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Journal articles on the topic 'Optimization nonlinear resource allocation problems'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Hu, Yifan, Mingang Liu, and Yizhi Feng. "Resource Allocation for SWIPT Systems with Nonlinear Energy Harvesting Model." Wireless Communications and Mobile Computing 2021 (April 6, 2021): 1–9. http://dx.doi.org/10.1155/2021/5576356.

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In this paper, we study the resource allocation for simultaneous wireless information and power transfer (SWIPT) systems with the nonlinear energy harvesting (EH) model. A simple optimal resource allocation scheme based on the time slot switching is proposed to maximize the average achievable rate for the SWIPT systems. The optimal resource allocation is formulated as a nonconvex optimization problem, which is the combination of a series of nonconvex problems due to the binary feature of the time slot-switching ratio. The optimal problem is then solved by using the time-sharing strong duality

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Barkalaya, O. G. "Investigating competition in the problems of optimal resource allocation." Economics and Management 28, no. 4 (2022): 359–68. http://dx.doi.org/10.35854/1998-1627-2022-4-359-368.

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Aim. The presented study aims to address the issues of parameter estimation in the problems of optimal resources allocation for the previously introduced competition indicator; to analyze the influence of dimensionality, resource constraints, and other factors on the competition indicator; to exemplify the relationship between the indicator and the extremum of the objective function, constraints, and dual estimates.Tasks. The authors consider cases when the competition indicator captures a change in the initial data that cannot be estimated on the basis of traditional indicators of analysis an

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Wu, Di, Yu Zhang, and Yong Chen. "Joint Optimization Method of Spectrum Resource for UAV Swarm Information Transmission." Electronics 11, no. 20 (2022): 3372. http://dx.doi.org/10.3390/electronics11203372.

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For the problems brought by malicious interference in the unmanned aerial vehicle (UAV) swarm network, we establish a cluster-based UAV swarm information transmission model. We mainly consider four aspects: cluster head selection, channel allocation, power allocation and UAV position. In order to improve the backhaul information rate of UAV swarm, we propose a joint optimization method of spectrum resource with the goal of maximizing the sum throughput of the cluster head UAV. We decompose the original mixed integer nonlinear programming (MINLP) problem into multiple sub-problems based on the

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Chernova, Liubava, Sergiy Titov, Iryna Zhuravel, and Liudmyla Chernova. "APPLICATION OF THE GENERAL ALGORITHM OF LINEARIZATION IN LINEAR FRACTIONAL OPTIMIZATION PROBLEMS IN PROJECT MANAGEMENT." Bulletin of NTU "KhPI". Series: Strategic management, portfolio, program and project management, no. 2(9) (March 17, 2025): 69–76. https://doi.org/10.20998/2413-3000.2024.9.10.

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Effective planning of resources and optimization of the work schedule allows you to minimize costs and adhere to project deadlines, which ensures the quality of results. Many real projects include complex interdependencies and constraints that can be described by nonlinear models, complicating the process of their optimization. The use of a general linearization algorithm for nonlinear optimization problems offers an innovative approach to simplifying and solving complex planning problems. Linearization makes it possible to transform non-linear models into linear forms that are more convenient

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Song, Xin, Xiuwei Han, Yue Ni, Li Dong, and Lei Qin. "Joint Uplink and Downlink Resource Allocation for D2D Communications System." Future Internet 11, no. 1 (2019): 12. http://dx.doi.org/10.3390/fi11010012.

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In cellular networks, device-to-device communications can increase the spectrum efficiency, but some conventional schemes only consider uplink or downlink resource allocation. In this paper, we propose the joint uplink and downlink resource allocation scheme which maximizes the system capacity and guarantees the signal-to-noise-and-interference ratio of both cellular users and device-to-device pairs. The optimization problem is formulated as a mixed integer nonlinear problem that is usually NP hard. To achieve the reasonable resource allocation, the optimization problem is divided into two sub

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Zhao, Pan, Wenlei Guo, Datong Xu, et al. "Hypergraph-based resource allocation for Device-to-Device underlay H-CRAN network." International Journal of Distributed Sensor Networks 16, no. 8 (2020): 155014772095133. http://dx.doi.org/10.1177/1550147720951337.

