Intelligent Management Research Articles

Exploring the Intersection of Artificial Intelligence and Human Resource Management: A Bibliometric Study

<p dir="ltr"><span>Artificial Intelligence presents promising opportunities to improve efficiency, enhance decision-making, and boost employee engagement within human resource management. This study explores the intersection of Artificial Intelligence and Human Resource Management through an in-depth bibliometric analysis, offering a comprehensive overview of research trends and advancements over the past decade. Drawing from the Web of Science database, this research examined 491 journal articles published from 2014 to 2023. AI applications in Human Resource Management have been particularly valuable during the COVID-19 pandemic, aiding healthcare professionals, facilitating remote work, and optimizing workforce management under challenging conditions. Implementing Artificial Intelligence in Human Resource Management leadership management and information management has significantly improved organizational processes and outcomes. The study concludes that ongoing advancements in Artificial Intelligence technology will continue to enhance Artificial Intelligence practices, leading to a more adaptable, responsive, and efficient workforce capable of addressing future challenges.</span></p><div><span><br /></span></div>

Research on Intelligent Industrial Park Management System

In recent years, the scale of industrial parks in China has grown rapidly, but the use of intelligent management methods for industrial park management in China has not been carried out for a long time and lacks relevant experience. To explore the management mode and application characteristics of smart industrial parks, this article derives the operation mode of the smart industrial park management system based on the digital twin infrastructure, and elaborates and analyzes the main functions and characteristics of the smart industrial park management system, To provide reference and reference for better promoting the management system of smart industrial parks.

Intelligent fuzzy-PSO based grid connected solar EV charging station for electric buses to enhance stability and energy management

Abstract Modern power networks are relying more and more on Distributed Generation (DG) and solar energy-powered Electric Bus (EB) charging stations; nevertheless, controlling the charging process under situations of peak EB load and variable solar irradiation presents considerable hurdles. This study presents a coordinated control approach that maximizes energy distribution for solar (PV)-based EB charging stations by using an intelligent energy management system (IEMS). Through the analysis of load demand and meteorological data in real time, the IEMS optimizes PV generation and grid power consumption, thereby halving peak power demand, minimizing system losses, and easing distribution grid stress. In order to enable dynamic modifications to the energy flow between PV power, the grid, and battery storage, the method integrates an adaptive neuro-based fuzzy control system that anticipates solar energy generation and predicts EB load demands. Furthermore, to balance power distribution between battery and ultracapacitor systems and control energy storage in ultracapacitors, a fuzzy inference system optimized using Particle Swarm Optimization (PSO) is utilized. This prolongs battery life and guarantees effective energy management. The overall performance of the IEMS is improved by the fuzzy logic controller, which produces output signals that specify the best power distribution for any energy storage system. Digital simulations and a real-time hardware-in-loop experimental setup are used to validate the effectiveness of the proposed system, which shows notable gains in energy management, grid stability, and system efficiency. In order to improve intelligent energy management in grid-connected solar-powered systems, this study offers a viable method for incorporating renewable energy sources into EB charging stations.

Research on the design of prefabricated curved structure production capacity residential energy system: a case study of an entry of 2022 China International Solar Decathlon Competition- 'Solar Ark 3.0'

Under the severe situation of global warming, the problem of high energy consumption and carbon emissions in the construction industry need to be addressed urgently, and the method to promote the development of the construction industry in the direction of low-carbon sustainability has become a problem to be solved. Taking ‘Solar Ark 3.0’, the entry of Southeast University United Team in the Third China International Solar Decathlon Competition (SDC2022), as an example. This study proposes the wind and solar complementary capacity design, multi-mode energy storage design, intelligent energy use design and production, storage and use of multi-energy complementary intelligent energy management methods through the exploration of the integrated design and construction of curved structure housing and energy system. These designs of ‘Solar Ark 3.0’ improve the utilization efficiency of renewable energy in buildings and also provide methods for the integrated design of curved structural housing and energy systems.

