Journal of Data Analytics and Intelligent Decision-making

One of the key criteria for assessing the durability of a system is evaluating the probability of its performance over a specified time period, which is refered to as reliability. This probability depends on the performance of the system's components, and the failure rate of each component impacts this issue significantly. In most related studies, it is assumed that the failure rates of components remain constant over time; however, this rate is often not constant due to various factors. A common pattern for changes in failure rates over time is known as the bathtub curve, where the failure rate is initially high, decreases over a period, and then eventually increases again. The early life period, during which the failure rate of components is initially high and then decreases, can lead to early failures and a reduction in the probability of component performance. By conducting controlled experiments before the practical use of a system, this initial period can be eliminated, thereby preventing early failures, which in turn can lead to improved system reliability. This paper examines the impact of eliminating the early life period on system reliability. The results indicate that the elimination of this period does not necessarily lead to improved reliability under all conditions, as it depends on specific parameters. This paper will analyze the sensitivity of these parameters and the conditions under which eliminating the early life period can improve reliability.

Human beings have various needs, including the essential, daily need for food. Edible goods, such as many other products, reach customers through supply chains. However, due to the nature of food items, quality deterioration is unavoidable for this group of products throughout the supply chain. This decline in quality can result from factors such as time and environmental conditions. In any case, this quality deterioration affects customers' willingness to purchase the product. The resulting decrease in demand increases product accumulation, leading to increased product spoilage. Some sellers use increasing discounts to stimulate demand and prevent product spoilage. The extent of encouragement and hesitation created can aid the organization in making better decisions. The present study aims to examine the impact of discounts on changing the level of customer encouragement and hesitation. An integrated decision-making method for calculating the changes in these coefficients is proposed and solved using Microsoft Excel 2016 software. The research results indicate that increasing discounts simultaneously increases customers' encouragement and hesitation toward purchasing the product. However, it sustains customers' willingness to purchase the goods at approximately 48%, in stark contrast to the nearly negligible willingness observed in the no-discount scenario.

This study aims to design a social banking model for the development of micro and home businesses using the system dynamics method, addressing the lack of banking models that guarantee financial interests for both businesses and banks in Iran. The research employs a combination of quantitative (10 years of financial data from Qarz-ol-Hasanah Resalat Bank, 2013-2023) and qualitative (interviews with 15 banking and industry experts) data sources. The system dynamics modeling approach is applied to analyze key variables and relationships in social banking. The results indicate that developing social banking can shift banks away from non-productive investments and business ownership toward financing micro and home businesses. This shift enhances job creation, poverty reduction, sustainable value, and trust in the banking system while strengthening banks through increased public investment. While prior research has examined liquidity issues in manufacturing enterprises, this study uniquely focuses on the role of banks in supporting micro and home businesses through a dynamic social banking model, contributing to both theory and practice in sustainable finance. The proposed social banking model provides a practical framework for banks to reallocate resources from non-productive sectors toward micro and home businesses, fostering job creation, poverty reduction, and sustainable economic growth while enhancing trust in the banking system.

Smart cities leverage advanced technologies such as the Internet of Things (IoT), big data analytics, and automation systems to enhance the quality of life, improve resource efficiency, and reduce operational costs. Effective management of critical infrastructures, including power and energy networks, intelligent transportation, urban services, and data security, remains a significant challenge in the development of these cities. Artificial Intelligence (AI), with its extensive capabilities in big data analysis, energy consumption forecasting, power distribution optimization, and intelligent transportation management, plays a pivotal role in facilitating decision-making processes and enhancing the efficiency of urban systems. Meanwhile, blockchain technology provides a decentralized infrastructure, ensuring transaction transparency and enabling the execution of smart contracts, thereby guaranteeing security and trust in energy data and urban service management. This paper presents a comprehensive review of AI and blockchain applications in power systems and smart cities, analyzing their benefits and limitations, and identifying future research directions. The findings indicate that integrating these emerging technologies can substantially improve network efficiency, reduce operational costs, enhance security, and support sustainable energy management, ultimately contributing to the achievement of sustainable development goals in smart cities. Additionally, challenges related to scalability, interoperability between technologies, and legal and regulatory issues are discussed to provide a clear outlook for future research endeavors.

It is no secret that mathematics has always been a guide to solving many problems in human life. Humans, in their decision-making processes, constantly deal with various variables and indicators, and one of the approaches to analyzing these variables is the use of multi-criteria decision-making methods. However, a significant challenge in applying these techniques is the independence of criteria or indicators relative to one another. One of the modern solutions for achieving independence among variables (or indicators) is the use of the Gram-Schmidt method; however, the accuracy of this algorithm might decline when it is implemented on large-scale vectors. This paper proposes a Developed Gram–Schmidt Algorithm (DGSA). The Schmidt vectors obtained from the proposed algorithm are prone to a lower error rate than those resulting from the Gram–Schmidt algorithm. To demonstrate the superiority of the proposed algorithm, several different numerical examples have also been used. At the end of the research, a case study based on the proposed approach was also conducted at Shazand Oil Refinery (SOR) to demonstrate the applicability of this approach in a real-world example. The findings have shown that the proposed approach has relatively high accuracy.

The rapid growth of urbanization and the increasing use of motor vehicles have intensified transportation challenges, such as congestion and air pollution. In light of this, bicycle-sharing systems have emerged as a sustainable and cost-effective solution. This study presents the design of a bicycle-sharing system for the University of Bojnord, covering an area of approximately 150 hectares with more than 5,000 students. A mixed-integer linear programming model was developed to determine the optimal number and locations of bicycle stations as well as the required bicycles while minimizing total system costs, including installation, purchasing, and maintenance. The model was solved and analyzed using GAMS software. Sensitivity analysis was conducted to evaluate the effects of key parameters, and the most frequent travel routes were identified to enhance system efficiency. The results indicated that implementing the proposed system can significantly improve transportation quality and promote sustainable mobility within the campus. However, successful implementation requires the development of supporting infrastructure, including smart stations and secure parking areas. The proposed framework can also serve as a reference for designing similar systems in other universities and urban areas.