Optimal Resource Allocation Research Articles

Stackelberg Game-Based Task Offloading for Joint Service Caching and Resource Allocation Optimization in UAV-Assisted VEC

Stackelberg Game-Based Task Offloading for Joint Service Caching and Resource Allocation Optimization in UAV-Assisted VEC

Dynamic resource allocation and scheduling for appointment-based systems with walk-ins

Effectively managing constrained and perishable capacity amid fluctuating demands is a common challenge in service industries, often addressed through appointment-based systems that incorporate walk-ins. Balancing appointments and walk-ins is crucial for enhancing system value and maintaining stability. In this article, we study an integrated decision problem for appointment scheduling and resource allocation across multiple facilities, considering stochastic demands from three segments: priority customers, walk-in customers, and appointment customers. We formulate the problem as a sequential scheduling-allocation challenge using stochastic dynamic programming. Leveraging the anti-multimodularity of the value function, we fully characterize the optimal dynamic resource allocation policy and its monotone structural properties under any appointment schedule. Given the complexity and intractability of optimal scheduling with dynamic resource allocation, we introduce two suboptimal scheduling policies—vertical scheduling and horizontal scheduling—and a heuristic allocation policy. Additionally, we create an Upper Bound (UB) for the original problem as a surrogate, establishing an asymptotically optimal UB for the scaled problem through its objective value and a corresponding lower bound through its solution. This UB solution serves as an efficient and effective heuristic, demonstrating superior performance compared with the combined performance of suboptimal scheduling policies with optimal dynamic resource allocation, especially as problem size increases. It also holds significant practical implications for integrated pooling systems.

Optimal Clean Energy Resource Allocation in Balanced and Unbalanced Operation of Sustainable Electrical Energy Distribution Networks

Optimal Clean Energy Resource Allocation in Balanced and Unbalanced Operation of Sustainable Electrical Energy Distribution Networks

Physics-informed convolutional neural network for microgrid economic dispatch

The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be incredibly challenging. This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges. Compared to traditional methods, CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties. While CNN has shown promising results, it does not extract explainable knowledge from the data. To address this limitation, a physics-inspired CNN model is developed by incorporating constraints of the ED problem into the CNN training to ensure that the model follows physical laws while fitting the data. The proposed method can significantly accelerate real-time economic dispatch of microgrids without compromising the accuracy of numerical optimization techniques. The effectiveness of the proposed data-driven approach for optimal allocation of microgrid resources in real-time is verified through a comprehensive comparison with conventional numerical optimization approaches.

Competitive resource allocation on a network considering opinion dynamics with self-confidence evolution

The formation of public opinion is typically influenced by different stakeholders, such as governments and firms. Recently, various real-world problems related to the management of public opinion have emerged, necessitating stakeholders to strategically allocate resources on networks to achieve their objectives. To address this, it is imperative to consider the dynamics of opinion formation. Notably, in existing opinion dynamics models, individuals possess self-confidence parameters reflecting their adherence to historical opinions. However, most extant studies assume the individuals’ self-confidence levels remain constant over time, which cannot accurately capture the intricacies of human behavior. In response to this gap, we first introduce a self-confidence evolution model, which encompasses two influencing factors: the self-confidence levels of one's group mates and the passage of time. Furthermore, we present the social network DeGroot model with self-confidence evolution, and conduct some theoretical analyses. Moreover, we propose a game model to identify the optimal resource allocation strategies of players on a network. Finally, we provide sensitivity analyses, comparative studies, and a case study. This paper highlights the significance of incorporating self-confidence evolution into the process of opinion dynamics, and the results can provide valuable practical insights for players seeking to improve their optimal resource allocation on a network to more effectively manage public opinions.

Internal control effectiveness, supply chain management efficiency and capital allocation efficiency: evidence from the COVID-19 pandemic