A Framework for Smart City Traffic Management utilizing BDA and IoT

This paper explores a new approach to traffic flow optimization in smart cities, harnessing the combined power of Big Data Analytics (BDA) and the Internet of Things (IoT). The system utilizes a citywide network of connected sensors to acquire live traffic information, including vehicle speeds, density, and congestion points. These data are thereafter processed applying some top-notch BDA algorithms to identify traffic anomalies and forecast congestion levels, and generate actionable insights. By analyzing this information, the system can dynamically adjust traffic signals, recommend alternative routes, and improve traffic efficiency in real-time. The system's adaptive learning capabilities allow it to continuously enhance its predictions based on new data, ensuring its effectiveness in managing evolving traffic patterns. This intelligent traffic management solution promises to significantly reduce congestion, ameliorate overall mobility and road safety, and contribute to a more sustainable city environment.

OptiPower AI: A deep reinforcement learning framework for intelligent cluster energy management and V2X optimization in industrial applications

Peer-to-peer (P2P) energy trading has emerged as a practical solution for efficient energy management, particularly with the rising affordability of renewable energy and electric vehicles (EVs). This paper introduces the Optimal Power Artificial Intelligence (OptiPower AI) algorithm, a significant advancement in Intelligent Cluster Energy Management (ICEM). OptiPower AI combines Double Deep Q-Network (DDQN)-based Deep Reinforcement Learning (DRL), Vehicle-to-Everything (V2X) technology, and P2P energy trading to optimize energy distribution among clusters of prosumers and consumers. The system efficiently manages Renewable Energy Sources (RES) and EVs, achieving a 19.18% reduction in energy costs and a 50.02% decrease in average energy prices across V2X and P2P scenarios.OptiPower AI uses DRL to dynamically allocate energy and implement real-time pricing, enhancing energy efficiency and user satisfaction. Simulations based on meteorological data from Tunisia validate the system’s ability to improve thermal comfort, increase energy savings, and lower costs. The model’s parameters enable accurate forecasting and allocation, showcasing OptiPower AI’s reliability in variable demand conditions. This work advances the application of DRL in decentralized, sustainable P2P energy management systems for industrial clusters, addressing critical challenges in energy distribution, efficiency, and cost reduction.

A smart home energy management system based on human activity recognition and deep reinforcement learning

Smart home energy management systems (SHEMS) can help users save on energy costs by controlling various household loads in response to environmental changes. However, human activity is closely related to household energy consumption, which can lead to potential energy waste. Additionally, smart homes often feature multiple types of distributed energy resources (DER), and how to leverage their potential for participating in grid scheduling without significantly impacting users remains an unresolved issue. This paper proposes an intelligent home energy management method based on human activity recognition (HAR) and deep reinforcement learning. By using activity recognition results derived from intrusive load monitoring (ILM), we categorize contexts into two groups. The agent employs the soft actor-critic (SAC) algorithm for real-time control. In the first context, the goal is to ensure user satisfaction while reducing energy costs. In the second context, the focus is on controlling DER to participate in grid ancillary services to generate revenue. The proposed agent is trained using a real dataset, and simulations validate the effectiveness of the algorithm in home energy management.

Research on intelligent river water quality management system using blockchain-internet of things

Research on intelligent river water quality management system using blockchain-internet of things

Hybrid manufacturing thermal environment cycle and industrial economic transformation based on artificial intelligence

With the continuous development of global manufacturing, hybrid manufacturing has become an important way to improve production efficiency and respond to market demand. However, traditional manufacturing enterprises face many challenges in thermal environment management and energy consumption, and urgently need to achieve transformation and upgrading through artificial intelligence technology. This article aims to explore the thermal environment cycle of hybrid manufacturing based on artificial intelligence and its impact on industrial economic transformation, in order to provide theoretical support and practical guidance for manufacturing enterprises in intelligent manufacturing and energy management. We have researched and designed an intelligent manufacturing system, clarified the design principles and requirements of the system, constructed the system architecture, and designed the functional modules in detail. By collecting and processing data, using intelligent scheduling prediction technology to simulate the thermal environment of the manufacturing workshop, and then simulate the energy consumption situation. Finally, a manufacturing workshop scheduling model was constructed, process optimization strategies were proposed, and the transformation path of industrial economy was explored. Research has shown that through the implementation of intelligent manufacturing systems, the energy consumption of manufacturing workshops is significantly reduced, the thermal environment is effectively improved, and the application of process optimization and scheduling models greatly improves production efficiency. The exploration of the path of industrial economic transformation provides new ideas for the sustainable development of enterprises in the new situation.

