Resource Allocation Scheme Research Articles

Optimal allocation of enterprise marketing resources based on hybrid parallel genetic algorithm and simulated annealing algorithm

Abstract Aiming at minimizing the use of marketing resources, this article establishes the mathematical model of marketing resources allocation, designs the algorithm of marketing resources allocation, and compares the examples. An improved heuristic algorithm considering tilt angle matching is proposed and used as a local search algorithm for enterprise marketing resources. We design an innovative optimization strategy that incorporates the concept of tilt angle matching to enhance the local search efficiency of enterprise marketing resource allocation. In addition, we have introduced a novel parallel grouping genetic algorithm (PGGA), which utilizes grouping coding and exon crossover to further enhance the search and optimization efficiency of the solution. PGGA is improved by using adaptive parameters to form IPGGA, which improves the efficiency and convergence speed of enterprise marketing resource allocation. The annealing function of the simulated annealing algorithm is improved, and a model is constructed to solve the problem of enterprise marketing resource allocation. Simulated annealing algorithm is introduced to solve the problem of marketing resource allocation, and the framework of simulated annealing algorithm is analyzed. To solve the problem of fast decay rate of simulated annealing algorithm, Doppler effect function is used to optimize the algorithm. This article mainly uses qualitative and quantitative analysis methods to conduct in-depth research on enterprise marketing resource allocation. It focuses on the planning and allocation of enterprise marketing resources. Through IPGGA-ISAA research and analysis of all kinds of data of enterprise marketing resources, the present situation, efficiency, and main problems of enterprise marketing resources allocation are discussed and analyzed more deeply. Compared with other algorithms, IPGGA-ISAA can better analyze the causes of problems and provide better marketing resource allocation schemes for enterprises.

A Federated Learning-Based Resource Allocation Scheme for Relaying-Assisted Communications in Multicellular Next Generation Network Topologies

Growing and diverse user needs, along with the need for continuous access with minimal delay in densely populated machine-type networks, have led to a significant overhaul of modern mobile communication systems. Within this realm, the integration of advanced physical layer techniques such as relaying-assisted transmission in beyond fifth-generation (B5G) networks aims to not only enhance network performance but also extend coverage across multicellular orientations. However, in cellular environments, the increased interference levels and the complex channel representations introduce a notable rise in the computational complexity associated with radio resource management (RRM) tasks. Machine and deep learning (ML/DL) have been proposed as an efficient way to support the enhanced user demands in densely populated environments since ML/DL models can relax the traffic load that is associated with RRM tasks. There is, however, in these solutions the need for distributed execution of training tasks to accelerate the decision-making process in RRM tasks. For this purpose, federated learning (FL) schemes are considered a promising field of research for next-generation (NG) networks’ RRM. This paper proposes an FL approach to tackle the joint relay node (RN) selection and resource allocation problem subject to power management constraints when in B5G networks. The optimization objective of this approach is to jointly elevate energy (EE) and spectral efficiency (SE) levels. The performance of the proposed approach is evaluated for various relaying-assisted transmission topologies and through comparison with other state-of-the-art ones (both ML and non-ML). In particular, the total system energy efficiency (EE) and spectral efficiency (SE) can be improved by up to approximately 10–20% compared to a state-of-the-art centralized ML scheme. Moreover, achieved accuracy can be improved by up to 10% compared to state-of-the-art non-ML solutions, while training time is reduced by approximately 50%.

Influence of Stakeholder Engagement on Strategy Implementation in Selected Nigerian Government Agencies

