Resource Allocation Management Research Articles

Energy-Efficient Optimization in Distributed Massive MIMO Systems for Slicing eMBB and URLLC Services

The fifth generation (5G) communication system aims to support diverse services such as enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC). In order to enable the coexistence of eMBB and URLLC services on a common physical infrastructure, network slicing incorporated in the distributed massive multiple-input multiple-output (DM-MIMO) system is regarded as a promising solution. In this paper, a non-orthogonal slicing scheme is proposed to improve the spectral efficiency, and the short packet transmission is adopted for URLLC to meet the low latency requirement. Through a joint optimization of beamforming and remote radio unit (RRU) selection, we investigate the energy efficiency (EE) maximization problem for the downlink DM-MIMO system. The formulated problem is a mixed integer nonlinear program (MINLP) that we solve by applying some efficient approaches, such as the Dinkelbach method, the reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm and difference of convex (DC) programming. By combining these approaches in one iteration, a double-loop procedure based on semi-definite relaxation (SDR) and successive convex approximation (SCA) is employed to find the suboptimal solution of the problem. Simulation results show that the proposed scheme significantly improves the EE of the DM-MIMO system and efficiently manages resource allocation.

Dynamic User Routing, Paid and Free Users, Web applications, Content Delivery Network (CDN)

Abstract: This research paper introduces a novel approach for efficient routing between paid and free users in web applications by harnessing the capabilities of a Content Delivery Network (CDN). The primary objective of this study is to optimize user experience and resource allocation based on user type, aiming to deliver personalized content while maximizing resource utilization. The proposed routing mechanism dynamically directs users to the appropriate pages or content based on their subscription status, leveraging the integration of a CDN into the web application architecture. The paper thoroughly explores the implementation details, emphasizing the seamless interaction between the CDN and user authentication mechanisms, and investigates the routing algorithms employed to intelligently determine the most suitable content delivery path for each user type. Through comprehensive experimentation and evaluation, the research demonstrates the efficiency and scalability of the proposed approach. Various user scenarios are considered, and factors such as response time, resource utilization, and content availability are evaluated. The experimental results showcase the significant enhancement of the overall user experience achieved through the dynamic user routing mechanism, providing tailored content delivery while effectively managing resource allocation. The findings of this research contribute to the field of web application development, particularly in managing different user types and delivering personalized content. By effectively leveraging CDNs and implementing dynamic routing strategies, web applications can optimize user satisfaction, ensure efficient resource utilization, and accommodate varying user subscription models. This research offers valuable insights and practical implications for designing and implementing usercentric web applications that deliver customized experiences and efficiently allocate resources based on user types. The paper delves into the implementation details, highlighting the key components and functionalities of the proposed solution. It explores the integration of the CDN into the web application architecture, emphasizing the seamless interaction between the CDN and user authentication mechanisms. Additionally, it investigates the routing algorithms employed to intelligently determine the appropriate content delivery path for each user type.

Management of Optimal Resource Allocation in the Cloud

Management of Optimal Resource Allocation in the Cloud

A hierarchical constraint-based graph neural network for imputing urban area data

Urban area data are strategically important for public safety, urban management, and planning. Previous research has attempted to estimate the values of unsampled regular areas, while minimal attention has been paid to the values of irregular areas. To address this problem, this study proposes a hierarchical geospatial graph neural network model based on the spatial hierarchical constraints of areas. The model first characterizes spatial relationships between irregular areas at different spatial scales. Then, it aggregates information from neighboring areas with graph neural networks, and finally, it imputes missing values in fine-grained areas under hierarchical relationship constraints. To investigate the performance of the proposed model, we constructed a new dataset consisting of the urban statistical values of irregular areas in New York City. Experiments on the dataset show that the proposed model outperforms state-of-the-art baselines and exhibits robustness. The model is adaptable to numerous geographic applications, including traffic management, public safety, and public resource allocation.

Digital Twins: The New Frontier for Personalized Medicine?

Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, and physiological processes. Their application has the potential to transform patient care in the direction of increasingly personalized data-driven medicine. The use of DHTs can be integrated with digital twins of healthcare institutions to improve organizational management processes and resource allocation. By modeling the complex multi-omics interactions between genetic and environmental factors, DHTs help monitor disease progression and optimize treatment plans. Through digital simulation, DHT models enable the selection of the most appropriate molecular therapy and accurate 3D representation for precision surgical planning, together with augmented reality tools. Furthermore, they allow for the development of tailored early diagnosis protocols and new targeted drugs. Furthermore, digital twins can facilitate medical training and education. By creating virtual anatomy and physiology models, medical students can practice procedures, enhance their skills, and improve their understanding of the human body. Overall, digital twins have immense potential to revolutionize healthcare, improving patient care and outcomes, reducing costs, and enhancing medical research and education. However, challenges such as data security, data quality, and data interoperability must be addressed before the widespread adoption of digital twins in healthcare. We aim to propose a narrative review on this hot topic to provide an overview of the potential applications of digital twins to improve treatment and diagnostics, but also of the challenges related to their development and widespread diffusion.

Environmental Value Assessment of Plastic Pollution Control: A Study Based on Evidence from a Survey in China

A scientific environmental management decision is based on the correct assessment of environmental value. Aiming to objectively and accurately assess the environmental value of plastic pollution treatment, in this paper, we design a choice experiment with four item characteristic attributes consisting of source reduction, recycling, cleanup and remediation and price. With the research data of 450 interviewed residents, a choice experiment method (CEM) and numerical simulation were used to comprehensively assess the environmental value of plastic pollution treatment in China, incorporating discount rates and future price changes of environmental products into the study. The results showed that: (1) residents’ willingness to pay per capita for source reduction, remediation and recycling was CNY 32.79, CNY 25.27 and CNY 15.78, respectively; (2) from the perspective of compensation surplus, the residents were willing to pay CNY 147.68 per capita for environmental improvement; (3) the dynamic curve of the value of plastic pollution control displayed an increasing, then gradually declining trend, and its total economic value of 100a was CNY 21,259.97 per capita; and (4) the model simulated the possible trajectory of future changes in plastic pollution control (three scenarios of constant, decreasing and increasing rates of development of plastic pollution control services) and found that addressing the plastic pollution problem early is more conducive to improving the overall welfare of society. This study can provide a basis for scientific evaluation of the benefits of plastic pollution management and allocation of pollution management resources.

A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management

This study deployed k-means clustering to formulate earthquake categories based on magnitude and consequence, using global earthquake data spanning from 1900 to 2021. Based on patterns within the historical data, numeric boundaries were extracted to categorize the magnitude, deaths, injuries, and damage caused by earthquakes into low, medium, and high classes. Following a future earthquake incident, the classification scheme can be utilized to assign earthquakes into appropriate categories by inputting the magnitude, number of fatalities and injuries, and monetary estimates of total damage. The resulting taxonomy provides a means of classifying future earthquake incidents, thereby guiding the allocation and deployment of disaster management resources in proportion to the specific characteristics of each incident. Furthermore, the scheme can serve as a reference tool for auditing the utilization of earthquake management resources.

Empowering communication networks with MMR scheduler: A novel approach to balancing user throughput and fairness

In the field of communication networks, the management of resource allocation is a crucial research area. To achieve efficient radio resource management, mobile telecommunications operators utilize opportunistic scheduling algorithms. This study presents a novel opportunistic scheduler, called MMR (Min–Max Rate), which aims to ensure equal treatment of users, good quality of service, and maximum overall system throughput simultaneously and improve network throughput and fairness by up to 70% compared to traditional and classical scheduling algorithms. To evaluate the performance of MMR, we compared it with other reference algorithms such as Round Robin, MaxSNR, and Proportional Fair, known for their scheduling efficiency and robustness. The simulation results obtained by the MATLAB software and analysis focused on fairness criteria and system throughput for different user groups, and we found that our proposed algorithm outperforms the existing algorithms.

