Spectrum of Decision Making and Applications
Spectrum of Decision Making Applications publishes high-quality scientific papers that contribute significantly to the fields of decision-making. The material published is of high quality and relevance, written in a manner that makes it accessible to this wide-ranging readership. Spectrum of Decision Making Applications is an international peer-reviewed journal covering the main aspects of decision-making and providing a source of information for applied scientists. Spectrum of Decision Making Applications promotes research that can help decision-makers make the best choice by means of various optimization models. We invite research that supports the joint application of predictive models and optimization technology to create better solutions for decision-makers. Spectrum of Decision Making Applications invite research that utilizes the latest techniques in data mining, analysis, and performance management to help decision-makers gain and sustain a competitive edge. Spectrum of Decision Making Applications aims at this important task by providing a venue for high-quality scientific papers that contribute significantly to the field of decision science. The material published is of high quality and relevance, written in a manner that makes it accessible to a wide-ranging readership. Papers reporting original theoretical and/or practice-oriented research or extended versions of the already published conference papers are all welcome. The scope of the journal covers the whole spectrum of decision science. Papers for publication are selected through a double-blind peer-review process to ensure originality, relevance, and readability. In doing that, the objective is not only to keep the quality of published papers high but also to provide a timely, thorough, and balanced review process. Spectrum of Decision Making Applications publishes research articles, reviews, short communications, and case studies. Research articles must include motivation for the work, an adequate overview of the representative work in the field including up-to-date references, a clear statement of the novelty in the presented research, suitable theoretical background, one or more examples to demonstrate and discuss the presented ideas, and, finally, conclusions. Short communications are usually 4-7 pages long, research articles and case studies 8-14 pages, while reviews can be longer. Page number limits are not strict and, with appropriate reasoning, the submitted articles can also be longer or shorter. Authors are requested to follow the Spectrum of Decision Making Applications guidelines and strictly format their manuscripts as per the article template that is available here. If extensions of previously published conference papers are submitted, Editors will check if sufficient new material has been added to fulfill the journal standards and qualify the submission for the review process. The added material must have not been previously published. New results are desired but not necessarily required; however, the submission should contain expansions of key ideas, examples, elaborations, etc. of the conference submission. Aims and Scope: The principal aim of the journal is to bring together the latest research and development in various fields of decision science. We would like to highlight that papers should refer to Aims and scope, but they are not limited to. Publication Frequency: One issue per year is published online, but processed and accepted papers, with full bibliographic data, are added to the issue continuously over the whole year. Open Access: This is an open-access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. eISSN (Online): 3042-1462 Publication fee: There is no submission charge or publication fee. Publication in Spectrum of Decision Making Applications is free of charge for the authors.
A Robust Circular Complex Intuitionistic Fuzzy Framework for Optimizing Agricultural Robot Decision-Making Under Uncertain Environmental Conditions
Agricultural robotic systems frequently operate in highly dynamic and uncertain environmental conditions, where incomplete sensor readings, weather variability, terrain irregularities, and crop-state ambiguity significantly affect decision-making performance. To address these challenges, this study introduces the concept of Circular Complex Intuitionistic Fuzzy Sets (CrC-IFS) as an advanced mathematical tool for modeling uncertainty in agricultural robot decision-making processes. The Circular Complex Intuitionistic Fuzzy Set extends classical complex intuitionistic and circular intuitionistic fuzzy structures by incorporating enhanced higher-order membership flexibility for representing imprecise and multidimensional environmental information. To strengthen uncertainty modeling capability, refined algebraic operational laws for CrC-IFS are developed, including direct sum, direct product, and scalar multiplication based on generalized t-norms and t-conorms. Furthermore, circular complex intuitionistic fuzzy weighted and ordered weighted aggregation operators are proposed to integrate multiple environmental and operational criteria, such as terrain conditions, obstacle density, energy consumption, crop maturity levels, and weather fluctuations. Building upon these theoretical developments, a robust multi-criteria decision-making framework is constructed to optimize agricultural robot strategies, enabling systematic prioritization of navigation paths, task allocation, harvesting schedules, and adaptive control actions. The results demonstrate that the proposed framework enhances decision robustness, improves operational efficiency, and supports intelligent autonomous behavior under highly uncertain agricultural field conditions.
Circular complex intuitionistic fuzzy set
Copyright (c) 2026 Kaleem Ullah, Noor Rehman, Abbas Ali (Author) https://creativecommons.org/licenses/by/4.0
Sensitivity Analysis and Validation in MCDM Methods: A Comprehensive Review with Advancements, Applications, and Future Directions
The accelerated development of multi-criteria decision-making (MCDM) tools in engineering, management, environmental planning, and financial analytics has increased the demand for more precise and responsive weighting methods. Stereotypical subjective and objective weighting methods tend to be biased, inflexible, or fail to react to dynamic decision settings. The review summarizes a series of newly developed novel weighting approaches, such as hybrid, data-driven, machine-learning-assisted, and uncertainty-adaptive approaches, to address these limitations. Drawing from a broad scope of published literature, this paper assesses the methodological developments, including fuzzy-enhanced weighting, the use of entropy-based extensions, CRITIC variants, Bayesian and probabilistic schemes, combined Delphi-ANP-DEMATEL approaches, best-worst method (BWM) refinements, and new deep-learning-based weight estimators. The review points out the ability of such modern approaches to boost robustness, decrease subjectivity, and increase transparency in decision-making in a variety of MCDM models. It also establishes the gaps in methodology, current trends, and future research directions and sets new weighting strategies as a key tool in managing complex, uncertain, and high-dimensional decision problems. This combination helps them gain a better theoretical insight as well as provide some practical advice to researchers and practitioners wishing to apply advanced weighting methods in MCDM.