Effective strategy implementation has remained a huge challenge in Nigerian Government Agencies. This study investigates stakeholder engagement practices across Nigerian government agencies to evaluate their impact on strategy implementation effectiveness. The research focuses on four key dimensions: Effective Communication, Stakeholder Inclusiveness and Diversity, Mechanisms of Stakeholder Engagement, and Stakeholder Satisfaction, specifically assessing their influence on resource allocation. Using a descriptive survey design, three pivotal government agencies INEC, the Central Bank of Nigeria, and the Nigerian Electricity Regulatory Commission were purposively selected. Data was collected from 384 respondents, and multiple regression was used in the analysis. Findings reveal that transparent internal communication significantly influences increased resource allocation (B = 0.558, p < 0.005). Moreover, agencies incorporating diverse stakeholder input observe larger allocations to strategic priorities (B = 0.083, p = 0.005). Also, Mechanisms of stakeholder engagement (B = 0.101, p < 0.005) exhibit significant positive relationship. Equally, higher satisfaction among stakeholders correlates with greater deployment of resources (B = 0.205, p < 0.005). The study recommends that Nigerian government agencies enhance internal communication, promote inclusive stakeholder engagement, and refine resource allocation strategies. Emphasizing diverse perspectives and stakeholder satisfaction is crucial, while investments in organizational and management capacities alongside technical planning can solidify agencies' effectiveness in securing resources for proactive strategic changes.

Graph Attention Network Enhanced Power Allocation for Wireless Cellular System

The importance of an efficient network resource allocation strategy has grown significantly with the rapid advancement of cellular network technology and the widespread use of mobile devices. Efficient resource allocation is crucial for enhancing user services and optimizing network performance. The primary objective is to optimize the power distribution method to maximize the total aggregate rate for all customers within the network. In recent years, graph-based deep learning approaches have shown great promise in addressing the challenge of network resource allocation. Graph neural networks (GNNs) have particularly excelled in handling graph-structured data, benefiting from the inherent topological characteristics of mobile networks. However, many of these methodologies tend to focus predominantly on node characteristics during the learning phase, occasionally overlooking or oversimplifying the importance of edge attributes, which are equally vital as nodes in network modeling. To tackle this limitation, we introduce a novel framework known as the Heterogeneous Edge Feature Enhanced Graph Attention Network (HEGAT). This framework establishes a direct connection between the evolving network topology and the optimal power distribution strategy throughout the learning process. Our proposed HEGAT approach exhibits improved performance and demonstrates significant generalization capabilities, as evidenced by extensive simulation results.

Development of Enhanced Chimp Optimization Algorithm (OFCOA) in Cognitive Radio Networks for Energy Management and Resource Allocation

Transmit time and power optimisation increase secondary network energy efficiency (EE). The optimum resource allocation strategy in cognitive radio networks is the enhanced chimp optimisation algorithm (OFCOA) since the EE maximising problem is a nonlinear fractional programming problem. To control resources and energy, this research offers an energy-efficient CRN opposition function-based chimpanzee optimisation algorithm (OFCOA) solution. Combining the opposition function (OF) with the chimpanzee optimisation technique is recommended. OF in COAs improves decision-making. Spectrum measurements in energy management provide energy-efficient CRN operation. The suggested technique was evaluated using channel occupancy, CRN data, and four major and secondary user scenarios. CPU power, network life, transmission rate, latency, flush, power consumption, and overhead are utilized to evaluate the proposed approach in MATLAB. The proposed method is compared to existing approaches like Particle Swarm Optimisation (PSO), Chimpanzee Optimisation Algorithm (COA), and Whale Optimisation Algorithm.

Jointly Active/Passive Beamforming Optimization for Intelligent-Reflecting Surface-Assisted Cognitive-IoT Networks

To overcome challenges such as limited energy availability for terminal devices, constrained network coverage, and suboptimal spectrum resource utilization, with the overarching objective of establishing a sustainable and efficient interconnection infrastructure, we introduce an innovative Intelligent Reflective Surface (IRS) technology. This cutting-edge IRS technology is employed to architect a wireless and energy-efficient cognitive secure communication network assisted by IRS. To further optimize the overall energy harvesting of this network, we present a cognitive secure resource allocation scheme, aiming to maximize the system’s total collected energy. This scheme carefully considers various constraints, including transmission power constraints for cognitive base stations, power constraints for jammer devices, interference limitations for all primary users, minimum security rate constraints for all cognitive Internet of Things (IoT) devices, and phase shift constraints for IRS. We establish a comprehensive hybrid cognitive secure resource allocation model, encompassing joint cognitive transmission beam design, jammer device transmission beam design, and phase shift design. Given the non-convex nature of the formulated problem and the intricate coupling relationships among variables, we devise an effective block coordinate descent (BCD) iterative algorithm. The realization of joint cognitive/jammer base station transmission beam design and phase shift design employs sophisticated techniques such as continuous convex approximation methods and semi-definite programming. Simulation results underscore the superior performance of the proposed scheme compared to existing resource allocation approaches, particularly in terms of total harvested energy and other critical metrics.

