Demand Management Research Articles

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

Improving the Structure of the Electricity Demand Response Aggregator Based on Holonic Approach

A demand response (DR) aggregator is a specialized entity designed to collaborate with electricity producers, facilitating the exchange of energy for numerous stakeholders. This application is a pivotal development within the Russian Energy System as it transitions to a Smart Grid. Its successful operation relies on the advancement and implementation of more efficient strategies to manage emerging energy assets and structures. The holonic approach is a distributed management model used to handle systems characterized by random and dynamic changes. This paper analyzes the specific aspects of the electricity demand management mechanism in Russia, primarily aimed at reducing peak load in the energy system by engaging active consumers who are outside the wholesale market. The DR-Aggregator is considered both a cyber-physical system (CPS) with a cluster structure and a business process. The DR-Aggregator exhibits essential holonic properties, enabling the application of a holonic approach to enhance the efficiency of the DR-Aggregator mechanism. This approach will facilitate greater flexibility in managing the load schedules of individual holon consumers, bolster the reliability of power supply by aligning load schedules among holon consumers within the super-holon cluster, and improve the fault tolerance of the DR-Aggregator structure, providing greater adaptability of demand management services.

Air pollution reduction co-benefits associated with low-carbon transport initiatives for carbon neutrality in China by 2060

Low-carbon transport policy measures will contribute to greenhouse gas emission reductions, as well as improvements in air quality via air pollutant emission reductions. Here, we developed a regional transport energy model by integrating a transport demand model and an energy system model to explore the associated air pollutant emission reductions achieved by low-carbon transport initiatives when projecting a path towards carbon neutrality. Several scenarios were created to identify the effectiveness and feasibility of low-carbon strategies using the Avoid–Shift–Improve (A-S-I) framework including “Technology”, “Regulation”, “Information”, and “Economy” instruments. Significant reductions in air pollutant emissions alongside transport decarbonization in China could be realized by implementing policy measures to establish effective transport demand management, a modal shift in transport use, and technological improvements. In particular, the “Improve” strategy was more effective in delivering significant reductions in air pollutant emissions than were the “Avoid” and “Shift” strategies, but the policy effects of each A-S-I pillar varied greatly for different pollutants and across different regions. An air pollution reduction strategy for the transport sector should be designed in an integrated manner based on a series of different strategies and tools.

Understanding water-energy-carbon nexus in English and Welsh water industry by assessing eco-productivity of water companies

Understanding the water-energy-carbon nexus in water supply is essential for water regulators and utilities. This study employs a non-radial Data Envelopment Analysis (DEA) model to assess eco-productivity (ecoP) change, a synthetic indicator that integrates carbon emissions, energy costs, and water delivered. It also evaluates its components—eco-efficiency change and eco-technological change—across water companies in England and Wales from 2011 to 2018. The analysis reveals an annual improvement in ecoP of 1.1%, driven by a 2.1% gain in eco-efficiency but offset by a 1.0% decline in technological advancement. The reduction in GHG emissions emerged as the most significant positive contributor, enhancing ecoP by 3.22% annually, while energy costs detracted ecoP by –0.09%. The results underscore the negative impacts of increased water delivery (–1.74%) and the number of connected properties (–1.27%) on ecoP, highlighting the need for demand management policies.

Optimizing Large-Scale Demand and Capacity Balancing in Air Traffic Flow Management Using Deep Neural Networks

Over the past forty years, air traffic flow management (ATFM) has garnered significant attention since the initial approach was introduced to address single-airport ground delay issues. Traditional methods for solving both single- and multi-airport ground delay problems primarily rely on operations research techniques and are typically formulated as mixed-integer problems (MIPs), with solvers employed to approximate optimal solutions. Despite their effectiveness in smaller-scale problems, these approaches struggle with the complexity and scalability required for large-scale, multi-sector ATFM, leading to suboptimal performance in real-time scenarios. To overcome these limitations, we propose a novel neural network-based demand and capacity balancing (NN-DCB) method that leverages neural branching and neural diving to efficiently solve the ATFM problem. Using data from 15,927 flight trajectories across 287 airspace sectors on a typical day in February 2024, our method re-allocates trajectory entry and exit times in each sector. The results demonstrate that large-scale ATFM problems can be solved within 15 min, offering a significant performance improvement over the state-of-the-art methods. This study confirms that neural network-based approaches are more effective for large-scale ATFM problem-solving.

Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU

Given the inherent fluctuation of photovoltaic (PV) generation, accurately forecasting solar power output and grid feed-in is crucial for optimizing grid operations. Data-driven methods facilitate efficient supply and demand management in smart grids, but predicting solar power remains challenging due to weather dependence and data privacy restrictions. Traditional deep learning (DL) approaches require access to centralized training data, leading to security and privacy risks. To navigate these challenges, this study utilizes federated learning (FL) to forecast feed-in power for the low-voltage grid. We propose a bottom-up, privacy-preserving prediction method using differential privacy (DP) to enhance data privacy for energy analytics on the customer side. This study aims at proving the viability of an enhanced FL approach by employing three years of meter data from three residential PV systems installed in a southern city of Germany, incorporating irradiance weather data for accurate PV power generation predictions. For the experiments, the DL models long short-term memory (LSTM) and gated recurrent unit (GRU) are federated and integrated with DP. Consequently, federated LSTM and GRU models are compared with centralized and local baseline models using rolling 5-fold cross-validation to evaluate their respective performances. By leveraging advanced FL algorithms such as FedYogi and FedAdam, we propose a method that not only predicts sequential energy data with high accuracy, achieving an R2 of 97.68%, but also adheres to stringent privacy standards, offering a scalable solution for the challenges of smart grids analytics, thus clearly showing that the proposed approach is promising and worth being pursued further.

Systematic Analysis of Commuting Behavior in Italy Using K-Means Clustering and Spatial Analysis: Towards Inclusive and Sustainable Urban Transport Solutions

Transport Demand Management (TDM) is crucial in shaping travel behavior and enhancing urban mobility by promoting sustainable transport options. This study represents a comprehensive analysis of employee commuting behavior across seventy-seven cities in Italy, with a focus on Rome as a case study. It investigates some requirements of the workplace travel plan as a TDM strategy for promoting sustainable commuting. An online survey conducted in June 2022 yielded 2314 valid responses, including 1320 from private car drivers. K-means clustering was used to identify distinct behavioral patterns among commuters, revealing four clusters based on demographic factors and transport preferences, such as age, gender, family circumstances, vehicle ownership, willingness to walk, ride bicycles, or e-scooters, and reasons for mode choice. This study analyzed Rome’s public transport network, land use, and private car use. Results underscore the need for tailored transport policies that enhance inclusivity and accessibility, especially for employees with family members who cannot commute independently. A spatial analysis of Rome reveals significant infrastructure deficiencies, such as complicated transfers and inaccessible stations, which discourage PT use. Future research should explore the impact of remote work and psychological factors and conduct in-depth subgroup analyses to inform inclusive transport policy development.

GESTÃO DA CADEIA DE SUPRIMENTOS USANDO IA

Supply Chain Management (GCS) integrates and coordinates all activities from obtaining materials to final delivery, aligning objectives to maximize efficiency, reduce costs and improve quality. Planning in the chain is crucial to guarantee resources, minimize uncertainties and add value to the customer. Logistics is essential in efficient distribution, and inventory management balances supply and demand. Performance indicators (KPIs) and process mapping are used to evaluate and improve operations, while production planning and control (PCP) ensure efficiency in production and delivery. These aspects form the basis of GCS, essential for customer success and satisfaction. Therefore, the objective of this work is to analyze the use of artificial intelligence of supply chain management in a contemporary context, identifying challenges, strategies and impacts for organizations, with a focus on optimizing processes and improving business competitiveness.

Body fat variation and redistribution across different stages of life measured by dual-energy x-ray absorptiometry.

The global obesity epidemic of all ages has increased the demand for accurate management of body fat in each stage of life. The primary aim of this study was to provide reference centiles of body fat indices for Chinese children and adults and compare those with ethnicities from USA using dual-energy x-ray absorptiometry (DXA). The samples were drawn from two nationwide cross-sectional surveys of China Body Composition Life-course Study (2013-2023) and the US National Health and Nutrition Examination Survey (2011-2018). Age- and sex-specific centile curves were generated for a set of fat measurements, including total fat mass (FM), fat mass index (FMI), body fat percentage (BF%), trunk-to-leg fat ratio (TLR), android-to-gynoid fat ratio (AGR) and visceral-to-subcutaneous fat ratio (VSR), using the general additive model for location scale and shape method. The age-related variations from childhood to adulthood were generally similar among Chinese, Non-Hispanic Whites, Non-Hispanic Blacks and Mexican American population, with distinct levels across races and ethnicities. For whole-body fat (FM, FMI, and BF%), Mexican American population consistently presented the highest levels before 40 years old, followed by Non-Hispanic White, Non-Hispanic Black and Chinese individuals. For central fats indices, although the TLR and AGR levels in Chinese males were second to Mexican American counterparts in most stages of life, the VSR was much higher in Chinese than other races and ethnicities from eight years old. DXA-based centiles for body fat quantity and distribution in Chinese population aged 3-60 years old were presented, and their differences with other race and ethnicity were noted across periods from early childhood to middle adulthood. Our findings will promote age-, sex- and ethnic-specific assessment of life-course body fat and obesity-related risks in clinical practice.

