In the concept of socio-economic development of Uzbekistan until 2030, a macroeconomic policy aimed at ensuring sustainable economic growth and fighting against poverty, as well as increasing the competitiveness of national exports is recognized as an important economic basis. Successful implementation of the Concept of integrated socio-economic development of the Republic of Uzbekistan until 2030, as well as Development Strategy of New Uzbekistan for 2022-2026 undoubtedly, require significant attention to external factors of economic development, including promotion of competitive exports. Today the issue of regulation of global socio-economic processes is one of the most important issues at the international level. The World Trade Organization (WTO), as a legal and institutional body of the multilateral trading system, is an international organization that enjoys differen advantages in the process of free goods exchange. At present, 164 countries are members of this organization, and the accession of Uzbekistan to the World Trade Organization (WTO) has caused a variety of observations among experts and scientists.
Read MoreDoi: https://doi.org/10.54216/JSDGT.030201
Vol. 3 Issue. 2 PP. 08-24, (2023)
As global supply chains become increasingly complex and environmentally conscious, the imperative for Sustainability-Driven Decision-Making (SDDM) gains paramount importance. This paper delves into the transformative potential of machine learning in reshaping sustainability practices within supply chains. Leveraging a diverse dataset encompassing provisioning, production, sales, and commercial distribution across clothing, sports, and electronic supplies, we employ a range of machine learning algorithms, including Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, k-Nearest Neighbors, Linear Discriminant Analysis, Random Forest, Extra Trees, XGBoost, and Decision Trees. Our analysis spans critical dimensions of supply chain management, from fraud detection to late delivery prediction, and illuminates the pivotal role of these algorithms in improving sustainability outcomes. Through empirical experimentation, we identify optimal models for each task, revealing their strengths and limitations. Additionally, we visualize feature importance, offering insights into the factors shaping sustainability within supply chains. Our research underscores the symbiotic relationship between data-driven decision-making and sustainable practices, paving the way for more responsible, efficient, and resilient supply chains. As businesses seek to navigate an evolving landscape, the fusion of machine learning and sustainability emerges as a compelling paradigm, fostering a future where supply chains not only optimize operations but also contribute to global sustainability goals.
Read MoreDoi: https://doi.org/10.54216/JSDGT.030202
Vol. 3 Issue. 2 PP. 25-34, (2023)
This article explores the causes, classification, and description of global problems, as well as ways to solve them. It also covers global development, the Millennium Development Goals, and sustainable development goals.
Read MoreDoi: https://doi.org/10.54216/JSDGT.030203
Vol. 3 Issue. 2 PP. 35-39, (2023)
In the field of Operations Research, the growing popularity of fruits, avocados, in the United States has sparked a need for thorough market analysis. This study aims to use Agent Based Modeling (ABM) principles to understand and predict sales volumes. By using intelligence techniques, the Extra Trees Regressor (ETR) we strive to identify the various factors that influence avocado sales. Our approach involves modeling data within ABM to provide an assessment and comparison, with classifiers. The results clearly demonstrate that ETR outperforms classifiers when it comes to predicting sales volume. Through plots and error prediction curves we can see how this model effectively captures sales patterns in a dynamic market environment. The predictive prowess of the proposed solution is validated through visual evaluation tools including residual plots as well as prediction curves, which prove its adeptness in predicting operational sales patterns within a dynamic market. The findings of our experiments study put emphasis on role of intelligence-based Agent-Based Modeling within Operations Research, exemplified by the Extra Trees Regressor, which offer a reliable tool for elucidating and projecting intricate market trends.
Read MoreDoi: https://doi.org/10.54216/JSDGT.030204
Vol. 3 Issue. 2 PP. 40-48, (2023)
This research paper examines the critical juncture of business intelligence and sustainable risk management in response to the increasing challenges faced by modern businesses. Our study recognizes that organizations must navigate uncertainties while prioritizing sustainability. It focuses on analyzing credit risk data. We present a comprehensive examination of predictive performance using Logistic Regression, Decision Tree, and K-Nearest Neighbors classifiers augmented by the Synthetic Minority Over-sampling Technique (SMOTE) for class rebalancing. The empirical findings presented through detailed tables and figures reveal intricate relationships and patterns within the data. This research also contributes to the broader discourse on responsible business practices by highlighting the integration of business intelligence in sustainable risk mitigation. Moreover, comparative analysis of machine learning algorithms under various resampling techniques further strengthens the framework’s reliability.
Read MoreDoi: https://doi.org/10.54216/JSDGT.030205
Vol. 3 Issue. 2 PP. 49-54, (2024)