Optimizing V2X Communication: Spectrum Resource Allocation and Power Control Strategies for Next-Generation Wireless Technologies

The upcoming wireless technology developments in the next generations are expected to substantially transform the vehicle-to-everything (V2X) communication network. The challenge of limited spectrum resources in V2X communication, caused by the need for high data rates, necessitates a thorough analysis of spectrum resource allocation and power control. This complex problem falls under the domain of mixed-integer nonlinear programming; a strategic approach is implemented to overcome these issues, which divides the main challenge into two sub-problems. The issue of resource allocation is addressed by implementing a multiaccess spectrum allocation method, which is deliberately designed to optimize the utilization of the spectrum resources that are currently accessible. Concurrently, the power control issue is resolved by employing a continuous convex approximation technique, which effectively converts non-convex power-allocation issues into convex equivalents. This approach helps to alleviate interference between users. Finally, the simulation results prove that the proposed approaches can improve vehicle performance. The algorithms proposed in this article significantly improve the system throughput and access rate of vehicular user equipment (VUEs) while ensuring the data rate of cellular user equipment (CUEs).

Plasticity of growth laws tunes resource allocation strategies in bacteria.

Bacteria like E. coli grow at vastly different rates on different substrates, however, the precise reason for this variability is poorly understood. Different growth rates have been attributed to 'nutrient quality', a key parameter in bacterial growth laws. However, it remains unclear to what extent nutrient quality is rooted in fundamental biochemical constraints like the energy content of nutrients, the protein cost required for their uptake and catabolism, or the capacity of the plasma membrane for nutrient transporters. Here, we show that while nutrient quality is indeed reflected in protein investment in substrate-specific transporters and enzymes, this is not a fundamental limitation on growth rate, at least for certain 'poor' substrates. We show that it is possible to turn mannose, one of the 'poorest' substrates of E. coli, into one of the 'best' substrates by reengineering chromosomal promoters of the mannose transporter and metabolic enzymes required for mannose degradation. This result falls in line with previous observations of more subtle growth rate improvement for many other carbon sources. However, we show that this faster growth rate comes at the cost of diverse cellular capabilities, reflected in longer lag phases, worse starvation survival and lower motility. We show that addition of cAMP to the medium can rescue these phenotypes but imposes a corresponding growth cost. Based on these data, we propose that nutrient quality is largely a self-determined, plastic property that can be modulated by the fraction of proteomic resources devoted to a specific substrate in the much larger proteome sector of catabolically activated genes. Rather than a fundamental biochemical limitation, nutrient quality reflects resource allocation decisions that are shaped by evolution in specific ecological niches and can be quickly adapted if necessary.

Exploring human resource management in the top five global hospitals: a comparative study.

The pivotal role of Human Resource Management (HRM) in hospital administration has been acknowledged in research, yet the examination of HRM practices in the world's premier hospitals has been scant. This study explored how the world's leading hospitals attain operational efficiency by optimizing human resource allocation and melding development strategies into their HRM frameworks. A comparative analysis of the HRM frameworks in the top five global hospitals was undertaken to offer a reference model for other hospitals. This research offers a comparative exploration of the HRM frameworks utilized by the top five hospitals globally, underscoring both shared and distinct elements. Using a multi-case study methodology, the research scrutinized each hospital's HRM framework across six modules, drawing literature from publicly accessible sources, including websites, annual reports, and pertinent English-language scholarly literature from platforms such as Google Scholar, PubMed, Medline, and Web of Science. The analyzed hospitals exhibited inconsistent HRM frameworks, yet all manifested potent organizational cultural attributes and maintained robust employee training and welfare policies. The design of the HR systems was strategically aligned with the hospitals' objectives, and the study established that maintaining a sustainable talent system is pivotal to achieving hospital excellence. The HRM frameworks of the five analyzed hospitals align with their developmental strategies and exhibit unique organizational cultural attributes. All five hospitals heavily prioritize aligning employee development with overall hospital growth and place a spotlight on fostering a healthy working environment and nurturing employees' sense of achievement. While compensation is a notable performance influencer, it is not rigorously tied to workload in these hospitals, with employees receiving mid-to-upper industry-range compensation. Performance assessment criteria focus on job quality and aligning employee actions with organizational values. Comprehensive welfare and protection are afforded to employees across all five hospitals.

