impact assessment

Engineering control, optimization, and development

Allocating and scheduling resource using fuzzy logic

Risk analysis and financial planning

Optimization of supply chain and logistics

Keywords

Fuzzy sets and logic; Uncertain information; Advanced fuzzy systems; Uncertainty modelling; Fuzzy optimization; Multi-objective optimization; Engineering data analytics; AI in engineering management; Multi-criteria decision making; Fuzzy analytical hierarchy process; Sustainable development; Energy system; Supply chain management; Logistic specialist

Published Papers

Show export options

  • Open Access

    ARTICLE

    A Unified Parametric Divergence Operator for Fermatean Fuzzy Environment and Its Applications in Machine Learning and Intelligent Decision-Making

    Zhe Liu, Sijia Zhu, Yulong Huang, Tapan Senapati, Xiangyu Li, Wulfran Fendzi Mbasso, Himanshu Dhumras, Mehdi Hosseinzadeh
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2157-2188, 2025, DOI:10.32604/cmes.2025.072352
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract Uncertainty and ambiguity are pervasive in real-world intelligent systems, necessitating advanced mathematical frameworks for effective modeling and analysis. Fermatean fuzzy sets (FFSs), as a recent extension of classical fuzzy theory, provide enhanced flexibility for representing complex uncertainty. In this paper, we propose a unified parametric divergence operator for FFSs, which comprehensively captures the interplay among membership, non-membership, and hesitation degrees. The proposed operator is rigorously analyzed with respect to key mathematical properties, including non-negativity, non-degeneracy, and symmetry. Notably, several well-known divergence operators, such as Jensen-Shannon divergence, Hellinger distance, and χ2-divergence, are shown to be special cases More >

    View

    797

    Download

    130

  • Open Access

    ARTICLE

    Dombi Power Aggregation-Based Decision Framework for Smart City Initiative Prioritization under t-Arbicular Fuzzy Environment

    Jawad Ali, Ioan-Lucian Popa
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 857-889, 2025, DOI:10.32604/cmes.2025.064604
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract With the rapid growth of urbanization, smart city development has become a strategic priority worldwide, requiring complex and uncertain decision-making processes. In this context, advanced decision-support tools are essential to evaluate and prioritize competing initiatives effectively. To support effective prioritization of smart city initiatives under uncertainty, this study introduces a robust decision-making framework based on the t-arbicular fuzzy (t-AF) set—a recent extension of the t-spherical fuzzy set that incorporates an additional parameter, the radius , to enhance the representation of uncertainty. Dombi-based operational laws are formulated within this context, leading to the development of four… More >

    View

    449

    Download

    163

  • Open Access

    ARTICLE

    Urban Transportation Strategy Selection for Multi-Criteria Group Decision-Making Using Pythagorean Fuzzy N-Bipolar Soft Expert Sets

    Sagvan Y. Musa, Zanyar A. Ameen, Wafa Alagal, Baravan A. Asaad
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3493-3529, 2025, DOI:10.32604/cmes.2025.070019
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility, cost-effectiveness, and environmental impact, often under uncertainty and incomplete information. These complex decisions require input from various stakeholders, including planners, policymakers, engineers, and community representatives, whose opinions may differ or contradict. Traditional decision-making approaches struggle to effectively handle such bipolar and multivalued expert evaluations. To address these challenges, we propose a novel decision-making framework based on Pythagorean fuzzy N-bipolar soft expert sets. This model allows experts to express both positive and negative opinions on a multinary scale, capturing nuanced judgments with higher accuracy. It… More >

    View

    986

    Download

    563

  • Open Access

    ARTICLE

    Auto-Weighted Neutrosophic Fuzzy Clustering for Multi-View Data

    Zhe Liu, Jiahao Shi, Dania Santina, Yulong Huang, Nabil Mlaiki
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3531-3555, 2025, DOI:10.32604/cmes.2025.071145
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations. However, traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data, as they rely on a single-dimensional membership value. To overcome these limitations, we propose an auto-weighted multi-view neutrosophic fuzzy clustering (AW-MVNFC) algorithm. Our method leverages the neutrosophic framework, an extension of fuzzy sets, to explicitly model imprecision and ambiguity through three membership degrees. The core novelty of AW-MVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions More >