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In the hybrid communication scenario of the Heterogeneous Cloud Radio Access Network and Device-to-Device in 5G, spectrum efficiency promotion and the interference controlling caused by spectrum reuse are still challenges. In this article, a novel resource management method, consisting of power and channel allocation, is proposed to solve this problem. An optimization model to maximum the system throughput and spectrum efficiency of the system, which is constrained by Signal to Interference plus Noise Ratio requirements of all users in diverse layers, is established. To solve the non-convex mi

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Bai, Wenle, and Ying Wang. "Jointly Optimize Partial Computation Offloading and Resource Allocation in Cloud-Fog Cooperative Networks." Electronics 12, no. 15 (2023): 3224. http://dx.doi.org/10.3390/electronics12153224.

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Fog computing has become a hot topic in recent years as it provides cloud computing resources to the network edge in a distributed manner that can respond quickly to intensive tasks from different user equipment (UE) applications. However, since fog resources are also limited, considering the number of Internet of Things (IoT) applications and the demand for traffic, designing an effective offload strategy and resource allocation scheme to reduce the offloading cost of UE systems is still an important challenge. To this end, this paper investigates the problem of partial offloading and resourc

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Cui, Yue, Peng Liu, Yalei Zhou, and Wenli Duan. "Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems." Applied Sciences 12, no. 19 (2022): 9740. http://dx.doi.org/10.3390/app12199740.

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With the rapid popularization of intelligent terminals and the explosive growth of wireless communication service demand, future mobile communication technology will face many challenges. Non-orthogonal multiple access (NOMA) technology for 5G can provide many connections and effectively improve the frequency spectrum and energy efficiency compared to traditional orthogonal multiple access technologies. Therefore, in recent years, NOMA technology has become one of the research hotspots of numerous scholars. However, the resource allocation problem in the NOMA system, as a high-dimensional nonl

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Ao, Qingxiang, Cheng Li, Jiaxin Yuan, and Xiaole Yang. "Finite-Time Resource Allocation Algorithm for Networked Fractional Nonlinear Agents." Fractal and Fractional 8, no. 12 (2024): 715. https://doi.org/10.3390/fractalfract8120715.

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This paper investigates finite-time resource allocation problems (RAPs) for uncertain nonlinear fractional-order multi-agent systems (FOMASs), considering global equality and local inequality constraints. Each agent is described by high-order dynamics with multiple-input multiple-output and only knows its local objective function. Due to the characteristics of dynamic systems, the outputs of agents are inconsistent with their inputs, making it challenging to satisfy the inequality constraints when solving RAPs. To address this complex optimization control problem, a novel hierarchical algorith

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Hof, John, Michael Bevers, and James Pickens. "Pragmatic Approaches to Optimization with Random Yield Coefficients." Forest Science 41, no. 3 (1995): 501–12. http://dx.doi.org/10.1093/forestscience/41.3.501.

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Abstract This paper discusses practical methods for handling normally distributed random technical (yield) coefficients in linear programs that optimize natural resource allocation and scheduling. These methods are practical in the sense that they are applicable to large-scale real world models and do not require nonlinear solution methods. The paper begins with a description and demonstration of postoptimization approaches that are applicable to large, linear problems, and then explores methods for reducing overall risk through land allocation diversification. A central theme of the paper is

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Tunçel, Kaya. "Advanced Applications and Methodologies in Mathematical Optimization and Operations Research: Insights into Linear Programming, Nonlinear Programming, and Decision-Making Frameworks." Human Computer Interaction 8, no. 1 (2024): 5. http://dx.doi.org/10.62802/b0ec2q73.