Managing resource allocation for the recruitment stocking problem

This paper revisits the Recruitment Stocking Problem (RSP), in which a prescribed total number of kits must be allocated across multiple distribution sites, where streams of subjects arrive for kit pickup. A kit can be a test-kit for a new pharmaceutical drug, a medical device, a vaccine, a humanitarian relief survival kit, etc. RSP seeks to minimize the expected recruitment time — the time to recruit a prescribed target number of kits — by identifying an optimal or near-optimal kit allocation across all locations. To this end, we first develop a computational method for evaluating the expected recruitment time using the so-called Randomization Procedure. Specifically, since the recruitment process forms a finite-state continuous-time Markov process, the Randomization Procedure transforms the Markov process with varied transition rates from different states into one with a constant transition rate across all states, and thus substantially simplifies the RSP statistics computation. Further, we develop two search heuristics to efficiently search for optimal or near-optimal kit allocations, especially for large RSP models that are too computationally expensive to optimize by exhaustive search. Finally, we illustrate the efficacy of the proposed heuristics by numerical experiments, and then discuss practical implications and provide managerial insights.

Host, environment, and anthropogenic factors drive landscape dynamics of an environmentally transmitted pathogen: Sarcoptic mange in the bare-nosed wombat.

Understanding the spatial dynamics and drivers of wildlife pathogens is constrained by sampling logistics, with implications for advancing the field of landscape epidemiology and targeted allocation of management resources. However, visually apparent wildlife diseases, when combined with remote-surveillance and distribution modelling technologies, present an opportunity to overcome this landscape-scale problem. Here, we investigated dynamics and drivers of landscape-scale wildlife disease, using clinical signs of sarcoptic mange (caused by Sarcoptes scabiei) in its bare-nosed wombat (BNW; Vombatus ursinus) host. We used 53,089 camera-trap observations from over 3261 locations across the 68,401 km2 area of Tasmania, Australia, combined with landscape data and ensemble species distribution modelling (SDM). We investigated: (1) landscape variables predicted to drive habitat suitability of the host; (2) host and landscape variables associated with clinical signs of disease in the host; and (3) predicted locations and environmental conditions at greatest risk of disease occurrence, including some Bass Strait islands where BNW translocations are proposed. We showed that the Tasmanian landscape, and ecosystems therein, are nearly ubiquitously suited to BNWs. Only high mean annual precipitation reduced habitat suitability for the host. In contrast, clinical signs of sarcoptic mange disease in BNWs were widespread, but heterogeneously distributed across the landscape. Mange (which is environmentally transmitted in BNWs) was most likely to be observed in areas of increased host habitat suitability, lower annual precipitation, near sources of freshwater and where topographic roughness was minimal (e.g. human modified landscapes, such as farmland and intensive land-use areas, shrub and grass lands). Thus, a confluence of host, environmental and anthropogenic variables appear to influence the risk of environmental transmission of S. scabiei. We identified that the Bass Strait Islands are highly suitable for BNWs and predicted a mix of high and low suitability for the pathogen. This study is the largest spatial assessment of sarcoptic mange in any host species, and advances understanding of the landscape epidemiology of environmentally transmitted S. scabiei. This research illustrates how host-pathogen co-suitability can be useful for allocating management resources in the landscape.

Assessing performance of ZCTA-level and Census Tract-level social and environmental risk factors in a model predicting hospital events

