Gotowe bibliografie tematyczne / Resource allocation

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Gotowa bibliografia na temat „Resource allocation”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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Artykuły w czasopismach na temat "Resource allocation"

Edavalath, Sheena, and Manikandasaran S. Sundaram. "Cost-based resource allocation method for efficient allocation of resources in a heterogeneous cloud environment." Scientific Temper 14, no. 04 (December 27, 2023): 1339–44. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.41.

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Cloud computing is appealing due to features like adaptability, portability, utility service and on-demand service. Cloud resource providers are a source of computing, and each provider delivers different types of resources. In an active cloud environment, timely resource allocation is more important. In order to increase the effectiveness and user-friendliness of resource allocation in the heterogeneous cloud, the paper suggests an efficient cost-based resource allocation (ECRA) method and framework. In the heterogeneous cloud, there is no centralized resource allocation manager (CRAM) to get all requested resources from a single counter. The proposed methodology for allocating resources divides them according to their cost. The paper’s framework for allocating resources consists of various parts. The Unified Heterogeneous Resource Allocation Manager (UHRAM) part of the framework collects and manages resources from several cloud resource providers. The resource identifier is one of the components in the framework, which is coupled to UHRAM to determine the cost of the resources. The low-cost resources are scheduled and to be in a ready state for allocation. The proposed ECRA is simulated and compared based on parameters like total computation time, response time and resource allocation percentage with existing resource allocation methods. The results prove that the proposed ECRA is efficient in allocating the resources in minimum response time and it allocates maximum resources for lower cost.

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Zheng, Junjun. "Optimization of Resource Allocation of University Innovation and Entrepreneurship Education Based on Collaborative Filtering Algorithm." Journal of Electrical Systems 20, no. 3s (March 31, 2024): 1853–62. http://dx.doi.org/10.52783/jes.1724.

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Entrepreneurship education resource allocation involves the strategic distribution of resources to support programs and initiatives aimed at fostering entrepreneurial skills and mindset among students. These resources can include funding, faculty support, curriculum development, mentorship opportunities, and access to networks and facilities. Effective resource allocation ensures that entrepreneurship education programs are adequately equipped to provide students with the knowledge, skills, and support needed to succeed as entrepreneurs. By prioritizing resource allocation to areas such as experiential learning, incubation spaces, and networking events, institutions can create a vibrant ecosystem that nurtures innovation and encourages entrepreneurial ventures. This paper presents an innovative approach to optimizing the resource allocation of university innovation and entrepreneurship education through the application of a collaborative filtering algorithm, enhanced by Flemingo Optimized Collaborative Filtering Classification (FOCFC). The study aims to address the challenge of efficiently allocating resources such as funding, mentorship, and infrastructure to support innovation and entrepreneurship initiatives within universities. Through simulated experiments and empirical validations, the effectiveness of the FOCFC-enhanced collaborative filtering algorithm is evaluated in recommending resource allocations tailored to the unique needs and preferences of students and entrepreneurial ventures. Results demonstrate significant improvements in accuracy and efficiency compared to traditional methods, with the FOCFC model achieving a precision rate of 95% in recommending resource allocations. Additionally, the model provides valuable insights into emerging trends and opportunities in the innovation and entrepreneurship ecosystem, enabling universities to adapt their resource allocation strategies proactively. These findings highlight the potential of collaborative filtering algorithms with FOCFC in optimizing resource allocation for university innovation and entrepreneurship education, fostering a supportive and conducive environment for entrepreneurial success.

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1Wang, Xiaojing *2 Tong Wei 1Ren Jia 1Ding Linjie 2. Liu Jingning. "WEIGHTED FAIRNESS RESOURCE ALLOCATION OF DISKS IN XEN." International Journal on Cloud Computing: Services and Architecture(IJCCSA) 2, June (September 29, 2018): 01–10. https://doi.org/10.5281/zenodo.1438577.

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Virtualization improves the utilization ratio of resource, saving considerable amount of equipment, energy, management, etc. However, the benefits of virtualization are at the penalty of fairness but inefficient resource sharing. Various tasks in virtual domains often have different demands of resources. Therefore, allocation of resource and scheduling function are particularly important. A weighted max-min fairness allocation of disk resources is proposed in virtual platform. We focus on properties of sharing incentive, initial irrelevant and Pareto efficiency while designing the allocation algorithm. This Allocator is implemented in Xen virtual block devices manager. Tests in this paper show that the weighted max-min Allocator has a better allocation of resources and guarantees the quality of services.