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Mathematical optimization and operations research are pivotal disciplines in solving complex decision-making problems across industries. This research delves into advanced methodologies within these fields, with a focus on linear programming (LP), nonlinear programming (NLP), and their applications in optimizing processes and resource allocation. Linear programming, with its capacity to model and solve large-scale problems, remains a cornerstone for optimization, particularly in logistics, finance, and manufacturing. Nonlinear programming, characterized by its ability to handle complex, real-w

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Lan, Yanwen, Xiaoxiang Wang, Chong Wang, Dongyu Wang, and Qi Li. "Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems." Electronics 8, no. 12 (2019): 1430. http://dx.doi.org/10.3390/electronics8121430.

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The hierarchical edge-cloud enabled paradigm has recently been proposed to provide abundant resources for 5G wireless networks. However, the computation and communication capabilities are heterogeneous which makes the potential advantages difficult to be fully explored. Besides, previous works on mobile edge computing (MEC) focused on server caching and offloading, ignoring the computational and caching gains brought by the proximity of user equipments (UEs). In this paper, we investigate the computation offloading in a three-tier cache-assisted hierarchical edge-cloud system. In this system,

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Zhou, Yang, and Rui Xing Chen. "An Improved Dynamic Programming Method for Solving the Problem of Nonlinear Programming." Applied Mechanics and Materials 353-356 (August 2013): 3359–64. http://dx.doi.org/10.4028/www.scientific.net/amm.353-356.3359.

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This paper, which based on the conventional dynamic programming solution , using the method that the decision variables of various stages are fully discrete in their feasible region to solve the optimal target function value under the various state variables. The method can be generic in solving the maximum and minimum objective function value, while avoiding the problem of the different discrete step lengths of the state variables lead to lower the precision of the target value. So, the method will make the solution process of the various stages more specific image, contributing to combining

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Du, Yongwen, Xiquan Zhang, Wenxian Zhang, and Zhangmin Wang. "Whale Optimization Algorithm with Applications to Power Allocation in Interference Networks." Information Technology and Control 50, no. 2 (2021): 390–405. http://dx.doi.org/10.5755/j01.itc.50.2.28210.

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Power allocation plays a pivotal role in improving the communication performance of interference-limitedwireless network (IWN). However, the optimization of power allocation is usually formulated as a mixed-integernon-linear programming (MINLP) problem, which is hard to solve. Whale optimization algorithm (WOA)has recently gained the attention of the researcher as an efficient method to solve a variety of optimizationproblems. WOA algorithm also has the disadvantages of low convergence accuracy and easy to fall into local optimum.To solve the above problems, we propose Cosine Compound Whale Op

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Voronin, Albert, and Alina Savchenko. "RESOURCE DISTRIBUTION PROBLEM." Journal of Automation and Information sciences 1 (January 1, 2022): 5–10. http://dx.doi.org/10.34229/1028-0979-2022-1-1.

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In various subject areas, the problem of such a distribution of the resources of a controlled system between individual elements (objects) is relevant, which ensures the most efficient functioning of the system in given circumstances. The problem of distribution of the given global resource is considered at restrictions from below, applied on partial resources. It is shown, that the problem consists in construction of adequate criterion function for optimization of process of distribution of resources in conditions of their limitation. The objective function is a scalar convolution of the part

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Pham, Xuan-Qui, Tien-Dung Nguyen, VanDung Nguyen, and Eui-Nam Huh. "Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing." Symmetry 11, no. 1 (2019): 58. http://dx.doi.org/10.3390/sym11010058.

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The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devic

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Chen, Jian, Jiajun Tian, Shuheng Jiang, Yunsheng Zhou, Hai Li, and Jing Xu. "The Allocation of Base Stations with Region Clustering and Single-Objective Nonlinear Optimization." Mathematics 10, no. 13 (2022): 2257. http://dx.doi.org/10.3390/math10132257.

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For the problem of 5G network planning, a certain number of locations should be selected to build new base stations in order to solve the weak coverage problems of the existing network. Considering the construction cost and some other factors, it is impossible to cover all the weak coverage areas so it is necessary to consider the business volume and give priority to build new stations in the weak coverage areas with high business volume. Aimed at these problems, the clustering of weak point data was carried out by using k-means clustering algorithm. With the objective function as the minimiza

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Hu, Wenfa, and Xinhua He. "An Innovative Time-Cost-Quality Tradeoff Modeling of Building Construction Project Based on Resource Allocation." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/673248.