PurposeThe present study aims to investigate the impact of internal control effectiveness on supply chain management efficiency (SCME) and capital allocation efficiency for companies listed in the Tehran Stock Exchange (TSE). In addition, it investigates the mediating role of supply chain management efficiency in the relationship between internal controls and capital allocation efficiency.Design/methodology/approachThe data about 191 companies in 2014–2022 were examined. The sales per inventory ratio was used to calculate SCME. The present study also applied the Generalized Method of Moments (GMM) for endogeneity concerns.FindingsThe results showed that internal control effectiveness has a significant positive effect on SCME. Moreover, internal control effectiveness and SCME significantly positively affect capital allocation efficiency. SCME has a mediating role in the relationship between internal control effectiveness and capital allocation efficiency. These findings remained robust even after several robustness tests. In addition, this study tested the results' robustness by dividing data into the pre-COVID-19 and post-COVID-19 years. The previous results were also confirmed according to the robustness test of COVID-19.Originality/valueChallenges in the supply chain often hinder capital allocation efficiency. In addition, enterprises should try to establish strong internal controls to ensure SCME. Therefore, the relationship between internal control effectiveness, SCME and capital allocation efficiency is complex and underscores the importance of robust internal controls in optimizing resource allocation within organizations. Interestingly, this topic has not been extensively researched in accounting and business research, and there is a lack of empirical evidence on these effects. Consequently, this study aims to fill the gap and identify potential opportunities for new research directions.

Task Offloading and Resource Allocation for Augmented Reality Applications in UAV-Based Networks Using a Dual Network Architecture

This paper proposes a novel UAV-based edge computing system for augmented reality (AR) applications, addressing the challenges posed by the limited resources in mobile devices. The system uses UAVs equipped with edge computing servers (UECs) specifically to enable efficient task offloading and resource allocation for AR tasks with dependent relationships. This work specifically focuses on the problem of dependent tasks in AR applications within UAV-based networks. This problem has not been thoroughly addressed in previous research. A dual network architecture-based task offloading (DNA-TO) algorithm is proposed, leveraging the DNA framework to enhance decision-making in reinforcement learning while mitigating noise. In addition, a Karush–Kuhn–Tucker-based resource allocation (KKT-RA) algorithm is proposed to optimize resource allocation. Various simulations using real-world movement data are conducted. The results indicate that our proposed algorithm outperforms existing approaches in terms of latency and energy efficiency.

Developing a framework for identifying risk factors and estimating direct economic disease burden attributable to healthcare-associated infections: a case study of a Chinese Tuberculosis hospital

Healthcare-associated infections (HAIs) represent a major global health burden, which necessitate effective frameworks to identify potential risk factors and estimate the corresponding direct economic disease burden. In this article, we proposed a framework designed to address these needs through a case study conducted in a Tuberculosis (TB) hospital in Hubei Province, China, using data from 2018 to 2019. A comprehensive multistep procedure was developed, including ethical application, participant inclusion, risk factor identification, and direct economic disease burden estimation. In the case study, ethical approval was obtained, and patient data were anonymized to ensure privacy. All TB hospitalized patients over the study period were included and classified into groups with and without HAIs after screening the inclusion and exclusion criteria. Key risk factors, including gender, age, and invasive procedure were identified through univariate and multivariate analyses. Then, propensity score matching was employed to select the balanced groups with similar characteristics. Comparisons of medical expenditures (total medical expenditure, medicine expenditure, and antibiotics expenditure) and hospitalization days between the balanced groups were calculated as the additional direct economic disease burden measures caused by HAIs. This framework can serve as a tool for not only hospital management and policy-making, but also implementation of targeted infection prevention and control measures. Moreover, it has the potential to be applied in various healthcare settings at local, regional, national, and international levels to identify high-risk areas, optimize resource allocation, and improve hospital management and governance, as well as inter-organizational learning. Challenges to implement the framework are also raised, such as data quality, regulatory compliance, considerations on unique nature of communicable diseases and other diseases, and training need for professionals.

Artificial intelligence for enhancing resilience

In an increasingly complex and unpredictable world, resilience-the ability to withstand and recover from adverse conditions is essential across various sectors. This research paper investigates the transformative potential of artificial intelligence (AI) in enhancing resilience across multiple domains. We explore how AI technology can be utilized to develop resilient infrastructure, providing advanced predictive maintenance and real-time monitoring capabilities that ensure robustness and longevity. The study examines the role of AI in improving disaster response, offering rapid data analysis and decision-making support to enhance emergency management outcomes. In climate change, AI-driven strategies are assessed for their effectiveness in fostering climate resilience, including predictive modeling of extreme weather events and optimizing resource allocation. The paper also discusses AI applications in healthcare resilience, such as enhancing diagnostics, patient care, and operational efficiency during crises. Business continuity and crisis management are examined, highlighting AI's capability to anticipate disruptions and maintain operational stability. The paper emphasizes the importance of strengthening cybersecurity resilience using AI to detect and mitigate threats proactively. AI's role in enhancing community and social resilience is analysed, particularly in supporting vulnerable populations and fostering social cohesion. Additionally, we explored AI-powered solutions for urban resilience, focusing on smart cities and sustainable development. The study also covers AI's contributions to environmental and ecological resilience, resilient supply chain management, and resilience in the hospitality and tourism industry. Finally, we investigated AI's potential in fostering psychological resilience, providing personalized mental health support and stress management tools. Through these diverse applications, the paper underscores AI's critical role in building a resilient future.