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Edavalath, Sheena, and Manikandasaran S. Sundaram. "MARCR: Method of allocating resources based on cost of the resources in a heterogeneous cloud environment." Scientific Temper 14, no. 03 (June 6, 2023): 576–81. http://dx.doi.org/10.58414/scientifictemper.2023.14.3.03.

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The cloud is an intelligent technology that provides requested services to users. It offers unlimited services for the users. Many small and medium-scale industries are startup their businesses to the next level using cloud computing. The services have been provided to the users by allocating the requested resources. Allocating resources without waste and with the finest allocation is a critical task in the cloud. This paper proposes a method for allocating resources using the cost of the resource. Resource allocation follows a priority system when allocating resources. The proposed method gives priority to low-cost resources. The cost denotes the service cost of the resource. The requested resource is assigned to the user by the CSP, who provides the specific resource at a low cost. This proposed method suggests a UHRAM for collecting and allocating the resources from the different CSPs. UHRAM is a centralized hub for delivering requested resources to users, and it maintains a repository of details about the resources from all CSPs in the heterogeneous cloud. The proposed method is implemented with the user’s data. The results from the comparison show that the proposed cost-based resource allocation method is more efficient than existing methods.

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M, Sumathi, Niranjana B, Akshaya C, Ajitha M, and Bhavadharanee M. "Round Robin Based Efficient Resource Allocation and Utilization in an Organization." International Research Journal of Multidisciplinary Technovation 2, no. 2 (March 30, 2020): 16–22. http://dx.doi.org/10.34256/irjmt2023.

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In an organization, resource allocation to a request is a complex task. Traditionally, resource allocation is done through manually with high time consumption. Similarly, collision is occurring for allocating a single resource to multiple requests. Thus, leads to complex problems and slow-down the working process. The existing resource allocation technique, allocate resources continuously to a specific request and omit another request. This kind of allocation technique also leads to lots of critical issues. That is the non-allocated process never gets a resource. To overcome these issues, the Round Robin based Resource allocation and Utilization technique is proposed in this work. The Round Robin technique allocates resources to the request in an efficient with equal priority. Similarly, the proposed technique reduces collision and takes less time for mapping a resource with a request. The experimental results shows improved accuracy than the traditional resource allocation technique.

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Sathish, Kuppani, and A. Rama Mohan Reddy. "Resource Allocation Mechanism with New Models for Grid Environment." International Journal of Grid and High Performance Computing 5, no. 2 (April 2013): 1–26. http://dx.doi.org/10.4018/jghpc.2013040101.

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Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.

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Artiushenko, Bohdan. "A Nucleolus-Based Approach for Cloud Resource Allocation." NaUKMA Research Papers. Computer Science 7 (May 12, 2025): 25–30. https://doi.org/10.18523/2617-3808.2024.7.25-30.

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Cloud computing has transformed organizational operations by enabling flexible resource allocation and reducing upfront hardware investments. However, the growing complexity of resource management, particularly for computing instances, has led to challenges in cost control and resource allocation. Fair allocation policies, such as max-min fairness and Dominant Resource Fairness, aim to distribute resources fairly among users. In recent years, the FinOps framework has emerged to address cloud cost management, empowering teams to manage their own resource usage and budgets. The allocation of resources among competing product teams within an organization can be modelled as a cooperative game, where teams with competing priorities must negotiate resource allocation based on their claims and the available budget.The article explores cloud resource allocation as a cooperative game, particularly in situations where the total budget is insufficient to meet all teams’ demands. Several resource allocation methods are discussed, including the proportional rule and the nucleolus-based approach, which seeks to minimize the coalitions’ incentives to deviate. The nucleolus method offers a stable and fair solution by distributing resources in a way that maximizes stability and reduces the likelihood of coalitions deviating from the overall allocation. This approach ensures that no team is allocated more than its claim and maintains fairness by adhering to principles such as claim boundaries, monotonicity, and resource constraints. Ultimately, the nucleolus-based method is proposed as an effective solution for allocating cloud resources in a cooperative and stable manner, ensuring that resource allocation is both fair and efficient.

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Lei, Xiaoli. "Resource Sharing Algorithm of Ideological and Political Course Based on Random Forest." Mathematical Problems in Engineering 2022 (May 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/8765166.

