Proactive Resource Allocation Research Articles

Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations.

Comparing resilience strategies for a multistage green supply chain to mitigate disruptions: A two-stage stochastic optimization model

Supply chain disruptions, notably exemplified by the COVID-19 pandemic, have substantial impacts, requiring effective resilience strategies. This study introduces a comprehensive multi-stage, multi-period green supply chain design model, incorporating six resilience strategies to tackle downstream and upstream disruptions. A rigorous two-stage stochastic optimization approach is employed to manage parameter uncertainties and disruptions. The objectives include minimizing disruption costs and CO2 emissions, for a chain that operates under cap-and-trade emission regulation. The findings, derived from a numerical experiment and sensitivity analysis, offer the optimal supply chain structure and effective resilience strategies to mitigate disruptions. The demand structure for essential and non-essential products during disruptions are explored, emphasizing proactive resource allocation. In particular, the impact of facility capacity reductions underscores the importance of capacity management. Sensitivity analyses reflect the trade-offs involved in capacity management strategies, emphasizing the critical need to maintain an optimal level of capacity to prevent service level degradation. Additionally, examination of carbon abatement regulations reveals the intricate balance between environmental responsibility and economic efficiency. In summary, this study addresses supply chain disruptions and offers actionable insights for supply chain managers and policy makers. The research highlights the importance of specific and data-driven resilience strategies to optimize supply chain performance in face of disruptions.

Innovation and Strategy in Tender Marketing PT Chop’s Approach to Enchance Tender Success

Introduction: This paper investigates the importance of innovative tender marketing strategies in enhancing PT Chop’s competitive positioning and ensuring continued success in the tendering process (PT Chop Tender Success Strategies). Data Collection Methods: Using a mixed-methods approach with surveys and interviews with 5 respondents. Data Analysis. The analysis will be employed the qualitative descriptive methods. Results and Discussion: PT Chop’s tender success hinges on financial strength, resourcefulness, experience, and connections. Strategic partnerships and cash flow management keep them competitive. Proactive resource allocation ensures they have the right talent for each project. Their proven track record and innovative solutions build trust with clients. Affiliations with various professionals provide a comprehensive understanding of project requirements. To further excel, PT Chop can target niche markets, invest in advanced project management tools, and leverage online collaboration for better communication. They can also address weaknesses through regular technology training and develop value-added services. Finally, identifying risks during tender prep and implementing sustainable practices will help mitigate threats. Conclusion: Based on the findings, an strategy found to Enchance Tender Success of PT Chop.

Optimizing healthcare resource allocation through data-driven demographic and psychographic analysis

This paper explores the multifaceted nature of healthcare resource allocation, examining demographic and psychographic analysis, data sources and analysis techniques, challenges, future directions, and innovations. Demographic analysis provides insights into population characteristics, healthcare demand, and disparities, informing targeted resource allocation strategies. Psychographic analysis complements demographic data by elucidating patient attitudes, behaviors, and preferences, guiding personalized care delivery and intervention design. A diverse array of data sources, including electronic health records, surveys, and geospatial data, underpins healthcare resource allocation. Advanced analysis techniques such as predictive analytics and machine learning unlock actionable insights from these data sources, enabling proactive resource allocation and optimization. Challenges such as data quality, bias, and ethical considerations underscore the complexity of healthcare resource allocation. However, emerging innovations, including artificial intelligence, precision medicine, and value-based care models, offer transformative potential for optimizing resource allocation and improving patient outcomes. Looking ahead, the integration of innovative technologies and patient-centered care models promises to reshape healthcare resource allocation, emphasizing equity, efficiency, and patient engagement. By navigating challenges thoughtfully and embracing future opportunities, healthcare systems can enhance resource allocation strategies to better meet the evolving needs of patients and communities. This paper explores the multifaceted nature of healthcare resource allocation, examining demographic and psychographic analysis, data sources and analysis techniques, challenges, future directions, and innovations. Demographic analysis provides insights into population characteristics, healthcare demand, and disparities, informing targeted resource allocation strategies. Psychographic analysis complements demographic data by elucidating patient attitudes, behaviors, and preferences, guiding personalized care delivery and intervention design. A diverse array of data sources, including electronic health records, surveys, administrative databases, geospatial data, and social determinants of health data, underpins healthcare resource allocation. Advanced analysis techniques such as predictive analytics, machine learning, spatial analysis, and simulation modeling unlock actionable insights from these data sources, enabling proactive resource allocation and optimization. Keywords: Healthcare Resource Allocation, Demographic Analysis, Psychographic Analysis, Data Sources, Advanced Analysis Techniques, Future Directions.