Copyright (c) 2026 Arkyadeep Sarkar, Shankha Shubhra Goswami, Krishna Kumar Gupta (Author) https://creativecommons.org/licenses/by/4.0
Matrix Games under an Intuitionistic Fuzzy Duality Approach with Exponential Functions: MRI-Related Cochlear Implant Analysis
Game theory has evolved to incorporate various forms of uncertainty. The intuitionistic fuzzy (I-Fuzzy) framework is a powerful tool for modeling such uncertainties, particularly in two-person zero-sum matrix games (TZMGs) with imprecise goals. This study addresses TZMGs where goals are represented in I-Fuzzy form under pessimistic, optimistic, and mixed approaches. By applying exponential functions, optimization problems are constructed for each player. These problems are then transformed into crisp linear programming problems (LPPs) using logarithmic functions, enabling the derivation of optimal strategies. The role and impact of the shape parameter in exponential functions are analyzed, highlighting its influence through comparative insights. To illustrate the effectiveness and real-world relevance of the proposed approach, a practical healthcare problem evaluating adverse MRI effects on cochlear implant (CI) users is solved, showcasing both the methodology's applicability and its potential for decision-making under uncertainty.
Copyright (c) 2026 Sandeep Kumar, Aashish, Parth Parashar (Author) https://creativecommons.org/licenses/by/4.0
A Comprehensive Review of Fuzzy Multiple Criteria Decision-Making (MCDM) Methods: Advancements, Applications, and Future Directions
Real-world situations can be associated with numerous conflicting criteria, imprecise information, and subjective human decisions. Traditional Multiple Criteria Decision-Making (MCDM) approaches are not able to manage such uncertainty sufficiently. MCDM methods based on fuzzy logic address these shortcomings by incorporating linguistic preferences and modelling uncertainty in expert appraisal. In this paper, fuzzy MCDM techniques are reviewed thoroughly, tracing their development from classical fuzzy extensions to more recent developments in intuitionistic, Pythagorean, and picture fuzzy models. The paper offers a systematic classification, including outranking, value-based, pairwise comparison, and hybrid decision models. Important application areas such as energy planning, healthcare, supply chain management, transportation, and intelligent systems are critically scrutinized. Challenges related to computational complexity, subjectivity, model validation, and real-time deployment are addressed. Finally, future directions are identified, including intelligent automation, data-driven decision support, standardization of uncertainty modelling, and autonomous decision-making in dynamic environments.
Copyright (c) 2026 Sushil Kumar Sahoo, Bibhuti Bhusan Choudhury, Prasant Ranjan Dhal (Author) https://creativecommons.org/licenses/by/4.0
Prioritizing Ventilator Machines in Emergency Scenarios Using Multi-Criteria Decision-Making with Circular Intuitionistic Fuzzy Set and Dombi Operations
In the modern age, the circular intuitionistic fuzzy (IF) set (CIFS) is highly popular because it includes a membership grade and a non-membership grade within the range of the unit interval [0,1], as well as a circular degree within the range of [0,√2]. In contrast, a simple IFS is based only on membership and non-membership grades within the range of [0,1]. Moreover, the theory of IFS is unable to handle information in which a circular degree is involved. Hence, the CIFS is the most generalized format of IFS. In decision-making (DM) sciences, multi-criteria decision making (MCDM) is one of the most effective approaches for combining data and opinions from different sources. It evaluates and selects the best option when a decision involves several criteria. The Dombi t-norm (DTNM) and Dombi t-conorm (DTCNM) are crucial tools for aggregating uncertain and imprecise information. The notion of a power aggregation operator (PAO) is also a powerful concept for evaluating the weight vectors (WV) of alternatives by combining them and their attributes. The idea of PAO is considered a valuable tool for the evaluation of WV, providing reliability in aggregated outcomes. Using the concepts of CIFS, PAOs, DTNM, and DTCNM operations, we construct a new family of aggregation operators (AOs) called circular IF Dombi power weighted averaging (CIFDPWA) and circular IF Dombi power weighted geometric (CIFDPWG) operators for finding reliable solutions to MCDM problems. We investigate some fundamental axioms of AOs, such as monotonicity, boundedness, and idempotency. We provide an MCDM algorithm based on the proposed theory. The case study details the best ventilator selection using the developed CIFDPWA and CIFDPWG based on the MCDM approach. We found that GE Healthcare is the best alternative using the CIFDPWA, while Medtronic is the best option using the CIFDPWG operator. To check the applicability of the developed AOs, we made a detailed comparison with some other existing AOs. Finally, it offers some firm conclusions.
Copyright (c) 2025 Muhammad Rizwan Khan, Kifayat Ullah, Waseem, Faizan Pervaiz (Author) https://creativecommons.org/licenses/by/4.0
Pythagorean Soft Sets and Hypersoft Sets: A Comprehensive Framework for Advanced Uncertainty Modeling in Decision Making
This paper introduces a comprehensive mathematical framework that integrates Pythagorean fuzzy sets (PyFS), soft sets (SS), and hypersoft set theory to establish robust methodologies for addressing complex uncertainties in multi-attribute decision-making (MADM). We present two formal approaches: Pythagorean soft sets (PySS) and Pythagorean hypersoft sets (PyHSS), which enhance the conventional soft set and hypersoft set frameworks by incorporating the expressiveness of Pythagorean fuzzy sets (PyFS). The theoretical framework demonstrates that these structures preserve all fundamental set-theoretic operations and facilitate the representation of the membership degree (MD) and non-membership degree (n-MD) with sums not exceeding 1, contingent upon satisfying the Pythagorean condition ψ² + φ² ≤ 1. We illustrate the applicability of our approach through extensive digital implementations in technology selection, cloud-based configuration, and educational recommendation systems. The mathematical functionalities exhibited, including operation closure and generalisation hierarchies, render PySS and PyHSS formidable decision support system instruments for managing imprecise and ambiguous information across several criteria.