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Three aspects of the system’s online resource distribution and application are built around subject, object, and intermediary resources. The invention relates to a method for allocating resources based on the random forest algorithm. The resource allocation process entails the following steps: constructing a mathematical model of the resource allocation process, defining a mathematical model of the resource allocation process for the target object, and designing the cost function. The training data set for random forest is constructed using the classification concept. It is based on the mathematical model of resource allocation and cost function. Generation of random forests and prediction of target objects are based on historical data. Resource allocation steps are based on predictive structure. The invention provides a resource allocation method that satisfies task completion degree constraints and includes a resource allocation algorithm based on random forest with a high probability of finding an optimal solution. It also addresses the issue that intelligent optimization algorithms such as genetic algorithms are prone to fall into local optimum.

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Othmen, Salwa, Wahida Mansouri, and Radhia Khdhir. "Applying Artificial Intelligence Techniques For Resource Management in the Internet of Things (IoT)." International journal of electrical and computer engineering systems 16, no. 2 (February 6, 2025): 183–94. https://doi.org/10.32985/ijeces.16.2.1.

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Internet of Things (IoT) applications in smart cities (SCs) rely on free-flow services streamlined by artificial intelligence (AI) paradigms. However, the nature of resource constraint prevails due to external infrastructure costs and energy-based allocations. Existing approaches to smart city resource distribution rely on static thresholds or reactive responses, which are not always sufficient. These approaches may limit system performance and scalability in dynamic IoT environments owing to increased energy consumption, postponed resource allocation, and frequent device failures. This article introduces a Concerted Resource Management (CRM) using the Leveled Reinforcement Training (LRT) method. The proposed method accurately identifies cost-complex and high energy-consuming sharing intervals based on service response time and device failure. The reinforcement learning and training concerts both energy and device incorporations for SC applications based on its demand. This process requires leveled training in resource management, from energy depletion to device activeness. The interrupted sessions are identified using resource allocation failures, and the active resources with optimal energy expenses are selected to pursue resource management. The training method thus identifies the demands based on independent or concerted resource allocations to mitigate the management constraints in an SC environment. This proposed method reduces the resource constraint-based waiting for allocations and allocation failures in any SC application services. Under the varying devices, the following is observed: Improvements: 9.1% (Allocation Rate), 10% (Device Detection), 11.88% (Constraint Mitigation— Energy), 9.06% (Constraint Mitigation—Resource Allocation); Reduced: 8.01% (Allocation Failure), 9.64% (Waiting Time).

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Willmott, Yvonne. "Resource allocation." Nursing Standard 4, no. 28 (April 4, 1990): 46. http://dx.doi.org/10.7748/ns.4.28.46.s51.

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Rozprawy doktorskie na temat "Resource allocation"

AuYoung, Alvin. "Practical market-based resource allocation." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3397175.

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Thesis (Ph. D.)--University of California, San Diego, 2010. Title from first page of PDF file (viewed March 29, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 146-155).

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Choueiry, Berthe Y. Choueiry Berthe Yazid. "Abstraction methods for resource allocation /." [S.l.] : [s.n.], 1994. http://library.epfl.ch/theses/?nr=1292.

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Muñoz, i. Solà Víctor. "Robustness on resource allocation problems." Doctoral thesis, Universitat de Girona, 2011. http://hdl.handle.net/10803/7753.

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En problemes d'assignació de recursos, normalment s'han de tenir en compte les incerteses que poden provocar canvis en les dades inicials. Aquests canvis dificulten l'aplicabilitat de les planificacions que s'hagin fet inicialment. Aquesta tesi se centra en l'elaboració de tècniques que consideren la incertesa alhora de cercar solucions robustes, és a dir solucions que puguin continuar essent vàlides encara que hi hagi canvis en l'entorn. Particularment, introduïm el concepte de robustesa basat en reparabilitat, on una solució robusta és una que pot ser reparada fàcilment en cas que hi hagi incidències. La nostra aproximació es basa en lògica proposicional, codificant el problema en una fórmula de satisfactibilitat Booleana, i aplicant tècniques de reformulació per a la generació de solucions robustes. També presentem un mecanisme per a incorporar flexibilitat a les solucions robustes, de manera que es pugui establir fàcilment el grau desitjat entre robustesa i optimalitat de les solucions. Resource allocation problems usually include uncertainties that can produce changes in the data of the problem. These changes may cause difficulties in the applicability of the solutions. This thesis is focused in the elaboration of techniques that take into account such uncertainties while searching for robust solutions, i.e. solutions that can remain valid even if there are changes in the environment. Particularly, we introduce the concept of robustness based on reparability, where a robust solution is one that can be easily repaired when unexpected events occur. Our approach is based in propositional logic, encoding the problem to a Boolean formula, and applying reformulation techniques in order to generate robust solutions. Additionally, we present a mechanism to incorporate flexibility to the robust solutions, so that one can easily set the desired degree between optimality and robustness.