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The time, quality, and cost are three important but contradictive objectives in a building construction project. It is a tough challenge for project managers to optimize them since they are different parameters. This paper presents a time-cost-quality optimization model that enables managers to optimize multiobjectives. The model is from the project breakdown structure method where task resources in a construction project are divided into a series of activities and further into construction labors, materials, equipment, and administration. The resources utilized in a construction activity woul

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Safaa Nouman Alali and Abdulkarim Assalem. "New Methods for Optimal Power Allocation and Joint Resource Scheduling in 5G Network which Use Mobile Edge Computing." Journal of Advanced Research in Applied Sciences and Engineering Technology 47, no. 2 (2024): 237–65. http://dx.doi.org/10.37934/araset.47.2.237265.

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Mobile Edge Computing (MEC) is considered one of the enabling and promising technologies in 5G networks, especially with the massive data movement of various devices and the increased demand for computing. Here, computational offloading of tasks to edge clouds provides an effective, flexible, low-latency solution for mobile users in the network. However, the limited computing resources in edge clouds and the dynamic demands of mobile users make it difficult to schedule computing requests to appropriate edge clouds, and make the offloading process energetically expensive for devices. Therefore,

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Lin, Zefang, Hui Song, and Daru Pan. "A Joint Power and Channel Scheduling Scheme for Underlay D2D Communications in the Cellular Network." Sensors 19, no. 21 (2019): 4799. http://dx.doi.org/10.3390/s19214799.

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Device-to-device (D2D) communication, as one of the promising candidates for the fifth generation mobile network, can afford effective service of new mobile applications and business models. In this paper, we study the resource management strategies for D2D communication underlying the cellular networks. To cater for green communications, our design goal is to the maximize ergodic energy efficiency (EE) of all D2D links taking into account the fact that it may be tricky for the base station (BS) to receive all the real-time channel state information (CSI) while guaranteeing the stability and t

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Feng, Yizhi, and Yan Cao. "Achievable Rate Maximization for Multi-Relay AF Cooperative SWIPT Systems with a Nonlinear EH Model." Sensors 22, no. 8 (2022): 3041. http://dx.doi.org/10.3390/s22083041.

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In this paper, the maximization of the achievable information rate is proposed for the multi-relay amplify-and-forward cooperative simultaneous wireless information and power transfer communication systems, where the nonlinear characteristic of the energy harvesting (EH) circuits is taken into account for the receivers of the relay nodes. The time switching (TS) and power splitting (PS) schemes are considered for the EH receivers and the achievable rate maximization problems are formulated as convex and non-convex optimization problems, respectively. The optimal TS and PS ratios for the relay

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Luo, Zhiyong, Xintong Liu, Shanxin Tan, Haifeng Xu, and Jiahui Liu. "Multi-Objective Multi-Stage Optimize Scheduling Algorithm for Nonlinear Virtual Work-Flow Based on Pareto." Processes 11, no. 4 (2023): 1147. http://dx.doi.org/10.3390/pr11041147.

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Work-flow scheduling is for finding the allocation method to achieve optimal resource utilization. In the scheduling process, constraints, such as time, cost and quality, need to be considered. How to balance these parameters is a NP-hard problem, and the nonlinear manufacturing process increases the difficulty of scheduling, so it is necessary to provide an effective heuristic algorithm. Aiming at these problems, a multi-objective nonlinear virtual work-flow model was set up, and a multi-objective staged scheduling optimization algorithm with the objectives of minimizing cost and time and max

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Zhang, Mi, Zixuan Liu, Rungang Bao, Shuli Zhu, Li Mo, and Yuqi Yang. "Application of Black-Winged Differential-Variant Whale Optimization Algorithm in the Optimization Scheduling of Cascade Hydropower Stations." Sustainability 17, no. 3 (2025): 1018. https://doi.org/10.3390/su17031018.

