Relevant bibliographies by topics / Optimization nonlinear resource allocation problems

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Optimization nonlinear resource allocation problems'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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

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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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Dissertations / Theses on the topic "Optimization nonlinear resource allocation problems"

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Wang, Chen. "Variants of Deterministic and Stochastic Nonlinear Optimization Problems." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112294/document.

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Les problèmes d’optimisation combinatoire sont généralement réputés NP-difficiles, donc il n’y a pas d’algorithmes efficaces pour les résoudre. Afin de trouver des solutions optimales locales ou réalisables, on utilise souvent des heuristiques ou des algorithmes approchés. Les dernières décennies ont vu naitre des méthodes approchées connues sous le nom de métaheuristiques, et qui permettent de trouver une solution approchées. Cette thèse propose de résoudre des problèmes d’optimisation déterministe et stochastique à l’aide de métaheuristiques. Nous avons particulièrement étudié la méthode de
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Hosein, Patrick Ahamad. "A class of dynamic nonlinear resource allocation problems." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14258.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1990.<br>Includes bibliographical references (leaves 213-214).<br>by Patrick Ahamad Hosein.<br>Ph.D.
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Каткова, Тетяна Ігорівна. "Моделі і методи оцінки, прогнозування та управління стратегічною діяльністю підприємства в умовах невизначеності". Thesis, НТУ "ХПІ", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/35129.

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Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.13.03 – системи та процеси керування – Національний технічний університет "Харківський політехнічний інститут", Харків 2018. Дисертаційну роботу присвячено вирішенню важливої та актуальної проблеми наукового обґрунтування і розробки комплексу моделей і методів оцінки, прогнозування та управління стратегічною діяльністю підприємства в умовах невизначеності. Розроблено моделі та методи оцінки і прогнозування стану об'єктів в умовах невизначеності з великим числом можливих станів і великим числом нечітко заданих
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Каткова, Тетяна Ігорівна. "Моделі і методи оцінки, прогнозування та управління стратегічною діяльністю підприємства в умовах невизначеності". Thesis, НТУ "ХПІ", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/35128.

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Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.13.03 – системи та процеси керування – Національний технічний університет "Харківський політехнічний інститут", Харків 2018. Дисертаційну роботу присвячено вирішенню важливої та актуальної проблеми наукового обґрунтування і розробки комплексу моделей і методів оцінки, прогнозування та управління стратегічною діяльністю підприємства в умовах невизначеності. Розроблено моделі та методи оцінки і прогнозування стану об'єктів в умовах невизначеності з великим числом можливих станів і великим числом нечітко заданих
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Marla, Lavanya. "Robust optimization for network-based resource allocation problems under uncertainty." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39280.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007.<br>Includes bibliographical references (p. 129-131).<br>We consider large-scale, network-based, resource allocation problems under uncertainty, with specific focus on the class of problems referred to as multi-commodity flow problems with time-windows. These problems are at the core of many network-based resource allocation problems. Inherent data uncertainty in the problem guarantee
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Louchart, Arthur. "Nonlinear impairments aware resource allocation for cognitive satellite systems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT031.

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Cette thèse traite le problème de l’allocation de ressources pour les communications cognitives par satellite. Dans un contexte de demandes grandissantes en terme de débit, les systèmes de communication par satellite sont amenés à utiliser des fréquences déjà employées par des systèmes terrestres. Le paradigme de la radio cognitive de type sous couche permet à un réseau secondaire d'utiliser la même bande de fréquence qu'un réseau primaire. Cependant, l'interférence créée par le réseau secondaire ne doit pas excéder une certaine limite, fixée par le réseau primaire. Nous considérons un système
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Osman, Ibrahim Hassan. "Metastrategy : simulated annealing and tabu search for combinatorial optimization problems." Thesis, Imperial College London, 1991. http://hdl.handle.net/10044/1/7596.

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Thomopulos, Dimitri <1987&gt. "Models and Solutions of Resource Allocation Problems based on Integer Linear and Nonlinear Programming." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amsdottorato.unibo.it/7399/1/thomopulos_dimitri_tesi.pdf.