Proactive Resource Allocation for Cloud Manufacturing: A Dynamic Approach with DNN-Based Hybrid Many Objective Leopard Seal Algorithm

In the domain of cloud manufacturing, diverse services and the escalating demand for efficient and cost-effective service composition selection in cloud manufacturing have spurred day by day. Traditional methods face several challenges such as the risk of convergence issues and suboptimal solutions, high dimensionality of decision space, increased computational complexity, and scalability issues. To overcome these complexities, this paper proposed a novel method named the Deep Neural Network (DNN) based Hybrid Many Objective Leopard Seal (HMOLS) algorithm. In this study, DNN is employed to predict future Quality of Service (QoS) for each service based on historical data and integrated features. These predictions are fed back into the optimization model, influencing the dynamic weight calculation for each objective function. In this work, the Leopard Seal Optimization is implemented for hyperparameter optimization to enhance the performance of the HMOLS algorithm. Various evaluation metrics namely accuracy, throughput, reliability, resource utilization, and scalability are utilized to evaluate the performance of the HMOLS method and compare its performance with existing methods. The experimental outcomes depict the effectiveness of the HMOLS approach for the effective selection of services and fusion for cloud manufacturing. DNN-based predictions utilize deep neural networks to predict resource demand or utilization patterns and enable more accurate allocation decisions. These predictions optimize resource allocation by reallocating resources accordingly and address the challenges of resource utilization. Meanwhile, this method is required to overcome the computational overhead for applicability in cloud manufacturing.

Healthcare Resource Allocation Optimization

Assuring high-quality patient care and making the best use of the resources at hand requires the efficient allocation of healthcare resources. This study uses a thorough examination of patient admission data, treatment plans, and staff availability to tackle the complex problem of optimizing healthcare resource allocation. We want to reduce wait times, optimize resource use, and eventually improve patient outcomes in healthcare facilities by integrating these essential elements. Our strategy makes use of cutting-edge data analytics tools, such as optimization algorithms and predictive modelling, to find underlying patterns and trends in patient demand, treatment regimens, and staffing needs. Our goal is to create prediction models that anticipate patient admissions, treatment requirements, and staffing levels by analysing historical data and real-time inputs. This will allow for proactive resource allocation techniques. The study's conclusions have important ramifications for healthcare administration since they provide practical advice on how to improve patient care delivery, cut wait times, and make the most use of the resources at hand. Healthcare institutions can enhance resource allocation procedures to meet the changing demands of patient care while guaranteeing the optimal use of resources by integrating technology, data-driven decision-making, and evidence-based practices. In summary, this study lays the groundwork for future investigations and innovations in hospital administration and operations while also supporting current initiatives to enhance healthcare resource allocation procedures. Healthcare organizations can improve patient outcomes and achieve operational excellence while providing high-quality, patient-centred care by adopting data-driven approaches and utilizing technology.

Proactive Resource Allocation for Aggregated VLC/RF Networks With Outdated CSI

Proactive Resource Allocation for Aggregated VLC/RF Networks With Outdated CSI

Harnessing Artificial Intelligence for Early Detection and Management of Infectious Disease Outbreaks

Infectious diseases pose ongoing threats to global public health, demanding advanced detection methods for effective outbreak management. This study explores integrating artificial intelligence (AI) for early detection and management. AI algorithms analyze diverse datasets, including electronic health records and social media, to identify potential outbreaks. Machine learning models predict disease spread and severity, aiding proactive resource allocation. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses AI's role in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study also evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. Ethical considerations are crucial, emphasizing collaboration between public health agencies, healthcare providers, and technology experts. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. This paper advocates for AI integration to enhance infectious disease surveillance, offering a proactive response to safeguard public health.