Predictive analytics are used in primary care to efficiently direct health care resources to high-risk patients to prevent unnecessary health care utilization and improve health. Social determinants of health (SDOH) are important features in these models, but they are poorly measured in administrative claims data. Area-level SDOH can be proxies for unavailable individual-level indicators, but the extent to which the granularity of risk factors impacts predictive models is unclear. We examined whether increasing the granularity of area-based SDOH features from ZIP code tabulation area (ZCTA) to Census Tract strengthened an existing clinical prediction model for avoidable hospitalizations (AH events) in Maryland Medicare fee-for-service beneficiaries. We created a person-month dataset for 465,749 beneficiaries (59.4% female; 69.8% White; 22.7% Black) with 144 features indexing medical history and demographics using Medicare claims (September 2018 through July 2021). Claims data were linked with 37 SDOH features associated with AH events from 11 publicly-available sources (e.g., American Community Survey) based on the beneficiaries’ ZCTA and Census Tract of residence. Individual AH risk was estimated using six discrete time survival models with different combinations of demographic, condition/utilization, and SDOH features. Each model used stepwise variable selection to retain only meaningful predictors. We compared model fit, predictive performance, and interpretation across models. Results showed that increasing the granularity of area-based risk factors did not dramatically improve model fit or predictive performance. However, it did affect model interpretation by altering which SDOH features were retained during variable selection. Further, the inclusion of SDOH at either granularity level meaningfully reduced the risk that was attributed to demographic predictors (e.g., race, dual-eligibility for Medicaid). Differences in interpretation are critical given that this model is used by primary care staff to inform the allocation of care management resources, including those available to address drivers of health beyond the bounds of traditional health care.

Accounting for Weather Variability in Farm Management Resource Allocation in Northern Ghana: An Integrated Modeling Approach

Smallholder farmers in Northern Ghana face challenges due to weather variability and market volatility, hindering their ability to invest in sustainable intensification options. Modeling can help understand the relationships between productivity, environmental, and economical aspects, but few models have explored the effects of weather variability on crop management and resource allocation. This study introduces an integrated modeling approach to optimize resource allocation for smallholder mixed crop and livestock farming systems in Northern Ghana. The model combines a process-based crop model, farm simulation model, and annual optimization model. Crop model simulations are driven by a large ensemble of weather time series for two scenarios: good and bad weather. The model accounts for the effects of climate risks on farm management decisions, which can help in supporting investments in sustainable intensification practices, thereby bringing smallholder farmers out of poverty traps. The model was simulated for three different farm types represented in the region. The results suggest that farmers could increase their income by allocating more than 80% of their land to cash crops such as rice, groundnut, and soybeans. The optimized cropping patterns have an over 50% probability of increasing farm income, particularly under bad weather scenarios, compared with current cropping systems.

Smart Load-Based Resource Optimization Model to Enhance the Performance of Device-to-Device Communication in 5G-WPAN

In wireless personal area networks (WPANs), devices can communicate with each other without relying on a central router or access point. They can improve performance and efficiency by allowing devices to share resources directly; however, managing resource allocation and optimizing communication between devices can be challenging. This paper proposes an intelligent load-based resource optimization model to enhance the performance of device-to-device communication in 5G-WPAN. Intelligent load-based resource optimization in device-to-device communication is a strategy used to maximize the efficiency and effectiveness of resource usage in device-to-device (D2D) communications. This optimization strategy is based on optimizing the network’s resource load by managing resource utilization and ensuring that the network is not overloaded. It is achieved by monitoring the current load on the network and then adjusting the usage of resources, such as bandwidth and power, to optimize the overall performance. This type of optimization is essential in D2D communication since it can help reduce costs and improve the system’s performance. The proposed model has achieved 86.00% network efficiency, 93.74% throughput, 91.94% reduced latency, and 92.85% scalability.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

The popularization of Internet applications and rapid advent of information technologies have invited increased number of developers and companies to focus on the area of Cloud Computing (CC). The most significant issues and challenges in the domain of CC include Load Balancing (LB), scheduling of task executions and managing resource allocation. In particular, LB being the process of distributing computing resources and workloads has attracted maximum attention as it is the predominant issue in CC. In this paper, Hybrid Grey Wolf and Improved Particle Swarm Optimization Algorithm with Adaptive Intertial Weight-based multi-dimensional Learning Strategy (HGWIPSOA)-based LB scheme is proposed for enhancing precision and rapidness in task scheduling and assignment of resources to Virtual Machines (VMs) in cloud environments. In the proposed scheme, initially, Grey Wolf Optimization Algorithm (GWOA) is incorporated into Particle Swarm Optimisation (PSO) for considering the highest fitness particle as alpha wolf search agent, such that the objective of allocating tasks to VMs is attained effectively and efficiently. It then integrates chaos, Adaptive Inertial Weight (AIW) and Dimensional Learning (DL) into PSO specifically to prevent premature convergence and achieve better convergence speed and global search ability depending on the best experience determined by particles for effective LB. The simulation experiments of the proposed HGWIPSOA mechanism confirm better results by offering 21.32 % improved throughput, 19.84 % reduced makespan, 24.98 % minimized degree of imbalance, 18.74 % reduced latency and 27.92 % reduced execution time independent of the number of tasks in the cloud environment on par with benchmarked LB schemes.