Copyright (c) 2025 Muhammad Tahir, Kamran Kfueit, Misbah Rasheed, Abdul Hanan, Muhammad Imran Shahid (Author) https://creativecommons.org/licenses/by/4.0
HyperFuzzy and SuperHyperFuzzy Group Decision-Making
Fuzzy sets model vagueness by assigning each element a membership value in the interval [0,1]. Hyperfuzzy sets extend this concept by mapping each element to a nonempty subset of [0,1], thereby capturing both uncertainty and variability in membership degrees. An (m,n)-superhyperfuzzy set further generalizes these ideas by associating each nonempty element of the mth and nth iterated powersets with a nonempty family of subsets of [0,1], enabling the representation of hierarchical and nested forms of imprecision. Decision-making refers to the process of identifying, evaluating, and selecting the most suitable option from multiple alternatives to achieve specified objectives. Fuzzy group decision-making aggregates experts’ fuzzy preference relations to produce collective rankings or to select optimal alternatives. While extensive research has been conducted on hyperfuzzy and superhyperfuzzy sets as well as on fuzzy group decision-making, their integrated framework remains largely unexplored. Motivated by this gap, the present study investigates the formulation, properties, and potential applications of hyperfuzzy and superhyperfuzzy group decision-making.
Copyright (c) 2025 Takaaki Fujita (Author) https://creativecommons.org/licenses/by/4.0
Dynamic Ergonomic Risk Assessment with REBA and Fuzzy Multi-Criteria Decision Making: Addressing Repetitive Movements
Repetitive work tasks are a prominent cause of musculoskeletal disorders (MSDs) that generate losses in productivity, absenteeism, and extended health risks. Static ergonomic evaluation tools such as the Rapid Entire Body Assessment (REBA) are very informative but insensitive to varying and dynamic working conditions. With the incorporation of linguistic uncertainty and expert judgment through fuzzy logic, the new model enables more sophisticated risk factor prioritization for various work operating conditions. A case study in a production environment demonstrates the effectiveness of this hybrid model in identifying high-risk postures and guiding proactive ergonomic countermeasures. Repetitive work activities are responsible for a considerable percentage of MSDs, which adversely impact worker health, production levels, and organizational efficiency. Conventional ergonomic analysis instruments like REBA are useful in ascertaining posture-related risks but are generally lacking in depicting the dynamic and hazardous nature of real working conditions. This study aims to develop an advanced ergonomic risk assessment model by integrating REBA and Fuzzy Multi-Criteria Decision Making (FMCDM) to provide a more flexible and comprehensive evaluation of hazards in repetitive tasks within an industrial setting. The model combines traditional REBA scoring with fuzzy logic to handle linguistic imprecision and expert judgment. Major ergonomic parameters such as posture risk, repetition frequency, and applied force are examined in fuzzy settings to better replicate real-time variability. A case study was conducted in a factory on three repeated tasks—lifting boxes, sorting products, and labeling goods—to test the efficiency of the model. The integrated REBA-FMCDM methodology successfully identified high-risk postures and tasks, allowing for more detailed prioritization of ergonomic interventions. The fuzzy logic model enabled the use of expert opinion and context variation, providing superior and adaptive decision-making compared to static assessments. The dynamic REBA-FMCDM model offers a straightforward yet robust ergonomic risk assessment tool for environments with variability and subjectivity. By adding fuzzy logic to traditional evaluation methods, the model improves risk prioritization and enables timely and effective implementation of occupational health and safety interventions.
REBA (Rapid Entire Body Assessment); Fuzzy Multi-Criteria Decision Making (FMCDM); Repetitive Movements; Musculoskeletal Disorders (MSDs), Occupational Health and Safety, Risk Assessment, Human Factors Engineering, Dynamic Decision-Making
Copyright (c) 2025 Safiye Turgay, Samet Özyurt (Author) https://creativecommons.org/licenses/by/4.0
Diagnosis of Coronary Heart Disease Through Fuzzy Information Measures and Pattern Recognition-Based Segmentation and Localization in Computed Tomography Angiography
Timely detection of coronary heart disease (CHD) is both significant and challenging. Advanced computational techniques such as similarity measures (SMs), entropy measures, and distance-measure-based segmentation can greatly enhance the precision, speed, and reliability of computed tomography angiography (CTA) image interpretation, thereby supporting more effective early detection and management of CHD. The concept of the interval-valued spherical fuzzy set (Iv-SFS) serves as a powerful tool for handling complex and ambiguous information, as it allows the representation of membership, abstinence, and non-membership values in the form of intervals. Building on the structure of Iv-SFS, this study introduces novel SMs, including interval-valued spherical fuzzy cosine SMs (Iv-SFCSM), Iv-SF cosine weighted SMs (Iv-SFCWSM), Iv-SF dice SMs (Iv-SFDSM), and Iv-SF dice weighted SMs (Iv-SFDWSM). Several axioms of SMs are demonstrated to verify the validity of the proposed measures. The developed SMs are then applied to solve a multi-attribute decision-making (MADM) problem for CHD diagnosis. Key features of the proposed approach are discussed, and comparative analyses with existing SMs highlight its advantages. The paper concludes with remarks underscoring the effectiveness and applicability of the proposed framework.