    View

    623

    Download

    520

  • Open Access

    ARTICLE

    An Improved Interval-Valued Picture Fuzzy TOPSIS Approach Based on New Divergence Measures for Risk Assessment

    Sijia Zhu, Yuhan Li, Prasanalakshmi Balaji, Akila Thiyagarajan, Rajanikanth Aluvalu, Zhe Liu
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2099-2121, 2025, DOI:10.32604/cmes.2025.068734
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract While interval-valued picture fuzzy sets (IvPFSs) provide a powerful tool for modeling uncertainty and ambiguity in various fields, existing divergence measures for IvPFSs remain limited and often produce counterintuitive results. To address these shortcomings, this paper introduces two novel divergence measures for IvPFSs, inspired by the Jensen-Shannon divergence. The fundamental properties of the proposed measures—non-degeneracy, symmetry, triangular inequality, and boundedness—are rigorously proven. Comparative analyses with existing measures are conducted through specific cases and numerical examples, clearly demonstrating the advantages of our approach. Furthermore, we apply the new divergence measures to develop an enhanced interval-valued picture More >

    View

    8482

    Download

    1492

  • Open Access

    ARTICLE

    Innovative Aczel Alsina Group Overlap Functions for AI-Based Criminal Justice Policy Selection under Intuitionistic Fuzzy Set

    Ikhtesham Ullah, Muhammad Sajjad Ali Khan, Fawad Hussain, Madad Khan, Kamran, Ioan-Lucian Popa
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2123-2164, 2025, DOI:10.32604/cmes.2025.064832
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract Multi-criteria decision-making (MCDM) is essential for handling complex decision problems under uncertainty, especially in fields such as criminal justice, healthcare, and environmental management. Traditional fuzzy MCDM techniques have failed to deal with problems where uncertainty or vagueness is involved. To address this issue, we propose a novel framework that integrates group and overlap functions with Aczel-Alsina (AA) operational laws in the intuitionistic fuzzy set (IFS) environment. Overlap functions capture the degree to which two inputs share common features and are used to find how closely two values or criteria match in uncertain environments, while the… More >

    View

    1869

    Download

    1552

  • Open Access

    ARTICLE

    Incorporating Fully Fuzzy Logic in Multi-Objective Transshipment Problems: A Study of Alternative Path Selection Using LR Flat Fuzzy Numbers

    Vishwas Deep Joshi, Priya Agarwal, Lenka Čepová, Huda Alsaud, Ajay Kumar, B. Swarna, Ashish Kumar
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 969-1011, 2025, DOI:10.32604/cmes.2025.063996
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract In a world where supply chains are increasingly complex and unpredictable, finding the optimal way to move goods through transshipment networks is more important and challenging than ever. In addition to addressing the complexity of transportation costs and demand, this study presents a novel method that offers flexible routing alternatives to manage these complexities. When real-world variables such as fluctuating costs, variable capacity, and unpredictable demand are considered, traditional transshipment models often prove inadequate. To overcome these challenges, we propose an innovative fully fuzzy-based framework using LR flat fuzzy numbers. This framework allows for more… More >

    View

    958

    Download

    641

  • Open Access

    ARTICLE

    Quantum-Driven Spherical Fuzzy Model for Best Gate Security Systems

    Muhammad Amad Sarwar, Yuezheng Gong, Sarah A. Alzakari, Amel Ali Alhussan
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3523-3555, 2025, DOI:10.32604/cmes.2025.066356
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract Global security threats have motivated organizations to adopt robust and reliable security systems to ensure the safety of individuals and assets. Biometric authentication systems offer a strong solution. However, choosing the best security system requires a structured decision-making framework, especially in complex scenarios involving multiple criteria. To address this problem, we develop a novel quantum spherical fuzzy technique for order preference by similarity to ideal solution (QSF-TOPSIS) methodology, integrating quantum mechanics principles and fuzzy theory. The proposed approach enhances decision-making accuracy, handles uncertainty, and incorporates criteria relationships. Criteria weights are determined using spherical fuzzy sets,… More >