DURATION OF IN-PATIENT PHYSICAL THERAPY AT A TERTIARY HOSPITAL: IMPLICATIONS OF A TIME AND MOTION STUDY

Introduction: In-patient physical therapy (PT) care entails careful provision of service for individuals across a spectrum of disorders needing evidence-based physical rehabilitation during their hospital admission. The main difficulty is identifying adequate time allocation for safe patient service within an eighthour working time frame. At a selected tertiary teaching government-run hospital, an arbitrary personnel-population ratio method is used for human resource allocation which may lead to issues in service delivery and healthcare workforce wellness. Apart from patient care, physical therapists also assume nonclinical roles revolving on administration, clinical education, and research. This highlights the importance of identifying the duration of tasks to assess work efficiency and manage time constraints posed by the limitations of the work shift. Practice guidelines may help improve staff workload scheduling, however, there is a lack of available guidelines regarding physical therapy practice in the hospital or acute care setting for developing countries. The purpose of this study was to determine the duration of care for inpatients with musculoskeletal and neurological conditions and administrative tasks, and to determine the relationship of duration of care with months of experience. Methods: A continuous observation time motion study of a typical workday of all physical therapists in an in-patient setting of a tertiary teaching hospital was conducted. An external observer will time the tasks done by the PT. The external observer kept distance from the direct patient encounter to minimize interference that may affect timing. Patients with conditions of different etiologies and functional levels were included. Results: Nineteen physical therapists with a mean experience of 54.53 months were observed. There were no adverse events during the implementation of this study. The total mean time in minutes of management of neurological patients is 37:32, and musculoskeletal patients is 28:30, respectively. Time for administrative tasks took an average of 20:33 minutes. There was also a low positive correlation on months of experience and treatment duration for patients with neurologic conditions (p = 0.0471). Results showed that the allocated treatment duration is determined to be sufficient for performing PT activities. The optimal number of patients to be decked can also be appropriately determined to optimize resource allocation. Discussion: Our pilot study attempted to quantify the duration of PT care in an inpatient setting that caters to patients with different diagnosis and varying needs for PT management. Although a weak to insignificant correlation was seen in most of the variables, there may be some benefit in gathering more duration data across different acute care settings within the country. Our pilot time and motion study can contribute to the limited evidence of duration of inpatient care that may inform human resource allocation, deliverables prioritization, and employee wellness and development. Figures have been presented which could be the basis for future policy research for management science and resource allocation studies.

Resource Allocation Energy Efficient Algorithm for H-CRAN in 5G

In today's generation, the demand for data rates has also increased due to the rapid surge in the number of users. With this increasing growth, there is a need to develop the next fifth generation network keeping in mind the need to replace the current 4G cellular network. The fifth generation (5G) design in mobile communication technology has been developed keeping in mind all the communication needs of the users. Heterogeneous Cloud Radio Access Network (H-CRAN) has emerged as a capable architecture for the newly emerging network infrastructure for energy efficient networks and high data rate enablement. It is considered as the main technology. Better service quality has been achieved by developing small cells into macro cells through this type of network. In addition, the reuse of radio resources is much better than that of homogeneous networks. In the present paper, we propose the H-CRAN energy-efficient methods. This energy-efficient algorithm incorporates an energy efficient resource allocation management design to deal to heterogeneous cloud radio access networks in 5G. System throughput fulfillment is elevating by incorporating an efficient resource allocation design by the energy consumption model. The simulation results have been demonstrated by comparing the efficiency of the introduced design with the existing related design.