Multi-attribute decision-making sciences
Copyright (c) 2025 Muhammad Rizwan Khan, Kifayat Ullah, Imran Siddique (Author) https://creativecommons.org/licenses/by/4.0
Invisible Walls to Green Mobility: Barriers to Sustainable Transportation in Izmir
The need for sustainable transportation has come to the forefront due to rapidly increasing population growth in metropolitan areas, increasing motor vehicle use, and deepening environmental problems. Izmir, the third-largest city in Turkey, has faced multidimensional transportation problems such as traffic congestion, air pollution, inadequate public transportation infrastructure, and social injustice in transportation. In order to solve these problems, it is necessary to systematically identify and prioritize the barriers to sustainable transportation throughout the city. In this study, the SWARA (Step-wise Weight Assessment Ratio Analysis) method, a popular Multi-Criteria Decision-Making (MCDM) method, was used to evaluate the barriers to sustainable transportation in Izmir under five different main dimensions: “environmentally friendly transportation,” “inclusive and accessible transportation,” “public transport quality,” “governance and digitalization,” and “safe urban mobility.” This method was applied to evaluate barriers to sustainable transportation in Izmir. The findings revealed that geographical/topographical constraints, lack of funding for accessibility, lack of digital infrastructure, and lack of emergency notification systems are the most important barriers to the development of sustainable transportation. This study provides a quantitative prioritization framework for decision-makers in developing sustainable transportation strategies in Izmir and highlights the need for holistic, data-driven planning approaches. As a result, the prioritized barriers and constraints identified contribute to the strategic planning and roadmap development in sustainable transportation for relevant stakeholders, especially Izmir's transportation department.
Copyright (c) 2026 Damla Çilingir, Hazel Hatice Öztürk, Kayapinar Kaya (Author) https://creativecommons.org/licenses/by/4.0
Digital Innovation Performance Evaluation of European Union Member and Candidate Countries with IDOCRIW and CRADIS Methods
This study evaluates the digital innovation performance of European Union (EU) members and candidate countries by applying a novel integrated multi-criteria decision-making (MCDM) approach. Specifically, the IDOCRIW method is employed to determine objective weights for digital innovation indicators derived from the Global Innovation Index (GII) 2024 report, while the CRADIS method is utilized to assess and rank country performance. The research incorporates indicators directly related to digital innovation, including ICT use, software spending, mobile app creation, GitHub activity, high-tech and ICT exports, mobile application creation, e-participation, and university–industry collaboration. The results highlight significant disparities in digital innovation performance among the analysed countries. Cyprus, Ireland, and the Netherlands emerged as the top performers, whereas Albania, Bosnia and Herzegovina, and Georgia ranked lowest. The integrated model provides a robust and transparent framework for comparative innovation analysis, offering valuable insights for policymakers aiming to enhance digital innovation capacity.
Copyright (c) 2026 Kevser Arman, Nilsen Kundakcı, Ali Katrancı (Author) https://creativecommons.org/licenses/by/4.0
Dynamic Aggregation Operators for Selection of Optimal Communication System in the Rescue Department Under Complex q-rung Orthopair Fuzzy Environment
Selecting the advanced communication system for the rescue department is pivotal for enhancing coordination, operations, and effectiveness in today’s dynamic environment. Addressing the complexities arising from uncertainty and periodicity, the Complex q-rung Orthopair Fuzzy Set theory emerges as adept, encapsulating comprehensive problem specifications. This study introduces two innovative aggregation operators within the Cq-ROFS framework: the Complex q-rung Orthopair Fuzzy Dynamic Weighted Averaging (Cq-ROFDWA) and the Complex q-rung Orthopair Fuzzy Dynamic Weighted Geometric (Cq-ROFDWG) operators. Some important characteristics of the newly defined operators are established. Moreover, these operators contribute to a systematic framework for handling Multiple Attribute Decision Making (MADM) problems involving complex q-rung Orthopair fuzzy information. The article exemplifies their application in resolving a MADM problem, determining the optimal option of an advanced communication system. Finally, to validate the derived methodologies, a thorough comparison study is carried out, demonstrating the superiority of the presented operators against various existing operators.
Copyright (c) 2026 Fakiha Ijaz, Muhammad Azeem, Jawad Ali (Author) https://creativecommons.org/licenses/by/4.0
The Hyperfuzzy VIKOR and Hyperfuzzy DEMATEL Methods for Multi-Criteria Decision-Making
Handling uncertainty in information systems remains an active area of research, with foundational frameworks such as fuzzy sets, rough sets, intuitionistic fuzzy sets, neutrosophic sets, hyperneutrosophic sets, and plithogenic sets, among others. In particular, Hyperfuzzy Sets and SuperHyperfuzzy Sets offer a powerful means to capture multi-layered uncertainty. At the same time, fuzzy set–based decision-making techniques—such as the Analytic Hierarchy Process (AHP), DEMATEL, VIKOR, TOPSIS, and other MCDM methods—have proven highly effective in practice. In this paper, we introduce novel formulations of Hyperfuzzy VIKOR and Hyperfuzzy DEMATEL that extend the classical Fuzzy VIKOR and Fuzzy DEMATEL methods. We further develop corresponding extensions built upon the SuperHyperfuzzy Set paradigm, laying the groundwork for richer, hierarchically structured decision-making models.