Dataset of United States Incident Management Situation Reports from 2007 to 2021

This paper presents a unique 15-year dataset of Incident Management Situation Reports (IMSR), which document daily wildland fire situations across ten geographical regions in the United States. The IMSR dataset includes summaries for each reported day on national and regional wildfire activities, wildfire-specific activities, and committed fire suppression resources (i.e., personnel and equipment). This dataset is distinct from other wildfire data sources as it provides daily information on national fire suppression resource utilization, national and regional preparedness levels, and management priority for each region and fire. We developed an open-source Java program, IMSR-Tool, to process 3,124 IMSR reports available from 2007 to 2021 to generate this structured IMSR dataset, which can be updated when future reports become available. The dataset presented here and its future extension enable researchers and practitioners to study historical wildfire activity and resource use across regions and time, examine fire management perceptions, evaluate strategies for fire prioritization and fire resource allocation, and exploit other broader usage to improve wildfire management and response in the United States.

Proximal Policy Optimization for Efficient D2D-Assisted Computation Offloading and Resource Allocation in Multi-Access Edge Computing

In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliability and real-time responsiveness in end devices. However, the inherent complexity and variability of MEC networks pose significant challenges in computational offloading decisions. To tackle this problem, we propose a proximal policy optimization (PPO)-based Device-to-Device (D2D)-assisted computation offloading and resource allocation scheme. We construct a realistic MEC network environment and develop a Markov decision process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D communication-based offloading framework allows for collaborative task offloading between end devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP model is solved using the PPO algorithm in deep reinforcement learning to derive an optimal policy for offloading and resource allocation. Extensive comparative analysis with three benchmarked approaches has confirmed our scheme’s superior performance in latency, energy consumption, and algorithmic convergence, demonstrating its potential to improve MEC network operations in the context of emerging 5G and beyond technologies.

Research on optimization strategy of container orchestration technology for cloud computing environment

Abstract Containers are becoming the new standard of technology in the cloud era, and a huge number of services are shifting from the traditional virtual machine-based monolithic architecture to the container-based cloud-native architecture. This paper explores the optimization of container orchestration technology in the cloud computing environment, mainly focusing on the optimization study of Kubernetes, a cloud resource orchestration tool for Docker containers. The problems of this container cloud platform were analyzed, and it was found that the orchestration strategy adopted by Kubernetes is a static orchestration strategy, which can easily cause redundancy of container resources. This paper suggests an optimization strategy that first analyzes the PSUB container resource allocation strategy and then presents a new container orchestration optimization strategy based on the PSUB container resource allocation strategy. This paper proposes a PBFDR strategy that utilizes CPU, memory, network bandwidth, and disk IO in a more reasonable and balanced manner by comparing and analyzing the optimization strategies of different container orchestration technologies. Introducing the PBFDR strategy into the Kubernetes container orchestration framework and comparing and analyzing it with the mainstream container orchestration frameworks Rancher and Docker Swarm, the balanced point of resource utilization of KLLPL is around 23%, and the load balancing degree in different deployment sizes is around 13.7%. The evidence indicates that container orchestration technology that is based on PBFDR policy can effectively enhance the utilization of cluster resources.

