In the realm of understanding consumer purchasing behaviors and refining decision-making across diverse sectors, Market Basket Analysis (MBA) emerges as a pivotal technique. Traditional algorithms, such as Apriori and Frequent Pattern Growth (FP-Growth), face challenges with computational efficiency, particularly under low minimal support settings, which precipitates an excess of weak association rules. This study introduces an innovative approach, termed Customer-Centric (CC)-MBA, which enhances the identification of robust association rules through the integration of consumer segmentation. By employing Recency, Frequency, and Monetary (RFM) analysis coupled with K-means clustering, customers are categorized based on their purchasing patterns, focusing on segments of substantial value. This targeted approach yields association rules that are not only more relevant but also more actionable compared to those derived from conventional MBA methodologies. The superiority of CC-MBA is demonstrated through its ability to discern more significant association rules, as evidenced by enhanced metrics of support and confidence. Additionally, the effectiveness of CC-MBA is further evaluated using lift and conviction metrics, which respectively measure the observed co-occurrence ratio to that expected by chance and the strength of association rules beyond random occurrences. The application of CC-MBA not only streamlines the analytical process by reducing computational demands but also provides more nuanced insights by prioritizing high-value customer segments. The practical implications of these findings are manifold; businesses can leverage this refined understanding to improve product positioning, devise targeted promotions, and tailor marketing strategies, thereby augmenting consumer satisfaction and facilitating revenue growth.
This study was undertaken to elucidate the influence of self-management on the productivity levels of personnel within the Water and Wastewater Department, District 2, Tehran, utilizing a descriptive survey method that engaged 119 respondents. The assessment was founded on the administration of meticulously validated questionnaires, with subsequent statistical analysis conducted using Statistical Package for the Social Sciences (SPSS). The analysis included the Kolmogorov-Smirnov test to confirm the normal distribution of the variables, namely, self-management strategies and productivity levels, and the Pearson-Spearman tests to evaluate correlations. The findings, underscored by Cronbach's Alpha values of 0.879 for self-management strategies and 0.906 for productivity levels, confirmed the hypothesis of a significant positive impact of self-management on workforce productivity. Notably, the natural reward strategy was identified as having the least effect on ameliorating workplace conditions. This investigation contributes to the body of knowledge by highlighting the critical role of self-management practices in enhancing the efficiency of public sector operations. The insights garnered from this study pave the way for the implementation of strategic self-management practices aimed at boosting productivity within public sector entities.
The pressing need to reduce reliance on petroleum in the energy sector and the increasing demand for environmental protection are driving research and practical endeavors in the management of renewable supply chains. Professionals, global institutions and scholars have widely acknowledged the importance of studying the correlation, between the performance of supply chains and renewable energy sources. It's important to delve into the articles in terms of the methodologies that have been used, the principal concerns addressed, the specific renewable energy sources focused on, and the performance indicators employed to optimize supply chains for renewable energies. This paper provides an analysis that improves the understanding of research in the realm of quantitative decision making for renewable energy supply chains. The analysis commences by searching for articles published. Subsequently, they are narrowed down to those that are most relevant. The article also addresses knowledge gaps in the literature. The findings provide a reference for researchers who are considering conducting studies in this area.
Confidence sets provide a robust method for addressing the uncertainty inherent in the membership degrees of elements within fuzzy sets (FSs). These sets enhance the capability of FSs to manage imprecise or uncertain data systematically. Analogous to repeated experimentation, the interpretation of confidence sets remains valid before sample observation. However, once the sample is examined, all confidence sets exclusively encompass parameter values of either 1 or 0. This study introduces novel techniques in the domain of confidence levels, specifically the Confidence Complex Polytopic Fuzzy Weighted Averaging (CCPoFWA) operator, confidence complex polytopic fuzzy ordered weighted averaging (CCPoFOWA) operator, and Confidence Complex Polytopic Fuzzy Hybrid Averaging (CCPoFHA) operator. These aggregation operators are indispensable tools in data analysis and decision-making, aiding in the understanding of complex systems across diverse fields. They facilitate the extraction of valuable insights from extensive datasets and streamline the presentation of information to enhance decision support. The efficacy and utility of the proposed methods are demonstrated through a detailed illustrative example, underscoring their potential in strategic decision-making for the placement of nuclear power plants in Pakistan.
The COVID-19 pandemic has prompted extensive modeling efforts worldwide, aimed at understanding its progression and the myriad factors influencing its spread across diverse communities. The necessity for tailored control measures, varying significantly by region, became apparent early in the pandemic, leading to the implementation of diverse strategies to manage the virus both in the short and long term. The World Health Organization (WHO) has faced considerable challenges in mitigating the impact of COVID-19, necessitating adaptable and localized public health responses. Traditional mathematical models, often employing classical integer-order derivatives with real numbers, have been instrumental in analyzing the virus's spread; however, these models inadequately address the fading memory effects inherent in such complex scenarios. To overcome these limitations, fuzzy sets (FSs) were introduced, offering a robust framework for managing the uncertainty that characterizes the pandemic’s dynamics. This research introduces innovative methods based on complex Fermatean FSs (CFFSs), alongside their corresponding geometric aggregation operators, including the complex Fermatean fuzzy weighted geometric aggregation (CFFWGA) operator, the complex Fermatean fuzzy ordered weighted geometric aggregation (CFFOWGA) operator, and the complex Fermatean fuzzy hybrid geometric aggregation (CFFHGA) operator. These advanced techniques are proposed as effective tools in the strategic decision-making process for reducing the spread of COVID-19. A compelling case study on COVID-19 vaccine selection was presented, demonstrating the practical applicability and superiority of these methods, effectively bridging theoretical models with real-world applications.
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