Unlocking AI Creativity: A Multi-Agent Approach with CrewAI

By automating processes that traditionally require human intelligence, such as learning, reasoning, and problem-solving, Artificial Intelligence (AI) has transformed many industries. Even with these developments, System 1 thinking is quick, providing instinctive reactions, while System 2 thinking entails thorough analysis and reasoned decision-making. AI systems still have a long way to go before they can replicate System 2 thinking—deliberate, analytical, and essential for managing complex problems. This study investigates how Multi-Agent Systems (MAS) integrate System 2 thinking into AI, concentrating on CrewAI, a no-code framework designed to improve AI creativity and decision-making. Unlike TensorFlow Agents and OpenAI’s Gym, which are limited to single-agent reinforcement learning, CrewAI excels in handling multi-agent, real-world tasks through collaboration. This study explores practical applications of CrewAI, such as intelligent grid management, automated customer support, and advertising. These examples highlight how CrewAI promotes AI creativity and problem-solving through cooperative agent interactions, leveraging System 2 thinking. Problems like scalability and coordination are also addressed, with solutions such as dynamic role assignment and hierarchical task management. In summary, the integration of System 2 thinking into MAS frameworks like CrewAI signifies progress toward creating intelligent, dependable AI systems capable of tackling the complexities of real-world problems.

An integrated approach to address the biodegradability of cosmetic formulations as part of a corporate sustainability strategy

AbstractObjectiveThe biodegradability of organic chemicals with usual down‐the‐drain disposal in their end‐of‐life is a key parameter to be considered by industries pursuing sustainability in their new product developments. It is a property that, once improved in any formulated product portfolio, will allow for a significant reduction in the presence of its ingredients in the natural environment, with a potential reduction in the pollution‐related environmental impact after consumption. This article aimed to propose an intelligent strategy for addressing biodegradability in the cosmetics sector, with the potential to support both product claims and corporate sustainability initiatives.MethodsThe framework for achieving a portfolio biodegradability indicator is composed of three levels of assessment, the first being an ingredient level assessment, the second being an individual product biodegradability calculation (PBI—Product Biodegradability Index), and finally a third level to calculate an emission‐based, company‐wide biodegradability indicator (GBI—Global Biodegradability Index). A case study is presented and discussed in detail, as a proof of applicability of the whole framework to support new developments for an improved environmental performance. Important insights in terms of ingredients assessment criteria are also presented.ResultsA fictitious portfolio composed of two soaps (a bar and a liquid), two hair care products (shampoo and hair conditioner) and one bath oil resulted in a GBI of 92.8%. The bar soap was the main contributor for this global index, with a PBI of 98.8% and a relatively high consumption volume of 1 ton/year, which accounts for approximately 66% of the total portfolio consumption. Other products, like hair care, presented lower PBIs, but their lower mass consumption representativeness within the portfolio prevented a significant negative impact on the global index. The main strategic directions derived from the case study, to ensure a stable and high biodegradability of the consumed organic mass, were properly discussed.ConclusionThe output of this framework provided an intelligent management strategy for innovation, where reformulation efforts can be targeted at products and ingredients with the highest potential to improve the biodegradability profile of the total organic mass released to the environment from a given portfolio.

Construction error control method of large-span spatial structures based on digital twin

In the process of construction of large-span spatial structures, there are many control elements, which need to carry on the dual control of internal force and shape. The traditional control method cannot measure the construction deviation completely and accurately, and there is a lag in information feedback. In this paper, spatio-temporal information is integrated into digital twin and a construction error control method is proposed for large-span spatial structures. By analyzing the fusion mechanism of spatio-temporal information and digital twin, a closed-loop control system for structural errors before, during and after construction is formed. Driven by the control architecture, the method of structure simulation analysis and real-time monitoring during construction is proposed, and the functional relationship between cable force and cable length is established. Based on point cloud data, construction error optimization detection method is proposed for structure formation state. The nonlinear relation between elevation change rate and cable force deviation is formed and the control measures of formation state error are obtained. The experimental model of cable truss structure is taken as an example to apply and verify the theoretical method. The construction error control twin platform is formed in the experiment. Driven by the platform, the twinning analysis and real-time monitoring of the construction process are carried out to control the error of the structure formation. The results show that this method integrates spatio-temporal information into digital twin to realize data integration processing. The optimized error detection method can save 30% time cost and effectively control the construction error within 3%. The integration of spatio-temporal information and digital twin provides theoretical and algorithm support for the intelligent management and control of large-span spatial structure construction.