Innovation of Innovation and Entrepreneurship Teaching Content and Methods under the Internet+ Education Mode

Abstract This paper proposes a way to innovate the teaching content and methods centered on building an online teaching system and information management system for innovation and entrepreneurship. In the J2EE-based innovation and entrepreneurship online teaching system, the genetic algorithm is used to improve the Pick-KX algorithm, and the resource allocation scheme is optimized and processed by the genetic algorithm, which effectively improves the efficiency of the Pick-KX algorithm on heterogeneous servers. In the innovation and entrepreneurship information management system, the TF-IDF algorithm and Word2vec are used to convert the text into document vectors, and K-Means clustering is used to realize document clustering, while the HyperLogLog algorithm is used to carry out the system website data statistics. The 2022 computer science and technology students of a teacher training school in Chongqing, China, were used as research subjects to carry out innovative and entrepreneurial teaching practices. The student’s performance on all dimensions of innovation and entrepreneurship practice ability was above 3.5, which was a good performance. In the evaluation of the online teaching system and information management system for innovation and entrepreneurship, the evaluation scores for each dimension are higher than 3.5, which meets the needs of students. After the practice, the average scores of satisfaction with the innovation and entrepreneurship practice course and their development were 9.2 and 9.02, which were improved by 0.42 and 0.68 compared to the pre-practice.

A Dynamic Pricing-based Offloading and Resource Allocation Scheme With Data Security for Vehicle Platoon

A Dynamic Pricing-based Offloading and Resource Allocation Scheme With Data Security for Vehicle Platoon

Practical experience from USA, Europe and Qatar in building a learning environment to support start-ups and its applicability to Vietnam

Entrepreneurship education serves as a pivotal driver of economic advancement globally, acknowledged for its multifaceted contributions to fostering innovation, generating employment opportunities, and catalyzing overall economic growth trajectories. However, the landscape of entrepreneurship education exhibits notable diversity, characterized by varying approaches, policies, and support mechanisms across different countries and even within specific regions. Despite its recognized importance, scholarly inquiry into the nuanced implementation of entrepreneurship education in Vietnam remains relatively limited. Consequently, this research endeavors to address this gap by embarking on a comprehensive exploration and comparative analysis of entrepreneurship education policies, support systems, and resource allocation strategies across diverse national contexts. By drawing upon illuminating case studies from a spectrum of countries, each distinguished by its unique socio-cultural milieu, this study aims to distill best practices and glean valuable insights that can inform and enrich the development of entrepreneurship education initiatives in Vietnam. Through a meticulous and nuanced examination, the research endeavors to furnish policymakers, educators, and stakeholders in Vietnam with actionable recommendations aimed at bolstering the efficacy and impact of entrepreneurship education endeavors in the nation. In doing so, it seeks to cultivate a more conducive environment for entrepreneurial innovation and economic prosperity.

Research on Optimized Allocation and Use Effect of Music Education Resources Based on MOOC Platform

Abstract The equitable distribution of educational resources epitomizes the essence of establishing a robust educational nation. Yet, the issue of unequal allocation of music education resources has largely been overlooked. This study addresses this gap by examining the current status of music resource distribution across colleges and universities in Province C. An objective function was formulated based on four key indicators reflective of music education resources. The Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multi-objective optimization algorithm, was employed to optimize this function and develop a novel resource allocation strategy. Upon implementation of this strategy, the optimized resource quantities at the sampled institutions were observed as follows: 1,131 square meters of space, 809 books, 72 musical instruments, and 7 personnel. Subsequent evaluation of the changes in students’ musical performance, pre-and post-implementation, revealed significant improvements. Specifically, the average music performance score in the experimental group increased by 7.32 points, compared to a 6.63-point increase in the control group, with both enhancements statistically significant as per the t-test results. Further, a survey assessing the perceptions of teachers and students towards the newly implemented program indicated that 66.8% of respondents endorsed the revised allocation. Collectively, these findings suggest that the optimized allocation strategy not only facilitated the music education resource distribution but also garnered a positive reception and improved student outcomes in music, thereby contributing constructively to the overarching goal of strengthening the educational infrastructure.