Copyright (c) 2026 Takaaki Fujita (Author) https://creativecommons.org/licenses/by/4.0
Vaccine Behaviour and the Perception of Vaccine Among Marginalised Population in India: An Exploratory Study
The COVID-19 vaccine arrested the rapid spread of the virus and saved the lives of millions of people across the world. India was one of the worst hit countries by the pandemic. Vaccination was the only way to curb the virulence of the virus. The broad objective of the study was to understand vaccine behaviour and the perception regarding vaccines among marginalized populations. The study employed mixed-method research using an especially Semi-structured Questionnaire developed by the investigators, including open-ended and closed-ended questions. The findings revealed that 94.6% of study subjects had taken the vaccines. Among them, 10.8% had taken the first dose, 10.8% had taken the booster dose, and 78.4% had taken the second one. The thematic analysis helped the researchers ascertain the factors that facilitate the use of vaccines by many study subjects, including their positive perception of their effectiveness. An overwhelming number of them (87.3%) felt that it helped the community to remain safe and helped them to return to everyday work life. A small percentage of people did not take it due to fear of negative consequences, not believing in COVID-19, lack of awareness, displacement, and lack of accessibility. Non-availability of booster dose, lack of understanding about its availability, waiting for the due date, conformity behaviour and after effects were predominant factors for low intake of booster dose among the study subjects. Informal discussions disclosed some myths attached to vaccines, including the non-existence of COVID-19, lack of faith in vaccines, the adverse impact of vaccines on the reproductive capacity of an individual, and so on. Based on the findings, the researchers suggested several key measures to deal with similar situations in future.
Copyright (c) 2026 Sibnath Deb, P Swathisha, Shayana Deb, Shaik Salman Asharaf, Kattur Soundarapandian Ravichandran (Author) https://creativecommons.org/licenses/by/4.0
Digitization in the Teaching Process: The Significance of Digital Tools Integration
Digitization, as the fourth industrial revolution, has stimulated changes in all areas of life. This paper highlights the importance of digitizing the teaching process, the evolving role of teachers, and the challenges they face in creating and implementing pedagogical-didactic models in a digital environment. To address these challenges, the DigCompEdu model was presented, which is primarily designed to help teachers develop digital competencies at all levels of education. The study was conducted on a sample of 221 students from the Autonomous Province of Vojvodina (Republic of Serbia) to evaluate their satisfaction with the digitization of the teaching process, aligned with the Strategy for Education Development until 2030. The results indicate that students generally support the digitalization of teaching, although the degree of satisfaction varies across different student categories. Based on the findings, it can be concluded that the digitization of teaching in the Republic of Serbia yields satisfactory results, and its continued progress requires ongoing upskilling and reskilling of teachers in this domain.
Copyright (c) 2026 Nataša Papić-Blagojević, Željko Račić (Author) https://creativecommons.org/licenses/by/4.0
An Interval-Valued Circular Intuitionistic Fuzzy MARCOS Method for Renewable Energy Source Selection
The global shift toward renewable energy has heightened the demand for precise decision-making frameworks that can effectively handle uncertainty in complex, multi-criteria evaluations. Traditional approaches often fall short in capturing the nonlinear hesitation and interval-based uncertainty present in real-world assessments. To address this challenge, this study introduces a novel decision-making framework that integrates interval-valued circular intuitionistic fuzzy sets (IVCIFSs) with the Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method. This hybrid approach effectively captures both interval-based uncertainty and nonlinear hesitation in expert judgments, making it particularly suitable for evaluating renewable energy sources (RES), where subjective assessments and conflicting criteria are common. To validate its practical utility, the framework was applied to a case study involving 10 RES alternatives across 7 criteria for a mid-sized Iranian food manufacturer. The analysis identified Economic Feasibility, Technical Viability, and Environmental Impact as critical criteria, with photovoltaic, biomass, and biodiesel emerging as the most favorable options. Comprehensive sensitivity and comparative analyses confirmed the model’s robustness across varying expert perspectives and its alignment with advanced multi-criteria decision-making (MCDM) methods. This study not only advances decision-making theory through the IVCIF-MARCOS integration but also offers a practical, adaptable tool for supporting effective energy transitions in uncertain and complex environments.
Interval-valued circular intuitionistic fuzzy sets
Copyright (c) 2026 Vahideh Shahin, Moslem Alimohammadlou, Dragan Pamucar (Author) https://creativecommons.org/licenses/by/4.0
Assessing and Prioritizing Construction Contracting Risks with an Extended FMEA Decision-Making Model in Uncertain Environments
This study aims to enhance risk assessment in construction contracting by addressing the limitations of traditional Failure Modes and Effects Analysis (FMEA), particularly its inability to effectively handle uncertainty and prioritize risks with equal Risk Priority Numbers (RPNs). To overcome these issues, a hybrid decision-making framework was developed by integrating fuzzy logic with the Step-wise Weight Assessment Ratio Analysis (SWARA) and Multi-Attributive Border Approximation Area Comparison (MABAC) methods. The proposed model enables more accurate weighting of risk criteria and prioritization of risks under uncertain conditions. A case study involving 18 identified construction contracting risks demonstrated the model’s effectiveness, with key risks such as inadequate technical skills and poor contract conditions ranked highest due to their severe impact on cost and project outcomes. The hybrid approach improved the clarity and consistency of risk prioritization compared to traditional methods. This research contributes a flexible and practical tool for managing risks in complex construction environments. Future studies are encouraged to explore integration with real-time project monitoring systems and the application of this model across different infrastructure domains.