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Tureli, Didem Kivanc. "Resource allocation for multicarrier communications /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/6068.

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Lai, John Kwang. "Truthful and Fair Resource Allocation." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10928.

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How should we divide a good or set of goods among a set of agents? There are various constraints that we can consider. We consider two particular constraints. The first is fairness - how can we find fair allocations? The second is truthfulness - what if we do not know agents valuations for the goods being allocated? What if these valuations need to be elicited, and agents will misreport their valuations if it is beneficial? Can we design procedures that elicit agents' true valuations while preserving the quality of the allocation? We consider truthful and fair resource allocation procedures through a computational lens. We first study fair division of a heterogeneous, divisible good, colloquially known as the cake cutting problem. We depart from the existing literature and assume that agents have restricted valuations that can be succinctly communicated. We consider the problems of welfare-maximization, expressiveness, and truthfulness in cake cutting under this model. In the second part of this dissertation we consider truthfulness in settings where payments can be used to incentivize agents to truthfully reveal their private information. A mechanism asks agents to report their private preference information and computes an allocation and payments based on these reports. The mechanism design problem is to find incentive compatible mechanisms which incentivize agents to truthfully reveal their private information and simultaneously compute allocations with desirable properties. The traditional approach to mechanism design specifies mechanisms by hand and proves that certain desirable properties are satisfied. This limits the design space to mechanisms that can be written down and analyzed. We take a computational approach, giving computational procedures that produce mechanisms with desirable properties. Our first contribution designs a procedure that modifies heuristic branch and bound search and makes it usable as the allocation algorithm in an incentive compatible mechanism. Our second contribution draws a novel connection between incentive compatible mechanisms and machine learning. We use this connection to learn payment rules to pair with provided allocation rules. Our payment rules are not exactly incentive compatibility, but they minimize a measure of how much agents can gain by misreporting. Engineering and Applied Sciences

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Reid, Jane Margaret. "Resource allocation during avian incubation." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392460.

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Owen, Robert Wyn. "Strategies for stochastic resource allocation." Thesis, University of Newcastle Upon Tyne, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315563.

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Robinson, Edward Robert. "Resource allocation via competing marketplaces." Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/1647/.

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This thesis proposes a novel method for allocating multi-attribute computational resources via competing marketplaces. Trading agents, working on behalf of resource consumers and providers, choose to trade in resource markets where the resources being traded best align with their preferences and constraints. Market-exchange agents, in competition with each other, attempt to provide resource markets that attract traders, with the goal of maximising their profit. Because exchanges can only partially observe global supply and demand schedules, novel strategies are required to automate their search for market niches. By applying a novel methodology, which is also used to explore, for the first time, the generalisation ability of market mechanisms, novel attribute-level selection (ALS) strategies are analysed in competitive market environments. Results from simulation studies suggest that using these ALS strategies, market-exchanges can seek out market niches under a variety of environmental conditions. In order to facilitate traders' selection between dynamic competing marketplaces, this thesis explores the application of a reputation system, and simulation results suggest reputation-based market-selection signals can lead to more efficient global resource allocations in dynamic environments. Further, a subjective reputation system, grounded in Bayesian statistics, allows traders to identify and ignore the opinions of those attempting to falsely damage or bolster marketplace reputation.

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Nguyen, Quang (Quang Duc) 1972. "Optimizing engineering analysis resource allocation." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/84521.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2001. Includes bibliographical references (p. 72-73). by Quang Nguyen. S.M. M.B.A.

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Lien, Yuan-Chuan. "Resource allocation in matching markets /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Książki na temat "Resource allocation"

Ushakov, Igor A., ed. Optimal Resource Allocation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118400715.

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Luss, Hanan. Equitable Resource Allocation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118449189.

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Langholtz, Harvey J., Antoinette T. Marty, Christopher T. Ball, and Eric C. Nolan. Resource-Allocation Behavior. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1131-1.

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1948-, Langholtz Harvey J., ed. Resource-allocation behavior. Boston: Kluwer Academic Publishers, 2003.

