Relevant bibliographies by topics / Resource allocation model

Academic literature on the topic 'Resource allocation model'

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Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Resource allocation model"

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Wang, Yanyan, and Baiqing Sun. "A Multiobjective Allocation Model for Emergency Resources That Balance Efficiency and Fairness." Mathematical Problems in Engineering 2018 (October 14, 2018): 1–8. http://dx.doi.org/10.1155/2018/7943498.

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Efficiency and fairness are two important goals of disaster rescue. However, the existing models usually unilaterally consider the efficiency or fairness of resource allocation. Based on this, a multiobjective emergency resource allocation model that can balance efficiency and fairness is proposed. The object of the proposed model is to minimize the total allocating costs of resources and the total losses caused by insufficient resources. Then the particle swarm optimization is applied to solve the model. Finally, a computational example is conducted based on the emergency relief resource allocation after Ya’an earthquake in China to verify the applicability of the proposed model.
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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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Kaushik, Achal, and Deo Prakash Vidyarthi. "Green Energy Model for Grid Resource Allocation." International Journal of Grid and High Performance Computing 6, no. 2 (April 2014): 52–73. http://dx.doi.org/10.4018/ijghpc.2014040104.

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The computational grid helps in faster execution of compute intensive jobs. Many characteristic parameters are intended to be optimized while making resource allocation for job execution in computational grid. Most often, the green energy aspect, in which one tries for better energy utilization, is ignored while allocating the grid resources to the jobs. The conventional systems, which propose energy efficient scheduling strategies, ignore other Quality of Service parameters while scheduling the jobs. The proposed work tries to optimize the energy in resource allocation to make it a green energy model. It explores how effectively the jobs submitted to the grid can be executed for optimal energy uses making no compromise on other desired related characteristic parameters. A graph theoretic model has been developed for this purpose. The performance study of the proposed green energy model has been experimentally evaluated by simulation. The result reveals the benefits and gives an insight for an energy efficient resource allocation.
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Wang, Shi-long, Zhe-qi Zhu, and Ling Kang. "Resource allocation model in cloud manufacturing." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 10 (April 9, 2015): 1726–41. http://dx.doi.org/10.1177/0954406215582016.

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Allocation of resources in cloud manufacturing is one of the key points of cloud manufacturing technology. To optimize cloud manufacturing resource management, it is indispensable to improve the process and efficiency of scheduling by matching jobs with resources according to the size of the job and establishing a four-level structure for resources based on the enterprise level, workshop level, primitive cell and service level. A resource scheduling model containing four indicators of cost, time, quality and risk with their own mathematical expressions is proposed. We also simulate the model with a new swap-shuffled leap-frog algorithm (SSFLA). Finally, we test the algorithm with different example scales and different end conditions and compare it with particle swarm optimization (PSO) and genetic algorithm (GA). The result shows that SSFLA performs well in convergence speed and robustness and does much better than PSO and GA. This algorithm provides an alternative choice for allocation of resources in cloud manufacturing model.
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Pujiyanta, Ardi, Lukito Edi Nugroho, and Widyawan Widyawan. "Resource allocation model for grid computing environment." International Journal of Advances in Intelligent Informatics 6, no. 2 (July 12, 2020): 185. http://dx.doi.org/10.26555/ijain.v6i2.496.

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Grid computing is a collection of heterogeneous resources that is highly dynamic and unpredictable. It is typically used for solving scientific or technical problems that require a large number of computer processing cycles or access to substantial amounts of data. Various resource allocation strategies have been used to make resource use more productive, with subsequent distributed environmental performance increases. The user sends a job by providing a predetermined time limit for running that job. Then, the scheduler gives priority to work according to the request and scheduling policy and places it in the waiting queue. When the resource is released, the scheduler selects the job from the waiting queue with a specific algorithm. Requests will be rejected if the required resources are not available. The user can re-submit a new request by modifying the parameter until available resources can be found. Eventually, there is a decrease in idle resources between work and resource utilization, and the waiting time will increase. An effective scheduling policy is required to improve resource use and reduce waiting times. In this paper, the FCFS-LRH method is proposed, where jobs received will be sorted by arrival time, execution time, and the number of resources needed. After the sorting process, the work will be placed in a logical view, and the job will be sent to the actual resource when it executes. The experimental results show that the proposed model can increase resource utilization by 1.34% and reduce waiting time by 20.47% when compared to existing approaches. This finding could be beneficially implemented in cloud systems resource allocation management.
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Vijayaraj, N., and T. Senthil Murugan. "Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 1008–19. http://dx.doi.org/10.2174/2213275912666190404160733.

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Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.
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BINTZ, JASON, and SUZANNE LENHART. "OPTIMAL RESOURCE ALLOCATION FOR A DIFFUSIVE POPULATION MODEL." Journal of Biological Systems 28, no. 04 (December 2020): 945–76. http://dx.doi.org/10.1142/s0218339020500230.

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The spatial distribution of resources for diffusive populations can have a strong effect on population abundance. We investigate the optimal allocation of resources for a diffusive population. Population dynamics are represented by a parabolic partial differential equation with density-dependent growth and resources are represented through their space- and time-varying influence on the growth function. We consider both local and integral constraints on resource allocation. The goal is to maximize the abundance of the population while minimizing the cost of resource allocation. After characterizing the optimal control in terms of the population solution and the adjoint functions, we illustrate several scenarios numerically. The effects of initial and boundary conditions are important for the optimal allocation of resources.
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BINTZ, JASON, and SUZANNE LENHART. "OPTIMAL RESOURCE ALLOCATION FOR A DIFFUSIVE POPULATION MODEL." Journal of Biological Systems 28, no. 04 (December 2020): 945–76. http://dx.doi.org/10.1142/s0218339020500230.