Copyright (c) 2026 Mahdieh Mohammadi, Sara Sarvi, Saied Jafarzadeh Ghoushchi (Author) https://creativecommons.org/licenses/by/4.0
A Hybrid MCDM Approach for SDGs Assessment of EU Countries
This study assesses the progress of EU countries in achieving the Sustainable Development Goals (SDGs) using Multi-Criteria Decision-Making (MCDM) methods. The evaluation covers 27 countries and is based on 15 indicators for the 2019–2021 period. The MPSI-RAPS model was employed to determine indicator weights and rank the alternatives. To ensure robustness, a comprehensive sensitivity analysis was conducted across 16 scenarios. The final rankings from the MPSI-RAPS model identified Sweden, the Netherlands, and Germany as the top-performing countries in sustainable development progress. Notably, rankings varied significantly across scenarios, underscoring the impact of methodological choices on outcomes.
Copyright (c) 2026 Nazlı Ersoy, Mehmet Özçalıcı, Do Duc Trung (Author) https://creativecommons.org/licenses/by/4.0
Advanced Decision-Making with Complex q-Rung Orthopair Fuzzy Muirhead Mean Models
Reliable and adaptable approaches are essential in MADM to address the complexities of medical diagnosis. To enhance decision-making (DM) processes, we propose a novel method combining the Muirhead mean (MM) with q-rung orthopair fuzzy sets (q-ROFS). The q-ROFS framework offers an advanced solution for handling ambiguity and vagueness in medical diagnoses by enabling a three-dimensional representation of expert opinions through membership degrees (MDs). The proposed method leverages MM's strength in capturing interrelationships among features, ensuring a more accurate and balanced integration of expert inputs. This integration is particularly valuable in medical diagnosis, where the interplay and relative importance of symptoms and diagnostic criteria are complex and critical. By expanding the theoretical applications of fuzzy sets (FS) in MADM, this innovative approach not only improves patient outcomes but also enhances the reliability of diagnostic procedures. It provides a practical tool for elevating DM quality in medical settings.
Copyright (c) 2026 Abrar Hussain, Chaudhary Amir Raza, Kifayat Ulllah (Author) https://creativecommons.org/licenses/by/4.0
Data Security and Privacy Concerns in Digitized Medical Services: Implications for Malpractice Risk Management
The growing digitization of medical services has transformed patient care while also raising serious data security and privacy concerns in the healthcare business. The ramifications of these problems on malpractice risk management are examined in this research article. The study investigates vulnerabilities, such as cyberattacks and insider threats, and their possible effects on patient trust, provider reputation, and the financial stability of healthcare facilities through a comprehensive literature analysis. It also dives into the legal and regulatory consequences, such as compliance gaps and penalties for noncompliance. Case studies and real-life examples demonstrated the value of open privacy policies and informed consent procedures. Strategies for improving data security are addressed, including encryption, strong authentication measures, and employee training, as well as ethical issues for data management and sharing. The study examines the current regulatory environment, focusing on rules such as HIPAA, and provides practical recommendations for healthcare businesses to strengthen data security and reduce malpractice risk. The study continues by underlining the crucial significance of data protection in preserving patient trust and ensuring compliance with shifting legislation, making it an essential component of malpractice risk management in the age of digitized medical services.
Copyright (c) 2026 Shankha Shubhra Goswami, Surajit Mondal (Author) https://creativecommons.org/licenses/by/4.0
An Integrated Hybrid MCDM Framework for Renewable Energy Prioritization in Sustainable Development
Switching to renewable energy is essential for reducing carbon emissions, enhancing energy security, and promoting sustainability. However, the decision-making process is complex, influenced by conflicting factors, uncertainties in expert judgments, and data variability. Existing methodologies often struggle to manage these uncertainties, leading to inconsistent outcomes. This study presents a decision-support framework that employs Linear Diophantine Fuzzy Sets (LiDFS) to more accurately model uncertainty and address these challenges. The Ranking Comparison (RANCOM) method is used to calculate subjective weights, offering a structured, expert-driven approach. In contrast, the Method based on the Removal Effects of Criteria (MEREC) determines objective weights, reducing bias and enhancing reliability. The Multi-Attribute Utility Theory (MAUT) method is then applied to systematically rank available options based on their overall utility. Sensitivity analysis evaluates the impact of weight variations on decision outcomes, while comparative analysis confirms the robustness of the proposed approach. This integrated framework provides a transparent and scientifically grounded methodology to support decision-makers in selecting optimal solutions within the renewable energy sector, effectively addressing uncertainty and conflicting priorities.
Copyright (c) 2026 Toqeer Jameel, Yasir Yasin, Muhammad Riaz (Author) https://creativecommons.org/licenses/by/4.0
An Enhanced Spherical Cubic fuzzy WASPAS Method and its Application for the Assessment of Service Quality of Crowdsourcing Logistics Platform
The service quality of crowdsourcing logistics platforms (CLP) represents the fulfillment of standardized requirements during service delivery. Evaluating this quality enables quantification of service performance, identification of deficiencies in key indicators and optimization of operational efficiency. Such assessment helps standardize courier behavior, enhance user satisfaction, and strengthen platform competitiveness. This study proposes an enhanced decision-making method for assessing CLP service quality under uncertainty using spherical cubic fuzzy (SCF) sets, which effectively capture fuzzy and uncertain information. We develop an SCF-based approach integrating Renyi entropy and the WASPAS method, featuring four key contributions: (1) definition of SCF Aczel-Alsina operations and four corresponding aggregation operators (weighted averaging, geometric, ordered weighted averaging, and ordered weighted geometric); (2) development of an SCF-Renyi entropy weight method using score functions to determine criteria weights; (3) improvement of the WASPAS method through SCF Aczel-Alsina operators; and (4) validation through a supplier selection case study demonstrating the method's applicability. Sensitivity analysis confirms the approach's stability and universality in handling multi-criteria decision analysis problems.