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Abstract: Hydropower is a vital strategic component of China’s clean energy development. Its construction and optimized water resource allocation are crucial for addressing global energy challenges, promoting socio-economic development, and achieving sustainable development. However, the optimization scheduling of cascade hydropower stations is a large-scale, multi-constrained, and nonlinear problem. Traditional optimization methods suffer from low computational efficiency, while conventional intelligent algorithms still face issues like premature convergence and local optima, which severely h

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Lev, Raskin, and Sira Oksana. "CONSTRUCTION OF THE FRACTIONAL-NONLINEAR OPTIMIZATION METHOD." Eastern-European Journal of Enterprise Technologies 4, no. 4 (100) (2019): 37–43. https://doi.org/10.15587/1729-4061.2019.174079.

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A method for solving the fractional nonlinear optimization problem has been proposed. It is shown that numerous inventory management tasks, on the rational allocation of limited resources, on finding the optimal paths in a graph, on the rational organization of transportation, on control over dynamical systems, as well as other tasks, are reduced exactly to such a problem in cases when the source data of a problem are described in terms of a probability theory or fuzzy math. We have analyzed known methods for solving the fractional nonlinear optimization problems. The most efficient among them

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Shao, Yanling, Zhen Shen, Siliang Gong, and Hanyao Huang. "Cost-Aware Placement Optimization of Edge Servers for IoT Services in Wireless Metropolitan Area Networks." Wireless Communications and Mobile Computing 2022 (July 27, 2022): 1–17. http://dx.doi.org/10.1155/2022/8936576.

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Edge computing migrates cloud computing capacity to the edge of the network to reduce latency caused by congestion and long propagation distance of the core network. And the Internet of things (IoT) service requests with large data traffic submitted by users need to be processed quickly by corresponding edge servers. The closer the edge computing resources are to the user network access point, the better the user experience can be improved. On the other hand, the closer the edge server is to users, the fewer users will access simultaneously, and the utilization efficiency of nodes will be redu

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Wen, Jing, Wen Ying Liu, and Chang Xie. "A Optimal Scheduling Method Based on Source and Load Interactive for Power System with Large-Scale Wind Power Integrated." Advanced Materials Research 953-954 (June 2014): 389–94. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.389.

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The random fluctuation and anti-peaking characteristics of wind power has brought new problems for the power system optimal dispatch. Based on the interaction characteristic of the load, this paper played the utility of interactive load which can help system consumers the positive and negative fluctuations of wind power, and considered interactive load as a scheduling resource into the traditional day-ahead scheduling model. Taking into account the effects of interactive load on system operating costs and power flow distribution, this paper established a generation scheduling model which the a

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Sivakumar, R. D. Assistant Professor Department of Computer Science, and S. Former Assistant Professor Department of Business Administration Brindha. "OPTIMIZATION TECHNIQUES FOR DECISION SUPPORT SYSTEMS." Indian Journal of Research and Development Systems in Technologization 1, no. 3 (2024): 30–40. https://doi.org/10.5281/zenodo.11202691.

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Decision Support Systems (DSS) are applied in these different areas like business, healthcare and logistics, they are critical tools that help users to get the best decision makings possible when using them. Optimization methods are paramount constitute the basis of the DSS for provisioning relevant and useful decision-making while maximizing accuracy. This paper starts by elucidating diverse optimization inner workings of DSSs, followed by data management and visualization, describing mathematical programming, heuristic approaches, and metaheuristic fundamentals. Mathematical programming cons

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Sharma, Shruti, and Wonsik Yoon. "Energy Efficient Power Allocation in Massive MIMO Based on Parameterized Deep DQN." Electronics 12, no. 21 (2023): 4517. http://dx.doi.org/10.3390/electronics12214517.

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Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex an

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Hu, Zike. "Optimization of Bi-directional Long and Short-term Memory Networks Based on Variational Modal Decomposition Combined with Particle Swarm Algorithm for Health and Longevity Prediction." Advances in Economics, Management and Political Sciences 170, no. 1 (2025): 19–26. https://doi.org/10.54254/2754-1169/2025.22494.