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In this thesis we deal with two problems of resource allocation solved through a Mixed-Integer Linear Programming approach and a Mixed-Integer Nonlinear Chance Constraint Programming approach. In the first part we propose a framework to model general guillotine restrictions in two dimensional cutting problems formulated as Mixed-Integer Linear Programs (MILP). The modeling framework requires a pseudo-polynomial number of variables and constraints, which can be effectively enumerated for medium-size instances. Our modeling of general guillotine cuts is the first one that, once it is implemente
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Thomopulos, Dimitri <1987&gt. "Models and Solutions of Resource Allocation Problems based on Integer Linear and Nonlinear Programming." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amsdottorato.unibo.it/7399/.

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In this thesis we deal with two problems of resource allocation solved through a Mixed-Integer Linear Programming approach and a Mixed-Integer Nonlinear Chance Constraint Programming approach. In the first part we propose a framework to model general guillotine restrictions in two dimensional cutting problems formulated as Mixed-Integer Linear Programs (MILP). The modeling framework requires a pseudo-polynomial number of variables and constraints, which can be effectively enumerated for medium-size instances. Our modeling of general guillotine cuts is the first one that, once it is implemente
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Gao, Cunhao. "Some Modeling and Optimization Problems in Cognitive Radio Ad Hoc Networks." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/35020.

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Since its inception, cognitive radio (CR) has quickly been accepted as the enabling radio technology for next-generation wireless communications. A CR promises unprecedented flexibility in radio functionalities via programmability at the lowest layer, which was once done in hardware. Due to its spectrum sensing, learning, and adaptation capabilities, CR is able to address the heart of the problem associated with spectrum scarcity (via dynamic spectrum access (DSA)) and interoperability (via channel switching). It is envisioned that CR will be employed as a general radio platform upon which num
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Books on the topic "Optimization nonlinear resource allocation problems"

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Basu, Sanjay. Modeling Public Health and Healthcare Systems. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190667924.001.0001.

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This book aims to empower readers to learn and apply engineering, operations research, and modeling techniques to improve public health programs and healthcare systems. Readers will engage in in-depth study of disease detection and control strategies from a “systems science” perspective, which involves the use of common engineering, operations research, and mathematical modeling techniques such as optimization, queuing theory, Markov and Kermack-McKendrick models, and microsimulation. Chapters focus on applying these techniques to classical public health dilemmas such as how to optimize screen
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Book chapters on the topic "Optimization nonlinear resource allocation problems"

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Katoh, Naoki, and Toshihide Ibaraki. "Resource Allocation Problems." In Handbook of Combinatorial Optimization. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4613-0303-9_14.

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Katoh, Naoki, Akiyoshi Shioura, and Toshihide Ibaraki. "Resource Allocation Problems." In Handbook of Combinatorial Optimization. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-7997-1_44.

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Katoh, Naoki, Akiyoshi Shioura, and Toshihide Ibaraki. "Resource Allocation Problems." In Handbook of Combinatorial Optimization. Springer New York, 2024. https://doi.org/10.1007/978-1-4614-6624-6_44-1.

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Kolker, Alexander. "Linear and Probabilistic Resource Optimization and Allocation Problems." In Healthcare Management Engineering: What Does This Fancy Term Really Mean? Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-2068-2_3.

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Gromov, Dmitry, Ingo Bulla, and Ethan O. Romero-Severson. "Optimal Resource Allocation for HIV Prevention and Control." In Trends in Biomathematics: Modeling, Optimization and Computational Problems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91092-5_9.

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Powell, Warren B. "The Next Generation of Optimization: A Unified Framework for Dynamic Resource Allocation Problems." In Optimization in Large Scale Problems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28565-4_9.

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Granmo, Ole-Christoffer, and B. John Oommen. "Learning Automata-Based Solutions to Stochastic Nonlinear Resource Allocation Problems." In Intelligent Systems for Knowledge Management. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04170-9_1.

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Kolker, Alexander. "Resource Allocation and Service Line Problems: Solutions Using Linear Optimization." In Healthcare Management Engineering In Action. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53663-2_5.