A novel user clustering and efficient resource allocation in non-orthogonal mutliple access for IoT networks.

Optimal resource allocation is crucial for 5G and beyond networks, especially when connecting numerous IoT devices. In this paper, user clustering and power allocation challenges in the downlink of a multi-carrier NOMA system are investigated, with sum rate as the optimization objective. The paper presents an iterative optimization process, starting with user clustering followed by power allocation of the users. Although the simultaneous transmission for multiple users achieves high system throughput in NOMA, it leads to more energy consumption, which is limited by the battery capacity of IoT devices. Enhancing energy efficiency by considering the QoS requirement is a primary challenge in NOMA-enabled IoT devices. Currently, fixed user clustering techniques are proposed without considering the diversity and heterogeneity of channels, leading to poor throughput performance. The proposed user clustering technique is based on the partial brute force search (P-BFS) method, which reduces complexity compared to the traditional exhaustive search method. After the user clustering, we performed optimal power allocation using the Lagrangian multiplier method with Karush-Kuhn-Tucker (KKT) optimal conditions for each user assigned to a subchannel in each cluster. Lastly, a deep neural network (DNN) based proposed P-BFS scheme is used to reduce resource allocation's complexity further. The simulation results show a significant improvement in the sum rate of the network.

Synergistic effect of carbon abatement and energy saving of digital infrastructure in China: Evidence from 284 cities

Digital infrastructure construction is paving an explorable path for carbon abatement and energy saving. Whether China's Broadband China Strategy (BCS) contributes to carbon abatement and energy saving is yet to be explored in depth. Using the dataset of 284 cities during 2010–2021 in China, this research applies staggered difference-in-differences (DID) to assess whether and how the BCS affects carbon abatement and energy saving. The results show that BCS significantly promotes the city's carbon abatement and energy saving, but the beneficial effects do not appear until the fourth year of the BCS implementation. The promotion of the BCS demonstration cities has contributed to carbon abatement and energy saving mainly by expanding scale aggregation, optimizing resource allocation, and stimulating low-carbon innovation related to energy. Heterogeneity analysis reveals that the carbon abatement and energy saving effects of BCS are more noticeable in eastern and larger economic scale cities. Non-resource-based cities benefit more from BCS's carbon abatement effects, while resource-based cities benefit more from BCS's energy saving effects. Furthermore, BCS shows a siphon effect on carbon abatement and energy saving in geographically neighboring cities, while it shows a demonstration effect in economically neighboring cities.

Improving efficiency in the emergency department Monte Carlo simulation for determining the optimal number of staff

This paper examines the application of Monte Carlo simulation to determine optimal staffing levels at reception desks in the Emergency Department. The study utilizes data collected from a hospital in Bogotá, with all data anonymized to maintain the confidentiality of both the institution and its patients. By leveraging programming tools, the study randomizes the data and models various scenarios to assess the staffing requirements accurately. The primary goal is to enhance the efficiency and quality of service by aligning staffing levels with patient demand. The use of historical data, combined with the simulation of hypothetical scenarios, provides a robust basis for predicting future needs and making informed staffing decisions. The study's findings offer valuable insights into human resources management, enabling the Emergency Department to strategically allocate personnel, minimize wait times, and improve overall patient care. This approach demonstrates the potential for simulation-based models to optimize resource allocation in critical healthcare environments.

Measuring and Mitigating the Risk of Advanced Cyberattackers

Sophisticated cyberattackers (commonly known as advanced persistent threats (APTs)) pose enormous risks to organizations such as financial institutions, industrial and commercial firms, government institutions, and power grids. This study presents a method and an index to measure the vulnerability of organizations to APT risk and shows why a one-size-fits-all solution to mitigate APT risk does not exist. Our vulnerability index is based on a model that describes the optimal behavior of a cyberattacker (APT) with research and development capabilities aspiring to attack a network that manages the organization and a network operator that deploys blocking and detection measures to protect its organization from the attack. We demonstrate how our vulnerability index, which accounts for the network’s structure and the APTs’ resources and strategy, can be used in realistic risk assessments and optimal resource allocation procedures and serve as a benchmark for organizations’ preparedness against APTs’ cyberattacks. We also propose that regulatory agencies of financial (and other) institutions provide the parameters that define an APT’s profile and request, as part of their periodic assessments of the organizations that they regulate, that our (or similar) vulnerability index will be reported to them by the regulated institutions. Finally, the viability of our index in modeling modern cybersecurity defense procedures shows that not only there is no silver bullet defense against all types of APTs, it is also imperative to account for APTs’ heterogeneity because detection and blocking measures can be complements, substitutes, or even degrade each other. For example, when the attacker’s (defender’s) budget is extremely large (small), the defender should deploy only detection measures, strongly advocating Zero Trust practices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0072 .