Land use planning and climate change adaptation in river-dependent communities in Nigeria

Land use planning and climate change adaptation are critical for ensuring sustainable development and resilience in river-dependent communities in Nigeria. This study examines land use planning and climate adaptation in river-dependent communities in the Lower Niger River region of Nigeria. The research utilizes a mixed-methods approach. Data for the study were gathered from 198 households through surveys, complemented by 17 qualitative interviews and remote sensing techniques to assess land use changes and community perceptions. Findings reveal a notable trend where poor farmers face heightened vulnerability to climate change due to financial constraints, hindering their adoption of adaptive land use strategies. Specifically, more than half of the sampled households possess individual land for agricultural purposes yet lack the resources to effectively mitigate climate-related risks. Among the studied communities, Odekpe emerged as particularly vulnerable, with a prevalence of vulnerability at 32.3% and higher exposure to flood-prone zones.The quantitative analysis delineates community engagement in land management practices, showcasing proactive resource allocation and conflict resolution efforts among over 60% of participants. However, a significant portion (57.6%) lacked awareness of hydrological changes, potentially impeding effective land use planning and adaptation measures.Further analysis through remote sensing and mapping techniques revealed shifts in land use land cover, highlighting a transition from predominantly vegetated areas to built-up regions over the past three decades. The flood vulnerability assessment maps underscored substantial vulnerability across the Lower Niger basin, especially in downstream regions, indicating heightened risks due to proximity to the Niger River and heavy rainfall.Qualitative insights from stakeholder interviews identified challenges impeding effective land use planning, including uncertain climate projections, insufficient data, institutional capacity constraints, and conflicting priorities. Additionally, participants emphasized the need for resilient land use decisions to address urbanization impacts, altered drainage patterns, and diminishing green spaces. The study recommends several key actions to enhance the effectiveness of land use planning and climate adaptation in the Lower Niger River region. These include the imperative to strengthen institutional capacities, improve stakeholder engagement processes, integrate traditional knowledge and practices into planning efforts, fortify legal frameworks governing land use, and prioritize the promotion of sustainable livelihoods among local communities.

PROVIDENT: Development and Validation of a Machine Learning Model to Predict Neighborhood-level Overdose Risk in Rhode Island.

Drug overdose persists as a leading cause of death in the United States, but resources to address it remain limited. As a result, health authorities must consider where to allocate scarce resources within their jurisdictions. Machine learning offers a strategy to identify areas with increased future overdose risk to proactively allocate overdose prevention resources. This modeling study is embedded in a randomized trial to measure the effect of proactive resource allocation on statewide overdose rates in Rhode Island (RI). We used statewide data from RI from 2016 to 2020 to develop an ensemble machine learning model predicting neighborhood-level fatal overdose risk. Our ensemble model integrated gradient boosting machine and super learner base models in a moving window framework to make predictions in 6-month intervals. Our performance target, developed a priori with the RI Department of Health, was to identify the 20% of RI neighborhoods containing at least 40% of statewide overdose deaths, including at least one neighborhood per municipality. The model was validated after trial launch. Our model selected priority neighborhoods capturing 40.2% of statewide overdose deaths during the test periods and 44.1% of statewide overdose deaths during validation periods. Our ensemble outperformed the base models during the test periods and performed comparably to the best-performing base model during the validation periods. We demonstrated the capacity for machine learning models to predict neighborhood-level fatal overdose risk to a degree of accuracy suitable for practitioners. Jurisdictions may consider predictive modeling as a tool to guide allocation of scarce resources.