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A, Bazzaz F., and Grace J. 1945-, eds. Plant resource allocation. San Diego: Academic Press, 1997.

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United States. General Accounting Office. RCED. BLM resource allocation. Washington, D.C: The Office, 1992.

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United States. General Accounting Office. RCED., ed. BLM resource allocation. Washington, D.C: The Office, 1992.

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Resources, Great Britain National Assembly for Wales National Steering Group on the Allocation of NHS. NHS resource allocation review. Cardiff: National Assembly for Wales = Cynulliad Cenedlaethol Cymru, 2001.

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Chuku, Dike. Resource allocation in education. Port Harcourt: Andisen Nigeria, 1987.

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Great Britain. National Assembly for Wales. National Steering Group on the Allocation of NHS Resources. NHS resource allocation review. Cardiff: National Assembly for Wales = Cynulliad Cenedlaethol Cymru, 2001.

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Części książek na temat "Resource allocation"

Huo, Jiazhen, and Zhisheng Hong. "Resource Allocation." In Service Science in China, 167–221. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34497-8_6.

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Trsek, Henning. "Resource Allocation." In Isochronous Wireless Network for Real-time Communication in Industrial Automation, 91–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49158-4_6.

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Srikant, R. "Resource Allocation." In Systems & Control: Foundations & Applications, 7–21. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8216-3_2.

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Gillman, Richard Alan, and David Housman. "Resource Allocation." In Game Theory, 275–94. Boca Raton : CRC Press, Taylor & Francis Group, 2019. | Series: Textbooks in mathematics: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315156880-9.

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Bland, Lucie M., Christine Li, Tom Kompas, Long Chu, Hoa-Thi-Minh Nguyen, and Susan M. Hester. "Resource Allocation." In Biosecurity, 135–55. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003253204-13.

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Weik, Martin H. "resource allocation." In Computer Science and Communications Dictionary, 1481. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_16223.

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ten Have, Henk, and Maria do Céu Patrão Neves. "Resource Allocation." In Dictionary of Global Bioethics, 911. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54161-3_449.

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Andrade, Gabriel. "Resource allocation." In Medical Ethics and Moral Psychology, 117–38. London: Routledge, 2025. https://doi.org/10.4324/9781003541653-6.

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Zili, He. "Resource Allocation." In Dictionary of Contemporary Chinese Economics, 50–51. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-4036-9_27.

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Langholtz, Harvey J., Antoinette T. Marty, Christopher T. Ball, and Eric C. Nolan. "An Introduction to Resource-Allocation Behavior." In Resource-Allocation Behavior, 1–8. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1131-1_1.

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Streszczenia konferencji na temat "Resource allocation"

Chen, Shenghui, Yagiz Savas, Mustafa O. Karabag, Brian M. Sadler, and Ufuk Topcu. "Deceptive Planning for Resource Allocation." In 2024 American Control Conference (ACC), 4188–95. IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644373.

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Kyropoulou, Maria, Warut Suksompong, and Alexandros A. Voudouris. "Almost Envy-Freeness in Group Resource Allocation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/57.

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We study the problem of fairly allocating indivisible goods between groups of agents using the recently introduced relaxations of envy-freeness. We consider the existence of fair allocations under different assumptions on the valuations of the agents. In particular, our results cover cases of arbitrary monotonic, responsive, and additive valuations, while for the case of binary valuations we fully characterize the cardinalities of two groups of agents for which a fair allocation can be guaranteed with respect to both envy-freeness up to one good (EF1) and envy-freeness up to any good (EFX). Moreover, we introduce a new model where the agents are not partitioned into groups in advance, but instead the partition can be chosen in conjunction with the allocation of the goods. In this model, we show that for agents with arbitrary monotonic valuations, there is always a partition of the agents into two groups of any given sizes along with an EF1 allocation of the goods. We also provide an extension of this result to any number of groups.

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Bott, Terry F., and Stephen W. Eisenhawer. "A Structured Approach to Resource Allocation." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-2998.

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Allocating limited resources among competing candidates is an important problem in management. In this paper, we describe a structured and flexible approach to resource allocation using logic-evolved decision (LED) analysis. LED analysis uses logic models to generate an exhaustive set of competing alternatives and the inferential model that is used for preference ordering of these alternatives. The inferential models can use data in numerical, linguistic, or mixed forms; uncertainty in the evaluation results can be expressed using probabilistic- or linguistic-based methods. We illustrate the use of LED analysis for an allocation problem with numerical input data and for an allocation problem with only linguistic input data.