Decision-making Model Construction of Enterprise Marketing Outlet Resource Allocation by Integrating Fuzzy Logic

Abstract In the tide of modern business competition, how to efficiently utilize limited resources to obtain the best economic benefits is the core issue that determines the survival and development of enterprises. This paper integrates fuzzy logic theory with resource allocation and proposes the direction of channel integration of marketing resources. The concept of affiliation degree is introduced in fuzzy logic to describe the relationship between things and form the fuzzy relationship in the fuzzy set, which indicates the degree of association between elements in the fuzzy set. The linguistic rule representation method is used to implement the control strategy for decision-making in enterprise marketing outlets. Improve the construction of the optimization model for enterprise outlets’ marketing resources by establishing a two-layer planning model. The research enterprise’s outlet marketing resources allocation situation is examined by combining actual and simulation, based on the principle of outlet marketing resources allocation. From the point of view of outlet affiliation, the central region with a “suitable” resource allocation level includes Hunan Province, with an affiliation degree of 0.4655, and the affiliation degrees of some western regions and northeastern regions range from 0.3896 to 0.5663. The fuzzy logic model generates six resource allocation strategies, and they are tested one by one using simulation experiments to determine the best allocation strategy.

The Time-Sensitive Networking Scheduling Algorithm Based on Q-learning

Abstract Time-Sensitive Networking (TSN) occupies a vital position in modern communication domains, with the 802.1Qbv standard being an important network technology designed to meet real-time requirements. This standard requires network traffic to be transmitted within strict time windows, presenting challenges in network planning, necessitating efficient resource allocation and scheduling strategies. This paper addresses the 802.1Qbv planning problem through the introduction of reinforcement learning algorithms, offering an automated and intelligent solution. We have designed a reinforcement learning agent capable of perceiving network status, learning optimal resource allocation strategies, and dynamically adjusting in real-time environments. Through simulation and experimentation, we have validated the effectiveness of our proposed method, comparing it with traditional planning approaches. The contribution of this study lies in introducing a novel solution to the 802.1Qbv planning problem for time-sensitive networks, enhancing network resource utilization and performance. This approach offers strong support for the development and enhancement of TSN-like networks, holding significant importance for meeting the growing demands of real-time applications.

Linear programming analysis of the optimal allocation problem of human resources in engineering projects

Abstract Human resources are the most dynamic and creative factors in the production factors of engineering projects, and human resource allocation is the key link to whether human resources can give full play to their roles. This paper discusses the realization strategy of optimal human resource allocation for engineering projects and designs a human resource optimization allocation scheme for multiple engineering projects based on the key human resource element of the project manager. Firstly, it constructs the engineering project manager competency element index system and engineering project priority evaluation index system and applies a multi-level fuzzy comprehensive evaluation method to evaluate project manager competency and engineering project priority ranking, respectively. Finally, taking an engineering and construction company as an example, using a linear programming model, an empirical study of human resource allocation for engineering projects is conducted. In the human resource allocation scheme of the optimization model constructed in this paper, all 30 project managers are assigned to 10 projects to meet the staffing needs of all projects within the planned construction period, and the total labor cost is 5.897 million yuan, which is smaller than the 6.192 million yuan of the traditional allocation scheme. It shows that the multi-project staffing model proposed in this paper can ensure the project staffing needs while completing the staff allocation with the lowest total labor cost, which is more effective compared with the traditional staffing methods.

Fluoridated food on preventing dental caries in Latin American and Caribbean children and adolescents: a systematic review

Dental caries is a significant global public health challenge, particularly among children and adolescents. It affects individuals' quality of life and can lead to various adverse outcomes, including pain, infection, and impaired school performance. Fluoride has been widely recognized as a key component in oral health improvement programs due to its ability to prevent tooth demineralization and promote remineralization. This systematic review aimed to evaluate the existing literature on the effectiveness of fluoridated food interventions in preventing dental caries in Latin American and Caribbean children and adolescents. The study focuses on this specific region, which faces unique challenges such as limited access to dental care services, socioeconomic inequalities, and cultural practices that may influence oral hygiene and dietary habits. The review follows the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. A comprehensive search was conducted in multiple databases, including EMBASE, MEDLINE, Scielo, and Web of Science. The search strategy utilized relevant MeSH terms and Boolean phrases related to fluoridated food interventions and dental caries in children aged 12 years and younger. After screening the articles based on predetermined inclusion criteria, no study met the eligibility criteria for inclusion in the systematic review. Overall, the systematic review highlights the absence of high-quality available evidence specifically focused on the effectiveness of fluoridated food interventions on children and adolescents of the Latin American and the Caribbean, underscoring the importance of conducting further studies to inform evidence-based decisions regarding public health policies, resource allocation, and oral health promotion strategies in this population.