Simultaneous and Rapid Detection of Glucose and Insulin: Coupling Enzymatic and Aptamer-Based Assays.

Diabetes management demands precise monitoring of key biomarkers, particularly insulin (I) and glucose (G). Herein, we present a bioelectronic chip device that enables the simultaneous detection of I and G in biofluids within 2 min. This dual biosensor chip integrates aptamer-based insulin sensing with enzymatic glucose detection on a single platform, employing a four-electrode sensor chip. The insulin voltammetric sensor employs a G-quadraplex methylene-blue-modified aptamer, while the amperometric biocatalytic glucose sensor utilizes a second-generation mediator-based approach. Simultaneous reagent-less sensing of I and G has been achieved by addressing key challenges. These include combining different surface chemistries, assay formats, and detection principles at closely spaced working electrodes and the substantially different concentration levels of the I and G targets. An attractive analytical performance, with no apparent crosstalk, is demonstrated for the simultaneous detection of millimolar G concentrations and picomolar I concentrations in single microliter serum or saliva sample droplets. This dual biosensor offers rapid, cost-effective, and reliable monitoring, addressing the unmet need for integrated multiplexed diabetes biomarker detection in decentralized settings. Such integration of enzymatic and aptamer-based bioassays could greatly expand the scope of decentralized testing in healthcare beyond diabetes care.

A SPINAL TRAUMA, A TUMOR AND EVERYTHING IN BETWEEN

Introduction Burkitt lymphoma is a male predominant (3:1 male:female ratio) aggressive B-cell non-Hodgkin lymphoma that can present as a sporadic variant with meningeal involvement in about 20% of cases. Therefore, it can manifest with neurologic deficits due to compression of the spinal cord or spine nerve root. Case Report We report the case of a 6 years-old girl who presented in the emergency department following a traumatic vertebral injury (fall from around 1.5 meters) with no spinal cord involvement. She accused upper back pain, walking and orthostatism difficulties and manifested a light to moderate inferior paraparesis (MCR 3/5) The MRI revealed a space-occupying epidural mass between T1-T4, severely compressing the spinal cord initially thought to be an epidural hematoma. Emergency surgery was performed with duro-radicular decompression and mass excision through a T1-T4 laminotomy followed by a laminoplasty. The unveiled mass was white, minimally vascularized and adherent to the dura raising the suspicion of a tumor. Postoperatively the patient exhibited significant neurological improvement with resolution of the paraparesis and normal gait restoration. Three weeks later, the patient returned with complaints of abnormal gait, lumbar pain radiating in the right lower leg and upon examination a light right lower limb monoparesis (MRC 4/5) following another traumatic event. Emergency MRI of the whole spine with contrast was requested just as the pathologist in charge of the case suggested a diagnosis of Burkitt lymphoma, prompting a whole-body MRI. A severe recurrence was noted at the site of the previous lesion along with a new lumbar lesion, and several intraabdominal, intrathoracic, ovarian tumor sites with diffuse infiltration of the bone marrow. The patient was urgently referred to the pediatric oncology department and started on steroidal anti-inflammatory treatment and chemotherapy. Conclusions Burkitt lymphoma involving the nervous system in children requires increased medical attention during the clinical, histopathological examination and treatment phase, due to the association with advanced disease and, although rare, should be considered as a differential diagnosis in pediatric patients with epidural lesions. The management of such lesions demands a solid collaboration between neurosurgeon, pediatrician, pathologist and oncologist for an early diagnosis and proper treatment.

THE INFLUENCE OF EMOTIONAL INTELLIGENCE, CULTURAL DIVERSITY IN ORGANIZATION, AND COMMUNICATION STRATEGIES ON LEADERSHIP EFFECTIVENESS IN DIVERSE WORKPLACES IN FUZHOU CITY, FUJIAN PROVINCE, CHINA