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Bei, Xiaohui, Alexander Lam, Xinhang Lu, and Warut Suksompong. "Welfare Loss in Connected Resource Allocation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/294.

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We study the allocation of indivisible goods that form an undirected graph and investigate the worst-case welfare loss when requiring that each agent must receive a connected subgraph. Our focus is on both egalitarian and utilitarian welfare. Specifically, we introduce the concept of egalitarian (resp., utilitarian) price of connectivity, which captures the worst-case ratio between the optimal egalitarian (resp., utilitarian) welfare among all allocations and that among the connected allocations. We provide tight or asymptotically tight bounds on the price of connectivity for various large classes of graphs when there are two agents, and for paths, stars and cycles in the general case. Many of our results are supplemented with algorithms which find connected allocations with a welfare guarantee corresponding to the price of connectivity.

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Schubert, Martin, and Holger Boche. "Robust Resource Allocation." In 2006 IEEE Information Theory Workshop. IEEE, 2006. http://dx.doi.org/10.1109/itw.2006.322880.

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Schubert, Martin, and Holger Boche. "Robust Resource Allocation." In 2006 IEEE Information Theory Workshop - ITW '06 Chengdu. IEEE, 2006. http://dx.doi.org/10.1109/itw2.2006.323695.

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Funaro, Liran, Orna Agmon Ben-Yehuda, and Assaf Schuster. "Stochastic resource allocation." In the 15th ACM SIGPLAN/SIGOPS International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3313808.3313815.

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Patil, Sulakshana, Vijaya Rajeshwarkar, VaIshali Velhal, and Pravin Latne. "Resource allocation management." In 2014 IEEE Global Conference on Wireless Computing and Networking (GCWCN). IEEE, 2014. http://dx.doi.org/10.1109/gcwcn.2014.7030896.

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Ahmed, Rafeeq, Mohammed A. Qadeer, and Musheer Ahmad. "Bandwidth Resource Allocation." In 2009 International Conference on Future Computer and Communication (ICFCC). IEEE, 2009. http://dx.doi.org/10.1109/icfcc.2009.133.

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Sanford, Caitlyn, Sarah Valley, Kaylee Keegan, Reed Foote, and Natalie Cherbaka. "Resource Allocation Playbook." In 2024 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2024. http://dx.doi.org/10.1109/sieds61124.2024.10534700.

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Raporty organizacyjne na temat "Resource allocation"

Stokes, Brian J., and Gregory S. Parnell. Alternate Resource Allocation Techniques. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada405460.

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Müller, Benito. Performance-based formulaic resource allocation. Oxford Institute for Energy Studies, January 2014. http://dx.doi.org/10.26889/9781907555862.

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Hansen, Jeff, Scott Hissam, B. C. Meyers, Gabriel Moreno, Daniel M. Plakosh, Joseph Seibel, and Lutz Wrage. Resource Allocation in Dynamic Environments. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada609913.

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Keller, William C. The Defense Resource Allocation Process. Fort Belvoir, VA: Defense Technical Information Center, April 1997. http://dx.doi.org/10.21236/ada326185.

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Kogan, Leonid, Dimitris Papanikolaou, Amit Seru, and Noah Stoffman. Technological Innovation, Resource Allocation, and Growth. Cambridge, MA: National Bureau of Economic Research, January 2012. http://dx.doi.org/10.3386/w17769.

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Leivent, Jonathan I., and Ann M. Wollrath. Resource Allocation in Distributed Memory Multiprocessing. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada243165.

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Kozlowski, Steve W., and Richard P. DeShon. Optimizing Dynamic Resource Allocation in Teamwork. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada478848.

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Yao, Y., T. Edmunds, D. Papageorgiou, and R. Alvarez. Optimal Resource Allocation in Electrical Network Defense. Office of Scientific and Technical Information (OSTI), January 2004. http://dx.doi.org/10.2172/15009766.

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Feldstein, Martin. The CEA: From Stabilization to Resource Allocation. Cambridge, MA: National Bureau of Economic Research, January 1997. http://dx.doi.org/10.3386/w5908.

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Newes, Emily, Brian Bush, Daniel Inman, Yolanda Lin, Trieu Mai, Andrew Martinez, David Mulcahy, et al. Biomass Resource Allocation among Competing End Uses. Office of Scientific and Technical Information (OSTI), May 2012. http://dx.doi.org/10.2172/1041351.

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