Service quality of crowdsourcing logistics platform
Copyright (c) 2026 Dongmei Li , Guorou Wan, Yuan Rong (Author) https://creativecommons.org/licenses/by/4.0
Consumer Behavior Analysis in Digital Space with Emphasis on Brand Storytelling: A Bibliometric Study from 2015 to 2024
This study presents a comprehensive bibliometric analysis of brand storytelling and its impact on consumer behavior in digital environments, mapping the evolution of research trends from 2015 to 2024. By analyzing 485 articles from Scopus and Web of Science using VOSviewer and CiteSpace, we identify key research clusters, influential authors, and emerging themes. The findings reveal that brand storytelling has transitioned from a peripheral marketing tactic to a central strategy for fostering emotional connections, trust, and loyalty in digital spaces. Notably, highly cited works by Lundqvist et al. [4] and Hollebeek et al. [16] underscore storytelling’s role in enhancing consumer engagement, while emerging trends highlight the integration of AI, AR/VR, and platform-specific narratives (e.g., TikTok, Instagram). Three critical gaps are identified: (1) the underexplored ethical implications of AI-generated storytelling, (2) the need for cross-cultural adaptations in global campaigns, and (3) the lack of standardized metrics to measure storytelling ROI. The study also demonstrates how immersive technologies (AR/VR) and social media algorithms are reshaping narrative personalization, with cases like IKEA’s AR app and Coca-Cola’s localized "Share a Coke" campaigns exemplifying best practices. This research contributes to theory by systematizing fragmented literature through bibliometric visualization, while offering practitioners actionable insights for leveraging storytelling in digital marketing. Its uniqueness lies in combining quantitative bibliometric rigor with qualitative analysis of cutting-edge trends, providing a dual lens to evaluate past achievements and future directions.
Copyright (c) 2026 Amir Mehdiabadi (Author) https://creativecommons.org/licenses/by/4.0
Application of Reliability-Centered Maintenance with a Computerized Maintenance Management System for the Wheelset in Rolling Stock
Maintenance plays an important role in ensuring the reliability and safety of rolling stock in the railway sector. This study focuses on the application of an integrated Reliability-Centered Maintenance (RCM) and Computerized Maintenance Management System (CMMS) for wheelsets in a wheel profiling measurement system. The combined use of RCM and CMMS helps optimize key reliability indices, such as Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and Availability. The results demonstrate that automation enhances performance, with MTBF increasing from 500 to 800 hours and MTTR decreasing from 8 to 4 hours. Additionally, CMMS improved inventory management efficiency from 60% to 90% and reduced emergency repairs, resulting in annual savings of ₹2,325,000. Despite the higher initial investment of ₹6,375,000, automation proved more cost-effective over a five-year period. Furthermore, system availability increased from 97.3% to 99.1%, minimizing downtime and improving operational efficiency. These findings have been shared with the maintenance manager of the system under study for further implementation and testing.
Copyright (c) 2026 Meenakshi, Dilbagh Panchal, Dixit Garg (Author) https://creativecommons.org/licenses/by/4.0
Ranking of Factors Affecting Transformational Leadership in Organizations: Application of AHP Method in Optimizing Leadership Strategies
Transformational leaders play a crucial role by introducing new ideas and visionary pathways, fostering organizational growth, enhanced performance, and competitive advantage. This study identifies and ranks the key factors influencing transformational leadership at Arak University of Medical Sciences. This descriptive-analytical, survey-based research targeted experts at Arak University of Medical Sciences, all of whom held doctoral degrees and had at least five years of experience in senior organizational management. Data collection began with factor identification using the Delphi method, followed by application of the Analytic Hierarchy Process (AHP) technique, with computations performed according to AHP's algorithmic formulation. The analysis revealed that transformational leadership not only guides organizational direction but also empowers and develops employees by identifying latent talents and fostering growth. The study identified and prioritized key influencing factors, providing managers with a framework to improve organizational performance and cultivate a culture of transformational change. The findings suggest that organizations and managers should focus on essential elements of transformational leadership while enhancing leadership skills to drive positive change and innovation. Recognizing these factors can significantly help universities and other institutions strengthen their competitive advantage and improve organizational effectiveness.
Copyright (c) 2026 Ameneh Malmir (Author) https://creativecommons.org/licenses/by/4.0
A Queuing Approach to Pricing Strategy in B2B Markets
Business-to-business (B2B) pricing strategies are intentionally complex and crucial levers of profitability and competitive advantage because of differences in temporal preferences, operational constraints, and multi-stakeholder decision-making. Static segmentation also neglects behavioral subtleties that correspond with service urgency; meanwhile, traditional models provide an inadequate representation of time-sensitive variables, such as delivery lead time and capacity utilization. This paper addresses such shortcomings by establishing a new and holistic framework that combines queuing theory and value-based pricing for the revenue optimization of a capacity-constrained B2B system. Related models combine game-theoretical frameworks, data-driven predictions with algorithms, and adaptive capacity control, through which firms can modulate prices in real time whenever requests, service times, and competitors' behaviors deviate. The results show that pricing, which considers temporal preferences and operational efficiency, reduces the conflict between service speed and profitability and offers insights into tiered pricing and resource allocation. Uniting queuing dynamics with segmentation strategies, the framework bolsters intellectual dialogue in calls for the creative development of B2B pricing literature. It allows managers to build customer loyalty, exploit time-sensitive demand, and mitigate margin erosion due to panic discounting.