In today’s rapidly evolving business landscape, characterized by heightened complexity and relentless change, the demand for adept management is more pronounced than ever. As organizations grapple with the challenges presented by an increasingly interconnected and technologically advanced world, the need to adapt and evolve management practices becomes a paramount concern (Drejer et al., 2014; BAGU et al., 2011). The aims of this study were to investigate the factors influencing Leadership Effectiveness based on 4 aspects, namely, the Demographic Factors, the Emotional Intelligence, the Cultural Diversity in Organization, and the Communication Strategies. The quantitative method was applied. Descriptive statistics such as frequency, percent frequency, mean, and standard deviation are introduced. Various inferential statistical methods are used to test the hypothesis, particularly the Independent samples t-test, the One-way ANOVA, and the Multiple linear regression analysis. The results obtained from the study indicate that differences in Marital Status, Age, Educational Level, and Monthly Income generate differences in Leadership Effectiveness. Based on the Multiple Linear Regression analysis, all aspects of Emotional Intelligence (Self-awareness, Self-regulation, Motivation, Empathy, and Social Skills), Cultural Diversity in Organization (Diversity, Equity, Inclusion, and Belonging) and Communication Strategies (Awareness and Understanding, Communication Quality, Accessibility, and Employee Engagement) had a positive impact on Leadership Effectiveness.

Methodology for 3D Management of University Faculties Using Integrated GIS and BIM Models: A Case Study

Three-dimensional virtual modeling is one of the tools being rapidly implemented in the construction industry, leading to the need for strategies based on intelligent 3D models of cities and/or digital twins, which allow simulation by interacting with their real physical counterparts, anticipating the outcomes of decision making. In practice, problems arise when creating and managing these twins, as different data, models, technology, and tools must be used, and they cannot all be combined as desired due to certain incompatibilities. On the other hand, today’s traditional building management demands a more optimized process to prevent errors and enable timely reactions to failures and defects. Managing and using a large amount of complex and disparate data are required, which is why the use of CMMS-type software is common (Computerized Maintenance Management System). However, such software is rarely designed for management in a 3D format, often due to the absence of three-dimensional models of the assets. This research aims to contribute to the technological development of the digitalization of the built environment, providing a simple methodology for generating and managing 3D models of cities. To achieve this, the tools and information useful for generating an integrated GIS 3D and BIM model, and for Computer-Aided Maintenance Management in a three-dimensional format (CMMS-3D), are identified. The final model obtained is used to optimize the three-dimensional management of a classroom building on the “Campus de Las Llamas” at the University of Cantabria in Spain. The results demonstrate that it is possible to integrate digital models with simple linking mechanisms between the existing tools, thus achieving an optimal three-dimensional management model.

Learning Dynamic Selection and Pricing of Out-of-Home Deliveries

Home delivery failures, traffic congestion, and relatively large handling times have a negative impact on the profitability of last-mile logistics. A potential solution is the delivery to parcel lockers or parcel shops, denoted by out-of-home (OOH) delivery. In the academic literature, models for OOH delivery are so far limited to static settings, contrasting with the sequential nature of the problem. We model the sequential decision-making problem of which OOH location to offer against what incentive for each incoming customer, taking into account future customer arrivals and choices. We propose dynamic selection and pricing of OOH (DSPO), an algorithmic pipeline that uses a novel spatial-temporal state encoding as input to a convolutional neural network. We demonstrate the performance of our method by benchmarking it against two state-of-the-art approaches. Our extensive numerical study, guided by real-world data, reveals that DSPO can save 19.9 percentage points (%pt) in costs compared with a situation without OOH locations, 7%pt compared with a static selection and pricing policy, and 3.8%pt compared with a state-of-the-art demand management benchmark. We provide comprehensive insights into the complex interplay between OOH delivery dynamics and customer behavior influenced by pricing strategies. The implications of our findings suggest that practitioners adopt dynamic selection and pricing policies. History: This paper has been accepted for the Transportation Science special issue on TSL Conference 2023. Funding: This work was supported by TKI DINALOG.

Tropical cyclone-induced rainfall variability and its implications for drought in Taiwan: Insights from 1981 to 2022

This study analyzes the interplay between tropical cyclones (TCs) and drought in Taiwan over the period from 1981 to 2022, leveraging data from CHIRPS, FLDAS, IBTrACS, and Sentinel and Landsat satellite imagery. Our findings reveal a significant decline in TC-induced rainfall over the last decade, with a turning point in 2017. We establish a low to moderate positive correlation between TC-induced rainfall and subsequent spring soil moisture levels, with April exhibiting the strongest connection. This relationship strengthens in transitions from agricultural to forested areas. Additionally, our research identifies a robust correlation (r = 0.77) between TC-induced rainfall and reservoir water levels, underscoring the consistent link between TCs and hydrological drought. However, intriguing disparities emerge in water availability patterns. In 2007 and 2020, reservoirs maintained normal levels despite reduced typhoon rainfall in prior years. Conversely, in 2019, water shortages occurred despite typical TC-induced rainfall. These cases suggest that water availability is influenced by factors beyond TCs, including water demand, infrastructure, monsoon and spring rainfall, air temperature, and water management strategies. Furthermore, we uncover a symmetrical relationship between TCs and monsoon-induced rainfall, indicating their potential to balance reservoir water supply. Nevertheless, both TCs and the monsoon exhibit declining trends in the past decade, heightening the risk of spring season water scarcity in Taiwan. Effective water resource management demands consideration of natural and anthropogenic factors, encompassing land use changes, deforestation, production activities, water pollution, and policy strategies. These measures are essential to mitigate water scarcity and promote sustainability in Taiwan.

Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning

The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.

Critical crisis management competencies: Perspectives from universities of technology leadership

AbstractThe South African Higher Education sector has faced challenging times recently, notably the #FeesMustFall student protest crisis, which inflicted damages exceeding R1 billion, and the COVID‐19 pandemic, claiming over 102,000 lives in the nation. Against this backdrop, a critical investigation has been initiated to discern the indispensable crisis management competencies required by executive management members of Universities of Technology (UoT) in South Africa. This exploratory study emphasises the higher education landscape, recognising its pivotal role in national development. Employing qualitative face‐to‐face interviews, the research aims to delve into the experiences of executive management members, providing insights into the nuanced demands of crisis management within the higher education context. In response to the challenges posed by the COVID‐19 pandemic, the study adapted by utilising online face‐to‐face meetings, ensuring a thorough exploration of crisis management competencies. Beyond mere identification, the research attempts to elucidate the profound significance of these competencies within the unique context of higher education institutions during crises. The findings contribute not only to the broader understanding of crisis management but also underscore the particular relevance and implications for the resilience and adaptability of South African UoT in the face of multifaceted challenges.

Mapping key areas to protect high-value and high-vulnerability groundwater from pollution load: Method for management

Severe groundwater pollution has necessitated prioritizing the prevention and control of groundwater pollution (PCGP). The fundamental strategy of PCGP involves identifying priority areas. Vulnerability assessment, such as DRASTIC, and its extension, pollution risk assessment, have been developed to guide PCGP. However, managers find it struggling to implement these results in PCGP due to a lack of consideration for practical management demands. This study establishes a management-oriented method to map key areas for groundwater protection and PCGP, considering water sources, pollution source load, vulnerability, and function value, to facilitate management implementation. The key area includes the protection area aimed at protecting water sources and the control area focused on preventing and controlling pollution load in high-value and high-vulnerability groundwater. The effectiveness and practicality of this method are demonstrated through a case study in a large district reliant on groundwater, enabling the key area and corresponding suggestions to directly guide local management. This method offers a practical tool for PCGP worldwide and is expected to guide the sustainable development plan.

915 nm pumping kilowatt fiber oscillator with high optical-to-optical efficiency

We demonstrate an all-fiber oscillator with high optical-to-optical efficiency using laser diodes working at 915nm as the pump sources to reduce the demand for thermal management. When the output power of the bidirectional pumped oscillator is 1.16kW, the optical-to-optical efficiency is as high as 75.4%. In this working state, the output characteristics of the oscillator are observed. The Raman suppression ratio is 41.23dB and the beam quality factor Mx2 = 1.14, My2 = 1.29. Analyzing the time trajectory of the highest power output, there are no significant characteristic peaks in the time domain signal and the frequency domain signal, which indicates that the oscillator has good stability.Improving the output characteristics of the 915nm pumping lasers has a positive significance for the application of non-strict ambient temperature control.

Modelling capability factors of logistics industry based on ISM-MICMAC

Supply chain disruptions and global pandemics have revealed vulnerabilities within the logistics industry in recent years. Consequently, there has been a growing emphasis across various industries on enhancing logistics capabilities. The focus on improving logistics performance through capability factors has shifted the paradigm toward prioritizing these factors. In light of this transition, this study develops a structural model to identify the logistics capability factors (LCFs) that contribute to logistics performance. To define the interrelationships and interdependencies among LCFs, expert-based interpretive structural modeling (ISM) combined with cross-impact matrix multiplication applied to classification (MICMAC) is used. A complex ISM model and a MICMAC diagram, based on driving and dependency power, are constructed to determine and categorize LCFs. The findings indicate that the demand management interface (DMI) of service capability is a pivotal factor in enhancing logistics capability, followed by the significance of information management and technological innovation capabilities. The results of this study have several implications for understanding the relationships among LCFs and for extending knowledge through the application of the ISM-MICMAC technique. Both theoretical and managerial insights are highlighted, providing guidance for supply chain leaders and operational managers.