Genotype-specific morphophysiological adaptations and proline accumulation uncover drought adaptation complexity in hemp (Cannabis sativa and Cannabis indica)

Hemp, which has a wide range of industrial applications, has been marginalized due to its association with marijuana. This stigma has hindered research into improving its resilience to various stressors, resulting in underutilization and neglect. As cultivation expands globally, particularly in hot, dry regions of Africa, understanding drought stress mechanisms in hemp is crucial. This study investigates the drought adaptation mechanisms of three CBD flower hemp genotypes: Cannabis indica (MP) from Switzerland, Cannabis sativa (AQ) from South Africa, and Cannabis sativa (ZB) from Zimbabwe. Conducted under well-watered (WW-75% field capacity [FC]), mild drought (MD-40% FC), and severe drought (SD-0% FC) conditions, the research examines morphophysiological adaptations and proline accumulation in these genotypes, assessed 55 days after transplanting. Results revealed genotype-specific responses to watering regimes. MP demonstrated controlled water use and inherent drought tolerance, maintaining high assimilation rates (A) and superior photosynthetic performance (ΦPSII) under drought conditions. ZB maintained proline levels during drought recovery, suggesting optimized resource allocation and alternative stress-responsive mechanisms, while exhibiting effective morning water use and high non-photochemical quenching (NPQ) for photoprotection. AQ showed conservative water use strategies beneficial in water-limited environments. These findings provide a foundation for breeding programs aimed at developing robust and resilient hemp varieties suited to specific environmental conditions.

Research on the synergy of the water–energy–food composite system in the Beijing–Tianjin–Hebei region

ABSTRACT Water, energy, and food are interdependent and mutually restrictive, and changes in any one resource may have an impact on the others. In order to promote the sustainable utilization of resources, it is important to study the synergy of the water–energy–food composite system (WEFCS). Taking the Beijing–Tianjin–Hebei region as an example, this study adopts the composite system synergy degree model to quantitatively measure the synergy degree of WEFCS from 2011 to 2021, then constructs an evolutionary model to identify the interactions between the subsystems, and finally analyzes the interactions dynamically by using the panel vector autoregression model. The results show that the WEFCS synergy level in the Beijing–Tianjin–Hebei region is generally basic and shows low stability during the study period. Regional subsystems are more dependent than collaborative or competitive. Moreover, the regional water subsystem has a short-term positive impact on the energy subsystem, the energy subsystem has a short-term negative impact on the food subsystem, and the food subsystem has a delayed and complex influence on the water and energy subsystems, with the water subsystem being particularly affected. This study provides a decision-making basis for policymakers to optimize resource allocation.

The Physiological Mechanism of Exogenous Melatonin on Improving Seed Germination and the Seedling Growth of Red Clover (Trifolium pretense L.) under Salt Stress.

Salt stress can affect various physiological processes in plants, ultimately hindering their growth and development. Melatonin (MT) can effectively resist multiple abiotic stresses, improving plant stress resistance. To analyze the mechanism of exogenous MT to enhance salt tolerance in red clover, we conducted a comprehensive study to examine the influence of exogenous MT on various parameters, including seed germination indices, seedling morphological traits, and physiological and photosynthetic indicators, using four distinct red clover varieties (H1, H2, H3, and H4). This investigation was performed under various salt stress conditions with differing pH values, specifically utilizing NaCl, Na2SO4, NaHCO3, and Na2CO3 as the salt stressors. The results showed that MT solution immersion significantly improved the germination indicators of red clover seeds under salt stress. The foliar spraying of 50 μM and 25 μM MT solution significantly increased SOD activity (21-127%), POD activity, soluble sugar content, proline content (22-117%), chlorophyll content (2-66%), and the net photosynthetic rate. It reduced the MDA content (14-55%) and intercellular CO2 concentration of red clover seedlings under salt stress. Gray correlation analysis and the Mantel test further verified that MT is a key factor in enhancing seed germination and seedling growth of red clover under salt stress; the most significant improvement was observed for NaHCO3 stress. MT is demonstrated to improve the salt tolerance of red clover through a variety of mechanisms, including an increase in antioxidant enzyme activity, osmoregulation ability, and cell membrane stability. Additionally, it improves photosynthetic efficiency and plant architecture, promoting energy production, growth, and optimal resource allocation. These mechanisms function synergistically, enabling red clover to sustain normal growth and development under salt stress.

