Huu Dang Quoc

This paper introduces the ETMS-RCPSP (Earliness–Tardiness Multi-Skill Resource-Constrained Scheduling Problem)—a novel problem derived from the MS-RCPSP by adding constraints on project completion time or actual production contracts. The goal of the new problem is to control the project completion time as closely as possible to reality—this differs from the original MS-RCPSP, which aimed to minimize project execution time. The objective of the problem is of greater practical significance in ensuring project completion on schedule while also addressing related issues, such as the ability to receive finished products on time as stipulated in the contract. The ETMS-RCPSP is an NP-hard problem whose result can be used for resource allocation in project execution or for resource arrangement in production lines to fulfill economic contracts. To address the ETMS-RCPSP, the paper proposes a new evolutionary algorithm, BDERL (Balanced Differential Evolution with Reinforcement Learning), that combines differential evolution with a problem-specific decoding mechanism and an adaptive parameter control strategy based on reinforcement learning (Q-learning). The proposed algorithm is evaluated on benchmark instances derived from the iMOPSE dataset and the TNG company dataset—a real-world dataset from manufacturing and contract-driven environments. Experimental results demonstrate that the approach consistently reduces total production costs compared to baseline heuristics while maintaining competitive computational efficiency. The findings underscore the efficacy of adaptive hybrid optimization techniques in solving intricate production scheduling problems characterized by limited resources and varied skill competencies.

This paper examines the interaction between robotic process automation (RPA), business process management (BPM), and digital transformation (DT)-three critical components in improving operational efficiency and driving business modernization. RPA automates repetitive tasks, reduces errors, accelerates processing, and optimizes resource use. When combined with artificial intelligence (AI) and machine learning (ML), it further enhances data analysis and decision-making. BPM focuses on analyzing, designing, and optimizing business processes to ensure organizational agility. DT provides a technological foundation for broader innovation in processes and structures. The paper contributes a comprehensive and updated perspective on how RPA, BPM, and DT interrelate—not only functioning independently but also reinforcing one another to create greater business value. It emphasizes that their integration is a strategic approach to improving performance, responsiveness, and continuous innovation. Importantly, the research is relevant to both Industry 4.0 and Industry 5.0. While Industry 4.0 (I4.0) prioritizes automation and data-driven systems, Industry 5.0 (I5.0) highlights human–technology collaboration for more adaptive and human-centric organizations. This study enriches theoretical insights and offers practical guidance for building effective and sustainable DT strategies.

In the rapid development of information technology, using enterprise data to support timely management decisions is crucial in helping businesses operate effectively and improve competitiveness. This study uses Microsoft power business intelligence (MPBI) to analyze data in retail systems, allowing managers to grasp the business situation in real time, track advanced sales, optimize inventory control, and analyze customer behavior and supply chain visibility. From the data generated by the business, the study uses the streaming extract transform load (ETL) model to support real-time data aggregation, then converts to the MPBI data visualization system to convert data into visual charts, helping businesses easily monitor, track, analyze, and make decisions to promote business activities. The study proposes a data structure to organize retail information storage. It proposes a system of calculation formulas and data synthesis, making integrate and convert tabular data into visual charts. Through analysis of real data from the LH83 retail system, the study shows the feasibility of implementing a data visualization system and the difficulties encountered when businesses want to deploy this model.

In the rapidly evolving retail industry, data-driven decision making is critical to maintaining competitive advantage and operational efficiency. This paper explores the diverse applications of Microsoft Power BI (MPBI) in retail, highlighting its impact on real-time data management, sales analysis, inventory optimization, customer insights, and supply chain performance. By synthesizing findings from recent studies and presenting empirical data from case studies, we demonstrate how Power BI's advanced analytics and visualization capabilities can transform raw data into actionable insights. Our research underscores the importance of integrating disparate data sources into a unified platform, facilitating comprehensive data analysis, and fostering a culture of data literacy across retail organizations. We also discuss the challenges and best practices for implementing Power BI across retail functions, highlighting its role in driving innovation and adapting to emerging market trends. The results of this study provide practical insights for retailers seeking to leverage data analytics for strategic decision-making and operational excellence.

The management information systems (MIS) for education bring many benefits to management at universities, educational institutions, and academies. The Vietnam National Academy of Education Management (NAEM) has successfully integrated information technology into the management, teaching, and learning process, bringing many benefits. Many professional activities have been included in the standard framework of the academy's faculties. However, more specific and detailed activities at specialized faculties are being actively researched and implemented for management purposes. This article presents a study on the construction and implementation of a management information system to support the management of activities at faculties, such as managing information about lecturers' teaching, information about scientific research activities, and published works, In addition, this system also allows students to engage in learning activities such as registering for internships and internships at enterprises, information about graduation thesis implementation, and lecturers' assignments to guide students. Deploying this system at faculties supports the management of detailed operations, improves data management and processing, and ensures consistency in management.

Applying IoT systems in industrial production allows data collection directly from production lines and factories. These data are aggregated, analyzed, and converted into reports to support manufacturers. Business managers can quickly and easily grasp the situation, making timely and effective management decisions. In industrial sewing, IoT applications collect production data from sewing lines, especially from industrial sewing machines, and transmit that data to cloud-based systems. This allows businesses to analyze production situations, thereby improving management capacity. This article explores the implementation of IoT applications at industrial sewing enterprises, focusing on data collection during the production process and proposing a data structure to integrate this information into the company’s MIS system enterprise. In addition, the research also considers applying the Real-RCPSP problem to support businesses in planning automatic production operations.