Fuzzy Optimzation and Modeling
Volume:5 Issue: 4, Autumn 2024

This research focuses on the theoretical examination of the fuzzy graph of aromatic hydrocarbon of Anthracene and computes several fuzzy-based degree topological indices. The research introduces new definitions for the first Zagreb fuzzy index, forgotten index, and Y−index, and provides a general formula for fuzzy topology indices of Anthracene hydrocarbon by considering the degree of each edge. Moreover, a general formula is presented to determine the specific topology index of a hydrocarbon-based on the number of rows and columns. We compared the topological indices of linear Anthracene and found that the Randic and harmonic indices had the highest values. Continuing with the drawing of the Randic index surface, we concluded that when the number of parameters in the rows and columns of the Anthracene hydrocarbon is equal, the Randic index has the highest value. This approach can help researchers predict and estimate compounds' physical and chemical properties using topology indices and precise bond lengths and atomic mass calculations through software.

While several studies have addressed technical aspects of vaccine production, a comprehensive ethical framework that integrates individual, organizational, and social dimensions is still missing. This study presents a novel integrated ethical innovation model for human vaccines production, combining fuzzy-based methodology with structural equation modeling to capture both expert knowledge and empirical validation. This research is applied in terms of its purpose and quantitative-qualitative [mixed] in terms of its method. The statistical population consisted of the Razi Vaccine and Serum Research Institute and Pasteur Institute of Iran, both active in the vaccine production domain. The participants in the qualitative phase involved the vaccine production researchers and experts selected by snowball sampling till theoretical saturation was reached. The statistical sample in the quantitative phase included the managers and experts of the vaccine production domain and was selected by purposive and convenient sampling. The Fuzzy Delphi method run in the EXCEL software was used for extracting the variables and presenting the theoretical model, while the structural equations modeling run in the SMART PLS software was employed for factor analysis. The analysis gave rise to 60 initial indices, which were reduced to 53 after screening and Fuzzy Delphi analysis. Then, they were framed into five dimensions, including individual ethics, organizational ethics, supervisory and legal ethics, social ethics, and infrastructures. All identified factors significantly impact ethical innovation in the production of COVID-19 vaccine in the following order: Organizational ethics, supervisory and legal ethics, infrastructures, individual ethics, and social ethics.

In this paper‎,‏‎ we use the least squares method to solve LR fuzzy interval systems by transforming an interval fuzzy number into two triangular fuzzy numbers. Then, ‎w‎e reduce the distance between the two obtained triangular fuzzy numbers to solve the fuzzy ‎LR‎ interval linear system. Essentially, we convert an LR fuzzy interval linear system into a triangular fuzzy linear system and subsequently solve it using the least squares method introduced ‎in [17, 18].

This research presents a robust mathematical model for optimizing hub location for military equipment, addressing the inherent uncertainties associated with logistical operations in defense contexts. The model aims to minimize transportation costs and enhance the efficiency of equipment distribution while considering various uncertainties, such as demand fluctuations, transportation delays, and operational constraints. To solve this complex optimization problem, we employ advanced meta-heuristic algorithms, including Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), which are designed to navigate the solution space effectively and provide high-quality solutions within reasonable computational time. The performance of the proposed model is evaluated through a series of simulations, demonstrating its effectiveness in identifying optimal hub locations that ensure timely and cost-effective delivery of military equipment. The first objective is to minimize costs, the second objective is to maximize the fulfillment of demands, and the third objective is to minimize congestion on the routes. Taking into account the parameters in the state of uncertainty, the mathematical model is modeled in a robust state and a robust counterpart model of the problem is proposed. In order to solve the problem on a small scale, the exact weighted sum method (WSM) is used in GAMS software.  The findings highlight the model's potential to improve logistical decision-making in military operations, ultimately contributing to enhanced operational readiness and resource allocation. This study serves as a foundational framework for future research in military logistics optimization under uncertainty.

Wireless Sensor Networks (WSNs) encounter considerable challenges in terms of energy efficiency and network longevity due to their limited energy resources. This paper proposes a novel hybrid clustering-based routing protocol that addresses these challenges by integrating fuzzy logic for dynamic and adaptive cluster head (CH) selection based on residual energy, node degree, and proximity, and genetic algorithms (GA) for optimising cluster formation by balancing energy consumption and minimising communication distances. The protocol's objectives are threefold: to minimise energy consumption, extend network lifespan, and enhance Quality of Service (QoS).The proposed method was simulated in MATLAB and benchmarked against the LEACH and TEEN protocols. The results demonstrated the protocol's superior performance, achieving a 30% reduction in energy consumption, a 25% increase in network longevity, and higher data reliability. The primary factors contributing to this enhanced performance are the integrated use of fuzzy logic for optimised cluster head selection and genetic algorithms for optimal cluster formation. The findings substantiate the protocol's capacity to substantially enhance the energy efficiency and scalability of WSNs, providing a resilient and pragmatic solution for practical applications in real-world settings.

Cooperative banks, due to their socially oriented nature, play a crucial role in the socio-economic development of local communities, with the expansion of new technologies, making them ideal candidates for the implementation of AI-based social banking. Despite extensive literature on social banking and AI applications separately, there is a significant research gap regarding comprehensive models that specifically integrate AI functionalities with social banking principles in cooperative banking contexts. Furthermore, previous studies have not adequately addressed the specific conditions of developing countries such as Iran, especially considering unique challenges such as international sanctions and local economic constraints. This study adopted a two-stage qualitative approach. First, we systematically reviewed 36 academic articles published between 2014 and 2024 using a meta-synthesis methodology to identify initial dimensions and constructs. Second, we used the Fuzzy Delphi method with 15 banking industry experts to validate and localise the model through three rounds of evaluation, using a 0.7 threshold for final component acceptance. The research results led to the identification of 9 main dimensions and 56 components, with defuzzified values ranging from 0.719 to 0.881. The dimensions include AI technology and infrastructure, social development and community empowerment, financial and economic aspects, management and strategy, legal and regulatory framework, banking products and services, customer centricity, risk and security, and sustainability. Quantitative analysis revealed that components such as localisation of AI technologies (0.754), empowerment of female-headed households (0.769), supply chain financing (0.881), sanctions management (0.787), and alternative foreign exchange services (0.822) received the highest expert consensus, reflecting their critical importance in the Iranian banking context.