Research on Enterprise E-commerce Mode Based on Intelligent Management

With the rapid development of Internet technology and e-commerce, the traditional e-commerce management mode has been unable to adapt to the fast-paced development of e-commerce enterprises, and the management mode of e-commerce is facing new changes. The application of intelligent management technology can improve the efficiency and competitiveness of enterprises, and make the enterprise operation more transparent and efficient. Therefore, the article through the concept of intelligent management technology, the characteristics of the development of enterprise electronic commerce intelligent management, combined with the point of view at home and abroad scholars and existing theoretical basis, research based on intelligent management of enterprise electronic commerce mode, improve the level of enterprise e-commerce management, promote the sustainable development of the enterprise.

Dynamic Seat Management Algorithm for Optimizing Sleeper Train Capacity

Abstract: In this paper, we present a Dynamic Day-Night Seat Allocation Algorithm designed to optimize the utilization of sleeper train berths during long-duration journeys. The algorithm dynamically reallocates berths based on real-time conditions, optimizing seat usage for daytime sitting and nighttime sleeping. The objective is to enhance passenger comfort, prioritize elderly and family passengers for lower berths, and maximize train capacity through intelligent seat management. Our approach incorporates greedy algorithms to efficiently assign passengers to the most appropriate berth based on time of day and available space.

Fault-tolerant hierarchical energy management system for an electrical power system on more-electric aircraft

The concept of More-Electric Aircraft (MEA) has the potential to improve the environmental, economic, and reliability performance in the energy and transportation sectors. To achieve this potential, it has become a tendency to develop complex architectures of Electrical Power Systems (EPSs) for MEA to supply increasing electrical power demands. Moreover, a reliable and intelligent Energy Management System (EMS) is critical to coordinate the various EPS subsystems to ensure safe and efficient flight, following the real-time EPS operating requirements and safety criteria, while reducing the operating costs for all flight stages. This paper presents a fault-tolerant hierarchical EMS, for an innovative multi-converter-based aircraft EPS, to configure the system, ensure power distribution, and manage energy storage in multiple faulty scenarios over different time scales. There are two levels in this EMS: The High Level (HL) is based on Model Predictive Control (MPC), formulated by Mixed-Integer-Linear-Programming (MILP), to optimise the long-term EPS performance while considering future predictions; The Low Level (LL) adopts deterministic rules to cope with load changes and fault occurrences over the short term, during the HL sample intervals, with a faster clock. In particular, the LL controller contains four modes: to either cooperate with the HL online MPC or to operate independently, in either EPS normal or faulty conditions. The proposed EMS is evaluated in two cases, firstly considering load deviations in a normal operating scenario, and then considering behaviour in fault scenarios. The results indicate that the proposed EMS successfully reduces the EPS operational costs while ensuring quick responses to dynamic changes with either EPS component faults or EMS internal faults.

The Relationship Between Managers’ Emotional Intelligence and Project Management Decisions

The Relationship Between Managers’ Emotional Intelligence and Project Management Decisions

Revolutionising agri‐energy: A comprehensive survey on the applications of artificial intelligence in agricultural energy internet

AbstractConsidering the rapid advancements in AI technologies such as reinforcement learning, ChatGPT, and deep learning, this paper conducts a comprehensive survey of the technological landscape of AI in the energy and agriculture sectors. It delineates the evolutionary path of AI technologies in smart grids and precision agriculture, highlighting significant advancements in energy prediction, optimisation of production and consumption, and intelligent management. Furthermore, the paper identifies key AI technologies crucial for the Agricultural Energy Internet (AEI), offering specialised exploration into AI applications for crop cultivation and fisheries, including disease detection, yield prediction, and resource management. The research provides essential theoretical foundations for AI integration in each of these agricultural domains. In addition, the paper envisions the future integration of ChatGPT in coupled modelling of agriculture and energy systems, enhancing synergistic intelligent control, and AI‐driven carbon tracking technologies within the AEI. This study facilitates a greater grasp of the transformative potential of AI in reshaping the nexus of agriculture and energy.