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Yazidi, Anis, Hugo Lewi Hammer, and Tore Møller Jonassen. "Two-Timescale Learning Automata for Solving Stochastic Nonlinear Resource Allocation Problems." In Advances in Artificial Intelligence: From Theory to Practice. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60042-0_10.

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Heinz, Stefan, Wen-Yang Ku, and J. Christopher Beck. "Recent Improvements Using Constraint Integer Programming for Resource Allocation and Scheduling." In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38171-3_2.

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Conference papers on the topic "Optimization nonlinear resource allocation problems"

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Wallrath, Roderich, Edwin Zondervan, and Meik B. Franke. "Integration of MILP and Discrete-Event Simulation for Flowshop Scheduling Using Benders Decomposition." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.180841.

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Real-world flowshop problems which are very common in the chemical industry are often difficult to solve in a reasonable time with allocation, sequencing, and lot-sizing decisions. Although great progress has been made in the last 20 years regarding MILP model formulations and solution algorithms, realistically-sized flowshop problems with resource and buffer constraints are still difficult to solve. On the other hand, discrete-event simulation (DES) allows for very detailed modelling of process plants, but lacking of optimization capabilities. Simulation Optimization (SO) combines the high-de
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Zhang, Haopeng, and Qing Hui. "Multiagent Coordination Optimization Based Model Predictive Control Strategy With Application to Balanced Resource Allocation." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-5954.

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Model predictive control (MPC) is a heuristic control strategy to find a consequence of best controllers during each finite-horizon regarding to certain performance functions of a dynamic system. MPC involves two main operations: estimation and optimization. Due to high complexity of the performance functions, such as, nonlinear, non-convex, large-scale objective functions, the optimization algorithms for MPC must be capable of handling those problems with both computational efficiency and accuracy. Multiagent coordination optimization (MCO) is a recently developed heuristic algorithm by embed
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Al-Dulaimi, Avmen, Mohammed Al-Dulaimi, and Dmvtro Asevev. "Realization of resource blocks allocation in LTE downlink in the form of nonlinear optimization." In 2016 13th International Conference on Modern Problems of Radio Engineering. Telecommunications and Computer Science (TCSET). IEEE, 2016. http://dx.doi.org/10.1109/tcset.2016.7452140.

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Bangla, Ajay Kumar, and David A. Castanon. "Auction algorithm for Nonlinear Resource Allocation Problems." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5717911.

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Ashish and Deepak Kumar. "Resource allocation problems in modular software." In 2014 3rd International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions). IEEE, 2014. http://dx.doi.org/10.1109/icrito.2014.7014703.

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Shih, Kuo-Chuan, and Shu-Shun Liu. "Optimization Model of External Resource Allocation for Resource-Constrained Project Scheduling Problems." In 23rd International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 2006. http://dx.doi.org/10.22260/isarc2006/0159.

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Huang, Jinjia, Fan Wang, and Ning Shi. "Resource Allocation Problems in Port Operations: A Literature Review." In 2014 Seventh International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2014. http://dx.doi.org/10.1109/cso.2014.35.

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Konnov, Igor, Aleksey Kashuba, and Erkki Laitinen. "Dual Decomposition Methods for Nonlinear Resource Allocation Problems in Telecommunication Networks." In 2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI). IEEE, 2017. http://dx.doi.org/10.1109/mcsi.2017.42.

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Srivastava, Amber, and Srinivasa M. Salapaka. "Robustness Analysis for Simultaneous Resource Allocation and Route Optimization Problems." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5179.

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This paper presents the robustness analysis for an algorithm that solves simultaneous resource allocation and route optimization problem (SARO). These problems appear in the context of multi-hop routing applications in sensor networks, which require placement of multiple resource nodes and determining routes from each sensor location to a common data destination center via these resource nodes. In [1], we proposed an algorithm based on Maximum Entropy Principle that addressed the determination of locations of these resource nodes and the corresponding multi-hop routing problem such that the to
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Tan, Chee Wei. "Wireless network resource allocation optimization by nonlinear Perron-Frobenius theory." In 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS). IEEE, 2014. http://dx.doi.org/10.1109/ursigass.2014.6929343.

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