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In this study, an innovative prediction model based on the integration of variational modal decomposition (VMD), particle swarm optimization (PSO) algorithm and bidirectional long and short-term memory network (BiLSTM) is proposed to address the mechanism of economic and social factors on the health life expectancy of the population and prediction problems. The adaptive modal decomposition of complex time-series features by VMD algorithm, combined with the global optimization of key parameters of BiLSTM network by PSO algorithm, effectively solves the limitations of the traditional model in no

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Qiuyue Zhang, Guoping Zhang,. "Building Engineering Cost Prediction Based On Deep Learning: Model Construction and Real - Time Optimization." Journal of Electrical Systems 20, no. 5s (2024): 151–64. http://dx.doi.org/10.52783/jes.1887.

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Effective project planning, risk mitigation, and stakeholder satisfaction in the construction business are greatly impacted by accurate cost projection. Overspending, setbacks, and ruined projects are all possible results of imprecise cost estimates. For this reason, it is critical to guarantee the viability and success of a project by increasing the precision of cost predictions. Construction project complexity, a myriad of cost variables, and uncertainty are the obstacles that building engineering cost prediction must overcome. Predictions made using traditional approaches are commonly inacc

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Saeidian, B., M. Saadi Mesgari, and M. Ghodousi. "OPTIMUM ALLOCATION OF WATER TO THE CULTIVATION FARMS USING GENETIC ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 631–38. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-631-2015.

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The water scarcity crises in the world and specifically in Iran, requires the proper management of this valuable resource. According to the official reports, around 90 percent of the water in Iran is used for agriculture. Therefore, the adequate management and usage of water in this section can help significantly to overcome the above crises. The most important aspect of agricultural water management is related to the irrigation planning, which is basically an allocation problem. The proper allocation of water to the farms is not a simple and trivial problem, because of the limited amount of a

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Kodialam, Muralidharan S., and Hanan Luss. "Algorithms for Separable Nonlinear Resource Allocation Problems." Operations Research 46, no. 2 (1998): 272–84. http://dx.doi.org/10.1287/opre.46.2.272.

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Chekanin, Vladislav A., and Alexander V. Chekanin. "Object-Oriented Class Library for Resource Allocation Problems." Applied Mechanics and Materials 799-800 (October 2015): 1149–53. http://dx.doi.org/10.4028/www.scientific.net/amm.799-800.1149.

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The object-oriented class library designed for solving various optimization problems of resource allocation, including problems of cutting materials and any dimensional packing problems, is described in this paper. The class library enables obtaining of suboptimal solutions of NP-completed resource allocation problems using standard evolutionary and modified heuristic optimization algorithms. The developed class library can be used in creation of an applied software for a wide class of optimization problems, including problems of resource allocation in storage systems and logistics, problems o

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Granmo, Ole-Christoffer, and B. John Oommen. "Solving Stochastic Nonlinear Resource Allocation Problems Using a Hierarchy of Twofold Resource Allocation Automata." IEEE Transactions on Computers 59, no. 4 (2010): 545–60. http://dx.doi.org/10.1109/tc.2009.189.

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Yin, Peng-Yeng, and Jing-Yu Wang. "Ant colony optimization for the nonlinear resource allocation problem." Applied Mathematics and Computation 174, no. 2 (2006): 1438–53. http://dx.doi.org/10.1016/j.amc.2005.05.042.

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Luss, Hanan. "Minimax resource allocation problems: Optimization and parametric analysis." European Journal of Operational Research 60, no. 1 (1992): 76–86. http://dx.doi.org/10.1016/0377-2217(92)90335-7.

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Wen, Jing Hua, He Ling Jiang, Mei Zhang, and Xi Yu. "Application of Dynamic Programming in Resources Optimization Allocation of Factory Production Line." Key Engineering Materials 474-476 (April 2011): 1632–37. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1632.

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The dynamic programming has significant implications for solving multi-stage decision of resource allocation problems. By inducting phase, state of variables and decision, the factory assembly line resource allocation problems was taken as a multi-stage decision process. The stage of resource allocation was divided in reason, and the dynamic programming equation was built with “Top-Down” ways to reverse recursion according to the dynamic programming principle and methods. Adopting the MATLAB7.0 as development platform, it was convenient for calculating optimal decision sequence and maximum tot

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Yingjie, Xu. "Application of BP Neural Network to Optimize the Allocation of Art Teaching Resources." Tobacco Regulatory Science 7, no. 5 (2021): 4122–32. http://dx.doi.org/10.18001/trs.7.5.1.188.