    View

    452

    Download

    341

  • Open Access

    ARTICLE

    Promoting Tailored Hotel Recommendations Based on Traveller Preferences: A Circular Intuitionistic Fuzzy Decision Support Model

    Sana Shahab, Ibtehal Alazman, Ashit Kumar Dutta, Mohd Anjum, Vladimir Simic, Željko Stević, Nouf Abdulrahman Alqahtani
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2155-2183, 2025, DOI:10.32604/cmes.2025.064553
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract With the increasing complexity of hotel selection, traditional decision-making models often struggle to account for uncertainty and interrelated criteria. Multi-criteria decision-making (MCDM) techniques, particularly those based on fuzzy logic, provide a robust framework for handling such challenges. This paper presents a novel approach to MCDM within the framework of Circular Intuitionistic Fuzzy Sets (C-IFS) by combining three distinct methodologies: Weighted Aggregated Sum Product Assessment (WASPAS), an Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN), and the CRITIC method (Criteria Importance Through Intercriteria Correlation). To address the dynamic nature of traveler preferences in hotel selection,… More >

    View

    1050

    Download

    526

  • Open Access

    ARTICLE

    Development of AHP-Based Divergence Distance Measure between –Spherical Fuzzy Sets with Applications in Multi-Criteria Decision Making

    Shah Zeb Khan, Muhammad Rahim, Adel M. Widyan, A. Almutairi, Njood Shaher Ethaar Almutire, Hamiden Abd El-Wahed Khalifa
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2185-2211, 2025, DOI:10.32604/cmes.2025.063929
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract This study introduces a novel distance measure (DM) for spherical fuzzy sets (SFSs) to improve decision-making in complex and uncertain environments. Many existing distance measures either fail to satisfy essential axiomatic properties or produce unintuitive outcomes. To address these limitations, we propose a new three-dimensional divergence-based DM that ensures mathematical consistency, enhances the discrimination of information, and adheres to the axiomatic framework of distance theory. Building on this foundation, we construct a multi-criteria decision-making (MCDM) model that utilizes the proposed DM to evaluate and rank alternatives effectively. The applicability and robustness of the model are More >

    View

    910

    Download

    427

  • Open Access

    ARTICLE

    Fuzzy N-Bipolar Soft Sets for Multi-Criteria Decision-Making: Theory and Application

    Sagvan Y. Musa, Baravan A. Asaad, Hanan Alohali, Zanyar A. Ameen, Mesfer H. Alqahtani
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 911-943, 2025, DOI:10.32604/cmes.2025.062524
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract This paper introduces fuzzy N-bipolar soft (FN-BS) sets, a novel mathematical framework designed to enhance multi-criteria decision-making (MCDM) processes under uncertainty. The study addresses a significant limitation in existing models by unifying fuzzy logic, the consideration of bipolarity, and the ability to evaluate attributes on a multinary scale. The specific contributions of the FN-BS framework include: (1) a formal definition and set-theoretic foundation, (2) the development of two innovative algorithms for solving decision-making (DM) problems, and (3) a comparative analysis demonstrating its superiority over established models. The proposed framework is applied to a real-world case More >

    View

    1311

    Download

    633

  • Open Access

    REVIEW

    Unveiling Effective Heuristic Strategies: A Review of Cross-Domain Heuristic Search Challenge Algorithms

    Mohamad Khairulamirin Md Razali, Masri Ayob, Abdul Hadi Abd Rahman, Razman Jarmin, Chian Yong Liu, Muhammad Maaya, Azarinah Izaham, Graham Kendall
    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1233-1288, 2025, DOI:10.32604/cmes.2025.060481
    （This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract The Cross-domain Heuristic Search Challenge (CHeSC) is a competition focused on creating efficient search algorithms adaptable to diverse problem domains. Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process. Numerous selection hyper-heuristics have different implementation strategies. However, comparisons between them are lacking in the literature, and previous works have not highlighted the beneficial and detrimental implementation methods of different components. The question is how to effectively employ them to produce an efficient search heuristic. Furthermore, the algorithms that competed in the inaugural CHeSC have not been collectively reviewed. This… More >

    View

    1006

    Download

    636

Copyright© 2025 Tech Science Press
© 1997-2025 TSP (Henderson, USA) unless otherwise stated

Share Link