Abstract WP196: Seasonal Variation In Stroke Ischemic Hospitalization: Results From The A Large Health System In Six Western States Of The United States.

Background: Evidence of seasonal variations in the incidence of stroke is inconsistent with some studies reporting no association and some a significant rise in different months of the year. In addition, less is known about how seasonality impacts the incidence of stroke subtype. Methods: This was a cross-sectional, observational study of data from a multi-state health system’s stroke registry (n=40 hospitals) in Alaska, California, Montana, Oregon, Texas, and Washington state. We included all cases with acute ischemic stroke admitted from September 29, 2016, to November 30, 2021. Admission data were categorized according to four meteorological seasons: winter, spring, summer, and fall. Acute ischemic stroke was categorized into two sub-types as large vessel occlusion (LVO) or non-LVO. We calculated the aggregate number of individuals admitted with stroke by season. Using, linear regression models with generalized estimating equations (GEE), we assessed the relationship between meteorological season and daily hospitalization rate. We used R version 4.0.4 (2021-02-15) for both the descriptive and inferential analyses and the R gee pack package (version 1.2-1) to perform GEEs. Results: During the study period, we identified 18,886 patients with acute ischemic stroke (median age: 73; 49.5% women). Acute ischemic stroke was more commonly observed during winter compared with other seasons with some variations between the selected regions. Based on a generalized estimating equations model, stroke hospitalization increased during winter, with an additional 3.3 cases per day (95%, CI [2.4, 4.1]). Winter is also associated with a higher incidence of large vessel occlusion. Conclusion: The incidence of ischemic stroke, including cases of large vessel occlusion, increased during the winter months. The results are important for human resource allocation for better management of cases with ischemic strokes.

Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic.

Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.

Airway Management of Suspected Traumatic Brain Injury Patients in the Emergency Room

AbstractThe patients of trauma offers a special challenge because of the associated head injury, maxillofacial, neck and spine injuries, which puts the airway at imminent risk. The response time for the emergency team to initiate the airway management determines the outcome of the individual undergoing treatment. A judious implementatin of triage and Advanced Trauma Life Suport (ATLS) guidelines are helpful in the allocation of resources in airway management of trauma patients. One must not get distracted with the severity of other organ systems because cerebral tissue permits a low threshold to the hypoxic insults. Adequate preparedness and a team effort result in better airway management and improved outcomes in trauma patients with variable hemodynamic response to resuscitation. All possible efforts must be made to secure a definitive airway (if required) and should be verified clinically as well as with the available adjuncts. The success of a trauma team depends on the familiarity to the airways devices and their discrete application in various situations.

Radio resource allocation in a 6G D-OMA network with imperfect SIC: A framework aided by a bi-objective hyper-heuristic

In the sixth generation (6G) of mobile communication networks, radio resource allocation management (RRAM) systems must offer high levels of customization and sustainability in their operations. For this reason, this article proposes a framework that maximizes the quality of experience (QoE) of users of a 6G network that implements the delta-orthogonal multiple access (D-OMA) scheme as well as the system’s energy efficiency (EE), considering imperfections in successive interference cancellation (SIC) activities. The framework has two phases: in phase (a), a low-complexity heuristic was implemented in order to carry out the selection of base stations (BSs) and distribution of the electromagnetic spectrum slices to each communication device (CD); and in phase (b), a new bi-objective selection hyper-heuristic (SHH) performs the transmission power levels allocation. To evaluate the framework’s performance, we performed two tests: First, we evaluated the generalization capacity of our SHH with the isolated application of its constituent metaheuristics. Second, our hyper-heuristic was compared to other state-of-the-art transmit power allocation methods (TPLAMs) developed for non-orthogonal multiple access (NOMA) environments. For a fair comparison, all TPLAMs used the same heuristic for phase (a). As a result, the framework, in addition to providing high levels of QoE to network users, also presented low levels of failure probability as well as improved the average EE of the system.