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Reasonable allocation of art teaching resources can improve the management efficiency of art teaching resources. There is a large delay in the allocation of art teaching resources, which leads to the long occupation time of network resource allocation channel. The traditional method of network experiment resource allocation is to assign resource tasks for different channels to complete the resource allocation. When the network resource allocation channel occupies a long time, the allocation efficiency is reduced. This paper proposes an optimal allocation method of art teaching resources based

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Wang, Xue-Fang, Yiguang Hong, Xi-Ming Sun, and Kun-Zhi Liu. "Distributed Optimization for Resource Allocation Problems Under Large Delays." IEEE Transactions on Industrial Electronics 66, no. 12 (2019): 9448–57. http://dx.doi.org/10.1109/tie.2019.2891406.

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Zhao, Xiaojuan. "Using Deep Learning to Optimize the Allocation of Rural Education Resources Under the Background of Rural Revitalization." International Journal of Agricultural and Environmental Information Systems 16, no. 1 (2025): 1–18. https://doi.org/10.4018/ijaeis.375426.

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Through a large amount of literature research, the current status of rural education resource allocation, the application of deep learning in education resource optimization, and the challenges and solutions faced are sorted out. A rural education resource optimization model based on deep learning is designed, and data collection and analysis, modeling of education resource allocation optimization problems, and optimization algorithm design based on deep learning are elaborated in detail. After experimental evaluation, the optimization algorithm based on deep learning is compared with the trad

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Мухамедиева, Д. Т., та М. Х. Раупова. "Квадратичное программирование в модели распределения ресурсов в сельском хозяйстве на основе квантового алгоритма". Проблемы вычислительной и прикладной математики, № 2(64) (15 травня 2025): 101–13. https://doi.org/10.71310/pcam.2_64.2025.09.

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This paper discusses the application of quantum algorithms to the problem of op timizing resource allocation in agriculture. In particular, the possibility of using the Quantum Approximate Optimization Algorithm to solve this problem is investigated. This is a quantum optimization algorithm that can be effectively applied to combinato rial optimization problems. The work involves adapting the algorithm to the problem of optimizing resource allocation in agriculture, including modeling the optimization func tion and taking into account resource use constraints. It is assumed that the use of a q

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Yazidi, Anis, and Hugo L. Hammer. "Solving stochastic nonlinear resource allocation problems using continuous learning automata." Applied Intelligence 48, no. 11 (2018): 4392–411. http://dx.doi.org/10.1007/s10489-018-1201-7.

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Miao, Bin. "Preliminary Study about Optimal Allocation of Human Resources Management." Advanced Materials Research 268-270 (July 2011): 1913–16. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1913.

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In today's world, the competition between enterprises in the final analysis is talent competition and giving full play to the role of talents of enterprise cannot leave the optimal allocation of human resources. Along with the increasing open of global economy, our state-owned enterprises face the more and more difficult challenge, but also have many opportunities, which will encourage enterprises to renew ideas, adjust human resource structure, strengthen the cultivation of talents, learn and introduce advanced hr optimization technology and method. In Nanyang mobile company, for example, on

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Yasser A.Al-khafaje. "Deep Learning for Resource Allocation in NOMA: A Comprehensive Review with Consideration of Classical User Grouping Methods." Journal of Information Systems Engineering and Management 10, no. 34s (2025): 613–25. https://doi.org/10.52783/jisem.v10i34s.5857.

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In next-generation wireless communication, non-orthogonal multiple access (NOMA) emerges as a disruptive technology that allows several users to connect concurrently on a shared time-frequency resource using successive interference cancellation (SIC). Power allocation and user clustering are the main areas of attention for this review, which looks into optimizing NOMA systems that mostly rely on resource allocation, which is vital to improving their performance. Identifying the optimal resource distribution involves a large computational expenditure because of non-convex optimization problems.