RLPRAF: Reinforcement Learning-Based Proactive Resource Allocation Framework for Resource Provisioning in Cloud Environment

RLPRAF: Reinforcement Learning-Based Proactive Resource Allocation Framework for Resource Provisioning in Cloud Environment

A Proactive Resource Allocation Algorithm for UAV-assisted V2X Communication Based on Dynamic Multi-objective Optimization

A Proactive Resource Allocation Algorithm for UAV-assisted V2X Communication Based on Dynamic Multi-objective Optimization

Proactive Resource Allocation Policy for Microsoft Azure Cognitive Search

Modern cloud services aim to find the middle ground between quality of service and operational cost efficiency by allocating resources if and only if these resources are needed by the customers. Unfortunately, most industrial demand-driven resource allocation approaches are reactive. Given that scaling mechanisms are not instantaneous, the reactive policy may introduce delays to latency-sensitive customer workloads and waste operational costs for cloud service providers. To solve this catch-22, we define the proactive resource allocation policy for Microsoft Azure Cognitive Search. In addition to the current resource demand, the proactive policy takes the typical resource usage patterns into account. We gained the following valuable insights from these patterns over several months of production workloads. One, 87% of the workload is stable due to continuous resource demand. Two, 90% of varying demand is predictable based on a few weeks of historical traces. Three, resources can be reclaimed 52% of the time due to extensive idle intervals of varying workload. Given the size and scope of our analysis, we believe that our approach applies to any latency-sensitive cloud service.

Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries

Geographical mapping has revolutionized data analysis with the help of analytical tools in the fields of social and economic studies, whereby representing statistical research variables of interest as geographic characteristics presents visual insights. This study employed the QGIS mapping tool to create predicted choropleth maps of Visegrád Group countries based on crime rate. The forecast of the crime rate was generated by time series analysis using the ARIMA (autoregressive integrated moving averages) model in SPSS. The literature suggests that many variables influence crime rates, including unemployment. There is always a need for the integration of widespread data insights into unified analyses and/or platforms. For that reason, we have taken the unemployment rate as a predictor series to predict the future rates of crime in a comparative setting. This study can be extended to several other predictors, broadening the scope of the findings. Predictive data-based choropleth maps contribute to informed decision making and proactive resource allocation in public safety and security administration, including police patrol operations. This study addresses how effectively we can utilize raw crime rate statistics in time series forecasting. Moreover, a visual assessment of safety and security situations using ARIMA models in SPSS based on predictor time-series data was performed, resulting in predictive crime mapping.

Weather-Aware Fiber-Wireless Traffic Prediction Using Graph Convolutional Networks

In recent years, there has been an increased demand for better and faster communication networks to meet the needs of Beyond 5G network. The fiber-wireless integrated network has been widely investigated, taking advantage of the large capacity and low transmission loss properties of the optical fiber to extend the coverage of wireless networks and the cellular networks in particular. To optimize the resource allocation in these networks, machine learning (ML) techniques have been proposed, which aim to predict the cellular traffic in advance to allow proactive resource allocation. Existing works mainly consider two factors in traffic prediction: the spatial correlation amongst nearby based stations and the temporal dynamics observed in historical records. In this paper, we study a crucial and yet unexplored aspect, i.e., meteorological factors, such as rain, wind, or temperature. We first perform statistical analysis on a real-world dataset, the results of which confirm the strong impact of meteorological factors upon traffic volume. Thereafter, we propose a traffic prediction model that captures the temporal, spatial, and meteorological patterns simultaneously. The proposed model learns the network traffic patterns through a novel graph convolutional network - gated recurrent unit (GCN-GRU) cell. The GCN-GRU has a hierarchical structure with two child GRUs capturing the temporal dynamics in traffic and meteorological records respectively and a parent GRU capturing the unified impact of historical traffic and meteorological data upon future traffic. The spatial correlation among traffic records is captured by a graphical convolution module within the proposed GCN-GRU cell. We conduct extensive experiments on real-world datasets. The results confirm the effectiveness of the proposed model, with a performance improvement by up to 24.8% achieved using the hierarchical GCN-GRU model.