Copyright (c) 2026 Fatemeh Ghasemzadeh, Babak Ghahremannasab (Author) https://creativecommons.org/licenses/by/4.0
Supplier Selection in the Food Sector: An Integrated Approach Using LODECI and CORASO Methods
As a major component of the world economy, food affects human health, and food safety is an important factor in sustainable development. Variables including population increase, changing consumer preferences, and climate change have made the effective management of the food supply chain ever more important. In this regard, supplier choice presents a multi-criteria decision-making difficulty that directly affects the strategic success of companies. This study identifies the best supplier for a milk and dairy manufacturing company using the LODECI and CORASO approaches. These two approaches, taken simultaneously for the first time, provide a more rigorous and exact assessment tool for decision-makers. Five main categories define the criteria developed in the study: cost, quality, delivery, financial strength, and capacity. These five factors comprise eleven sub-criteria, using which fifteen milk suppliers were evaluated. The findings show that whilst the MSU4-coded milk supplier showed the worst performance, the MSU2-coded milk supplier was determined to be the best milk supplier. Using comparison with other multi-criteria decision-making (MCDM) techniques, the accuracy of the CORASO approach was assessed, and it is shown to be a reliable method with high correlation coefficients.
Copyright (c) 2026 Didem Tufan, Alptekin Ulutaş (Author) https://creativecommons.org/licenses/by/4.0
Synergistic Effects of Lean Manufacturing and Digitalization on Operational Effectiveness: A Comprehensive Review
In today’s industrial landscape, lean manufacturing and digitalization are key strategies for enhancing efficiency. Lean manufacturing minimizes waste and optimizes processes, while digitalization leverages IoT, AI, and big data to streamline operations and improve decision-making. This review explores their integration across industries like manufacturing, healthcare, and logistics. The combination of lean principles and digital tools enhances productivity, reduces costs, and improves efficiency. Organizations benefit from real-time monitoring, predictive maintenance, and automation, leading to smoother workflows and less downtime. AI-driven analytics help identify inefficiencies, while digital twins enable real-time simulations for optimizing processes. IoT-enabled smart factories support continuous data collection, providing insights that align with lean objectives. This synergy fosters continuous improvement, enabling businesses to adapt swiftly to market changes and customer demands. However, challenges such as cultural shifts, cybersecurity risks, and initial investment costs must be addressed. By analyzing successful case studies, this review highlights strategies to overcome these barriers, reinforcing the importance of integrating lean manufacturing and digitalization to remain competitive in a technology-driven environment.
Copyright (c) 2026 Surajit Mondal, Shankha Shubhra Goswami, Krishna Kumar Gupta, Sushil Kumar Sahoo (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Supplier Selection in Public Manufacturing: A Hybrid Multi-Criteria Decision-Making Approach
In today's business world, outsourcing is a key strategy for organizations aiming to improve efficiency, reduce costs, and increase transparency. By using external experts, companies can quickly gain necessary resources and knowledge to better handle operational challenges. Therefore, choosing the right suppliers and contractors for manufacturing is essential. This study examines how public manufacturing organizations select suppliers and contractors, highlighting the importance of setting clear evaluation criteria before making selections. Following the Iranian Public Procurement Regulations, a strong set of criteria was created for effective supplier assessment. A combined Multi-Criteria Decision-Making (MCDM) approach was employed, integrating the Fuzzy DEMATEL method to analyze relationships among selection criteria, and the Best-Worst Method (BWM) to assign weights, with the best and worst criteria derived from the results of the Fuzzy DEMATEL analysis. The weighted criteria were then applied using three different MCDM techniques—COCOSO, MOORA, and TOPSIS—in a case study to rank potential suppliers. The results showed strong agreement among the methods, confirming the reliability of the evaluation framework and boosting stakeholder confidence in decision-making. This study demonstrates the effectiveness of MCDM strategies in promoting informed and strategic procurement decisions, ultimately leading to better operational efficiency and alignment with organizational goals in the public sector.
Copyright (c) 2026 Hossein Rahimi Kolour, Vahid Momayezi, Farid Momayezi (Author) https://creativecommons.org/licenses/by/4.0
Strategic Assessment of IoT Technologies in Healthcare: Grey MCDM Approach
The integration of Internet of Things (IoT) technologies in healthcare has revolutionized patient monitoring, data analytics, and remote medical device management, significantly improving healthcare delivery. However, selecting the most appropriate IoT technologies in the healthcare sector involves evaluating multiple criteria under conditions of uncertainty. This study addresses this challenge by utilizing the Grey Decision-Making Trial and Evaluation Laboratory (Grey DEMATEL) method to analyze the key criteria influencing IoT technology selection in healthcare. Eight critical criteria, including security, data privacy, technical performance, usability, scalability, and economic factors, were identified and examined for their causal relationships. The results show that security and data privacy are the most influential factors, followed by technical performance and usability. The findings offer valuable insights for decision-makers, assisting them in prioritizing crucial aspects for successful IoT implementation in healthcare. This research contributes to the field by providing a structured decision-making framework that enhances the effective integration of IoT technologies while addressing security, efficiency, and regulatory concerns.
Copyright (c) 2025 Gülay Demir (Author) https://creativecommons.org/licenses/by/4.0