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Zhang, Xun, Kehao Wang, Xiaobai Li, Kezhong Liu, and Yirui Cong. "Joint Task Allocation and Resource Optimization Based on an Integrated Radar and Communication Multi-UAV System." Drones 7, no. 8 (2023): 523. http://dx.doi.org/10.3390/drones7080523.

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This paper investigates the joint task allocation and resource optimization problem in an integrated radar and communication multi-UAV (IRCU) system. Specifically, we assign reconnaissance UAVs and communication UAVs to perform the detection, tracking and communication tasks under the resource, priority and timing constraints by optimizing task allocation, power as well as channel bandwidth. Due to complex coupling among task allocation and resource optimization, the considered problem is proved to be non-convex. To solve the considered problem, we present a loop iterative optimization (LIO) a

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Zhang, Yaping, and Qi Zhu. "Resource Allocation Algorithm for UAV Aided Symbiotic Radio Communication System." Journal of Internet Technology 26, no. 3 (2025): 315–25. https://doi.org/10.70003/160792642025052603004.

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Aiming at the issue of how to improve the system transmission rate in a multiple Internet of things (IoT) device application scenario, we propose the resource allocation algorithm of the symbiotic radio communication system under multiple backscatter devices (BDs) assisted by unmanned aerial vehicle (UAV). We formulate the optimization problem of maximizing BDs’ sum rate by jointly optimizing the time allocation, BDs’ reflection coefficient and UAV location under constraints of BD’s harvested energy, quality of service (QoS) of cellular user and UAV. Since the problem is non-convex, it is diff

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Okamura, Hiroyuki, and Tadashi Dohi. "Optimizing Testing-Resource Allocation Using Architecture-Based Software Reliability Model." Journal of Optimization 2018 (September 27, 2018): 1–7. http://dx.doi.org/10.1155/2018/6948656.

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In the management of software testing, testing-recourse allocation is one of the most important problems due to the tradeoff between development cost and reliability of released software. This paper presents the model-based approach to design the testing-resource allocation. In particular, we employ the architecture-based software reliability model with operational profile to estimate the quantitative software reliability in operation phase and formulate the multiobjective optimization problems with respect to cost, testing effort, and software reliability. In numerical experiment, we investig

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Shamshirband, Shahab, Javad Hassannataj Joloudari, Sahar Khanjani Shirkharkolaie, et al. "Game theory and evolutionary optimization approaches applied to resource allocation problems in computing environments: A survey." Mathematical Biosciences and Engineering 18, no. 6 (2021): 9190–232. http://dx.doi.org/10.3934/mbe.2021453.

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<abstract> <p>Today's intelligent computing environments, including the Internet of Things (IoT), Cloud Computing (CC), Fog Computing (FC), and Edge Computing (EC), allow many organizations worldwide to optimize their resource allocation regarding the quality of service and energy consumption. Due to the acute conditions of utilizing resources by users and the real-time nature of the data, a comprehensive and integrated computing environment has not yet provided a robust and reliable capability for proper resource allocation. Although traditional resource allocation approaches in a

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Yin, Peng-Yeng, and Jing-Yu Wang. "A particle swarm optimization approach to the nonlinear resource allocation problem." Applied Mathematics and Computation 183, no. 1 (2006): 232–42. http://dx.doi.org/10.1016/j.amc.2006.05.051.

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S. Nithya, Et al. "A Tutorial on Cross-layer Optimization Wireless Network System Using TOPSIS Method." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1809–17. http://dx.doi.org/10.17762/ijritcc.v11i10.8757.

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Each other, leading to issues such as interference, limited bandwidth, and varying channel conditions. These challenges require specialized optimization techniques tailored to the wireless environment. In wireless communication networks is to maximize the overall system throughput while ensuring fairness among users and maintaining quality of service requirements. This objective can be achieved through resource allocation optimization, where the available network resources such as bandwidth, power, and time slots are allocated to users in an optimal manner. Optimization-based approaches in wir

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