Adoption of Blockchain Technology for ASCM Solution: A Systematic Literature Review

With the global population projected to exceed 10.9 billion by 2100, agriculture faces significant strain. One approach to alleviate this pressure is through the use of blockchain technology, which could improve the traceability and transparency of agricultural products. This study investigates blockchain’s characteristics, advantages, and challenges to determine its suitability for agricultural supply chain management (ASCM). The methodology involved selecting keywords and conducting searches for papers published between 2017-2021 across multiple scholarly databases including Google Scholar, Scopus, Cross Ref, Science Direct, and Emerald Insight. The PRISMA method was employed for the literature review, resulting in the analysis of 91 papers. The analysis identified the top ten most commonly discussed blockchain characteristics. Findings suggest that blockchain technology offers advantages such as increased operational efficiency, enhanced management data transparency, intelligent contract management, and mitigation of fraud, errors, and financial losses in ASCM. However, blockchain adoption faces challenges including regulatory hurdles, stakeholder relationships, data ownership, scalability issues, and knowledge gaps. This study contributes to the understanding of blockchain's potential in ASCM and underscores the importance of addressing these challenges for its effective implementation.

The Design and Implementation of an Intelligent Carbon Data Management Platform for Digital Twin Industrial Parks

In the face of increasing environmental challenges, carbon emissions from industrial parks have become a global focal point, particularly as electricity consumption serves as a major source of carbon emissions that requires effective management. Despite proactive efforts by governments and industry stakeholders to transition industrial parks toward cleaner production methods, traditional energy management systems exhibit significant limitations in data collection, real-time monitoring, and intelligent analysis, making it difficult to meet the urgent demands for carbon reduction. To address these challenges, this study proposes a carbon data management approach for industrial parks based on digital twin technology and develops an intelligent system that integrates monitoring, environmental surveillance, energy management, and carbon emission monitoring. The system supports efficient energy-saving and carbon-reducing decision making by real-time collection of energy consumption data. By incorporating Building Information Modeling (BIM) and Internet of Things (IoT) technologies, the system facilitates the integration and visualization of multi-source data, significantly enhancing the transparency of carbon data. The results of the carbon reduction validation system demonstrate that the application of this platform and its associated facilities can significantly reduce carbon emissions in the park, providing robust support for the transition of industrial parks toward low-carbon and sustainable development.

The Impact of AI and LMS Integration on the Future of Higher Education: Opportunities, Challenges, and Strategies for Transformation

The integration of artificial intelligence (AI) and learning management systems (LMS) is revolutionising higher education, offering unprecedented opportunities for personalised learning, adaptive assessments, and data-driven decision-making. This review investigates the impact of AI–LMS integration on educational quality, student success, and institutional performance in higher education. In addition, this review not only examines the technological integration but also evaluates how AI–LMS systems contribute to sustainable development in higher education through reduced resource consumption, improved accessibility, and enhanced educational equity. Following the PRISMA 2020 guidelines, a comprehensive search of the Scopus database yielded 60 relevant studies published between 2014 and 2023. The review reveals significant benefits of AI–LMS integration, including enhanced student engagement, personalised learning paths, and improved learning outcomes. Key applications include AI-powered conversational agents, adaptive assessments, and learning analytics. However, challenges such as data privacy concerns, algorithmic bias, and the need for faculty training were also identified. The findings highlight strategies for effective AI–LMS implementation, emphasising the importance of ethical considerations and addressing the digital divide. Results demonstrate that AI–LMS integration can significantly enhance educational quality and student performance when implemented thoughtfully. The review also uncovers areas requiring further research, including long-term impacts on learning outcomes, scalability of AI–LMS solutions, and strategies for ensuring equitable access. Future studies should focus on longitudinal assessments of AI–LMS effectiveness, the development of ethical frameworks for AI in education, and the exploration of AI–LMS applications in diverse educational contexts. This review provides valuable insights for higher education institutions seeking to leverage AI–LMS integration to transform teaching and learning practices.