Adaptive Runtime Monitoring of Service Level Agreement Violations in Cloud Computing

The cloud service level agreement (SLA) manage the relationship between service providers and consumers in cloud computing. SLA is an integral and critical part of modern era IT vendors and communication contracts. Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers, the SLA emerges as a key aspect between the consumers and providers. Continuous monitoring of Quality of Service (QoS) attributes is required to implement SLAs because of the complex nature of cloud communication. Many other factors, such as user reliability, satisfaction, and penalty on violations are also taken into account. Currently, there is no such policy of cloud SLA monitoring to minimize SLA violations. In this work, we have proposed a cloud SLA monitoring policy by dividing a monitoring session into two parts, for critical and non-critical parameters. The critical and non-critical parameters will be decided on the interest of the consumer during SLA negotiation. This will help to shape a new comprehensive SLA based Proactive Resource Allocation Approach (RPAA) which will monitor SLA at runtime, analyze the SLA parameters and try to find the possibility of SLA violations. We also have implemented an adaptive system for allocating cloud IT resources based on SLA violations and detection. We have defined two main components of SLA-PRAA i.e., (a) Handler and (b) Accounting and Billing Manager. We have also described the function of both components through algorithms. The experimental results validate the performance of our proposed method in comparison with state-of-the-art cloud SLA policies.

Confidence Aware Deep Learning Driven Wireless Resource Allocation in Shared Spectrum Bands

Deep learning (DL) driven proactive resource allocation (RA) is a promising approach for the efficient management of network resources. However, DL models typically have a limitation that they do not capture the uncertainty due to the arrival of new unseen samples with a distribution different than the data distribution available at DL model-training time, leading to wrong resource usage predictions. To address this, we propose a confidence aware DL solution for the robust and reliable predictions of wireless channel utilization (CU) in shared spectrum bands. We utilize an encoder-decoder based Bayesian DL model to generate prediction intervals which capture the uncertainties in wireless CU. We use the CU predictions to design a novel metric score which in turn is utilized to make an adaptive RA algorithm. We show that a DL model capturing uncertainty in CU can achieve higher data rates for a wireless network. Both DL driven predictions and RA models are tested using synthetic data as well as real CU data collected in the University of Oulu. Using analytical and simulations results, we also study the stability of the proposed RA algorithm and show that it converges to a Nash equilibrium (NE). Our results reveal that the proposed algorithm converges to an NE under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2N $ </tex-math></inline-formula> iterations where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N $ </tex-math></inline-formula> is the number of network access points.

Proactive Radar Protection System in Shared Spectrum via Forecasting Secondary User Power Levels

Spectrum sharing in radar bands with interference forecasting for enhanced radar protection can help design proactive resource allocation solutions which can achieve high data rates for wireless communication networks on one hand and help protect the incumbent radar systems. We consider radar spectrum sharing in 5.6GHz where a weather radar operates as a primary system and the dominant secondary system is an enterprise network consisting of access points (APs) in a university campus. Our work models transmit the power of the APs as a time series with multinomial distribution based on real collected data. The aggregated interference due to the transmissions from the APs at the radar is forecasted using a long short-term memory (LSTM) based neural network. Monte Carlo dropout is utilized to generate prediction intervals that capture the uncertainties in the interference from the APs. Finally, by using both average and upper limits of predicted interference time series a cloud-assisted efficient sharing and radar protection algorithm is proposed. Tracking the rotating radar is not required in the proposed system. The results show that the proposed efficient sharing and radar protection system ensures better radar protection and increased throughput for wireless communication users.

QoS and QoE aware multi objective resource allocation algorithm for cloud gaming

Cloud gaming is an innovative model that congregates video games. The user may have different Quality-of-Experience (QoE), which is a term used to measure a user’s level of satisfaction and enjoyment for a particular service. To guarantee general satisfaction for all users with limited cloud resources, it becomes a major issue in the cloud. This paper leverages a game theory in the cloud gaming model with resource optimization to discover optimal solutions to resolve resource allocation. The Rider-based harmony search algorithm (Rider-based HSA), which is the combination of Rider optimization algorithm (ROA) and Harmony search algorithm (HSA), is proposed for resource allocation to improve the cloud computing system’s efficiency. The fitness function is newly devised considering certain QoE parameters, which involves fairness index, Quantified experience of players (QE), and Mean Opinion Score (MOS). The proposed Rider-based HSA showed better performance compared to Potential game-based optimization algorithm, Proactive resource allocation algorithm, QoE-aware resource allocation algorithm, Distributed algorithm, and Yusen Li et al., with maximal fairness of 0.999, maximal MOS of 0.873, and maximal QE of 1.

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.