Reducing costs or increasing efficiency? Unveiling the effects and mechanisms of the digital economy on sustained green innovation

This study examines the impact of the digital economy on sustainable green innovation in China, with a focus on the mediating roles of debt financing costs and resource allocation efficiency. Leveraging a comprehensive dataset of 24,741 firm-year observations from 2008 to 2022, we utilize fixed-effects regression models to analyze the relationships between the digital economy, debt financing costs, resource allocation efficiency, and sustainable green innovation. The results demonstrate that the digital economy positively influences sustainable green innovation, with this effect being partially mediated by reductions in debt financing costs and improvements in resource allocation efficiency. Specifically, the digital economy lowers financing costs and optimizes resource allocation, which collectively enhances green innovation sustainability. The findings remain consistent across various endogeneity and robustness checks, including instrumental variable approach, lagged effects, heckman two-stage method, sample adjustments, alternative measures, and model specifications. Additionally, heterogeneity tests reveal that the digital economy's positive effect is stronger for firms in the early and middle stages of their life cycle and for those operating in less-polluting industries. These insights contribute to the growing literature on digital technologies and sustainable development, offering valuable implications for managers and policymakers aiming to harness the digital economy for advancing sustainable green innovation.

Forecasting bridge damage within a predictive Structural Reliability-based DSS

Nowadays there is urgent need for expeditious and efficient approaches to assess the structural reliability of widespread aging bridge stocks. As such, a shift towards predictive maintenance measures is required. This paper addresses such pressing necessity for efficient methods to assess the structural state of aging bridges, centring its research question on developing a Decision Support System (DSS) that integrates an innovative reliability model with Markov Chain projections to prioritize bridge interventions. The proposed DSS effectively forecasts structural risks and computes a Priority Index to guide maintenance decisions. This tool empowers infrastructure managers with timely insights, enhancing bridge safety and optimizing resource allocation. The study's practical example underscores the DSS's applicability, demonstrating its capacity to streamline decision-making in real-world scenarios. By providing a sophisticated yet accessible approach to structural reliability assessment, this research lays the groundwork for future enhancements, including the incorporation of additional risk factors and refined calibration methods.

Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-Date Aerial Images and Ortho-Mosaics

Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks.

Inequity in the utilization of the home and community integrated healthcare and daily care services in older adults with limited mobility in China

BackgroundThis study aimed to analyze the needs and utilization of the home and community integrated healthcare and daily care services (“home and community care services” for short) among older adults in China and to investigate the inequity in services utilization.MethodsCross-sectional data were obtained from the 2018 China Health and Retirement Longitudinal Study. Needs and utilization rates of the home and community care services in older adults of 60 years old and above were analyzed. Binary logistic regression analysis was performed to explore the factors associated with services utilization among older adults with limited mobility. Concentration index, horizontal inequity index, and Theil index were used to analyze inequity in services utilization. Decomposition analyses of inequity indices were conducted to explain the contribution of different factors to the observed inequity.ResultsAbout 32.6% of older adults aged 60 years old and above had limited mobility in China in 2018, but only 18.5% of them used the home and community care services. Among the single service utilization, the highest using rate (15.5%) was from regular physical examination. Limited mobility, age group, income level, region, self-assessed health, and depression were statistically significant factors associated with utilization of any one type of the services. Concentration indices of any one type service utilization and regular physical examination utilization were both above 0.1, and the contribution of income to inequity were both over 60%. Intraregional factor contributed to about 90% inequity of utilizing any one type service, regular physical examination and onsite visit.ConclusionsThis current study showed that older adults with needs of home and community care services underused the services. Pro-rich inequities in services utilization were identified and income was the largest source of inequity. The difference of the home and community care service utilization was great among provinces but minor across regions. Policies to optimize resources allocation related to the home and community care services are needed to better satisfy the needs of older adults with limited mobility, especially in the low-income group and the central region.