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The spatial distribution of resources for diffusive populations can have a strong effect on population abundance. We investigate the optimal allocation of resources for a diffusive population. Population dynamics are represented by a parabolic partial differential equation with density-dependent growth and resources are represented through their space- and time-varying influence on the growth function. We consider both local and integral constraints on resource allocation. The goal is to maximize the abundance of the population while minimizing the cost of resource allocation. After characterizing the optimal control in terms of the population solution and the adjoint functions, we illustrate several scenarios numerically. The effects of initial and boundary conditions are important for the optimal allocation of resources.
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Manzoor, Muhammad Faraz, Adnan Abid, Muhammad Shoaib Farooq, Naeem A. Azam, and Uzma Farooq. "Resource Allocation Techniques in Cloud Computing: A Review and Future Directions." Elektronika ir Elektrotechnika 26, no. 6 (December 18, 2020): 40–51. http://dx.doi.org/10.5755/j01.eie.26.6.25865.

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Cloud computing has become a very important computing model to process data and execute computationally concentrated applications in pay-per-use method. Resource allocation is a process in which the resources are allocated to consumers by cloud providers based on their flexible requirements. As the data is expanding every day, allocating resources efficiently according to the consumer demand has also become very important, keeping Service Level Agreement (SLA) between service providers and consumers in prospect. This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. In the light of the uniqueness of the models and techniques, the main aim of the resource allocation is to limit the overhead/expenses associated with it. This research aims to present a comprehensive, structured literature review on different aspects of resource allocation in cloud computing, including strategic, target resources, optimization, scheduling and power. More than 50 articles, between year 2007 and 2019, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and they are reviewed under clearly defined objectives. It presents a topical taxonomy of resource allocation dimensions, and articles under each category are discussed and analysed. Lastly, salient future directions in this area are discussed.
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Krishna, Siva Rama, and Mohammed Ali Hussain. "An Efficient Multi-Core Resource Allocation using the Multi-Level Objective Functions in Cloud Environment." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 957–64. http://dx.doi.org/10.2174/2666255813666200213105651.

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Background: In recent years, the computational memory and energy conservation have become a major problem in cloud computing environment due to the increase in data size and computing resources. Since, most of the different cloud providers offer different cloud services and resources use limited number of user’s applications. Objective: The main objective of this work is to design and implement a cloud resource allocation and resources scheduling model in the cloud environment. Methods: In the proposed model, a novel cloud server to resource management technique is proposed on real-time cloud environment to minimize the cost and time. In this model different types of cloud resources and its services are scheduled using multi-level objective constraint programming. Proposed cloud server-based resource allocation model is based on optimization functions to minimize the resource allocation time and cost. Results: Experimental results proved that the proposed model has high computational resource allocation time and cost compared to the existing resource allocation models. Conclusion: This cloud service and resource optimization model is efficiently implemented and tested in real-time cloud instances with different types of services and resource sets.
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Dissertations / Theses on the topic "Resource allocation model"

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Ng, Peng-Teng Peter. "Distributed dynamic resource allocation in multi-model situations." Thesis, Massachusetts Institute of Technology, 1985. http://hdl.handle.net/1721.1/15184.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1986.
MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.
Bibliography: leaves 351-354.
by Peng-Teng Peter Ng.
Ph.D.
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Bopanna, Sumanth M. "The Extended Quality-of-Service Resource Allocation Model." Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1130198581.

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Eedle, Elizabeth Margaret, and n/a. "Resoure allocation in selected Australian universities." Swinburne University of Technology, 2007. http://adt.lib.swin.edu.au./public/adt-VSWT20070828.164416.

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Australian universities are multi-million dollar operations employing tens of thousands of people. They attract revenue from a variety of government and non-government sources, and yet, as non-profit organisations they are judged by governments, peers and their communities on their performance in teaching and research rather than on a financial bottom line. In order to achieve these results, university managers must make decisions on how to allocate available funding throughout the university. Faced with competing demands on scarce funds, how do university managers make these choices? One option is to use a resource allocation model to 'crunch the numbers'. Resource allocation models can incorporate a number of elements - student and staff numbers, weightings and performance data, for example - to allocate available funds. These allocation models are used in different ways in different universities, but serve the same basic purpose of assisting decision-making on how much to allocate to different sections of the organisation. Such models operate within a process and context that includes the strategic aims of the University, the organisation structure, its committees and culture. This thesis contains case studies of resource allocation models and processes used in three Australian universities. It examines the methods used for resource allocation at the first and second levels within each university; that is, from the Vice-Chancellor to Dean (or equivalent), and from Dean to Head of School (or equivalent). Observations and conclusions are drawn on the models used, the processes surrounding the models, and the continuity between the two layers of allocations. The research finds all the case-study universities operate models at multiple levels in their organisations, and that there is a concerning lack of consistency and flow-through at these different levels. The messages that the university leadership intends to send through the allocations may be lost to managers one-process removed from them. The research also concludes that transparency is the most important element of the resource allocation process. University staff dealing with allocation processes will accept the results, even if they are not ideal, if they can understand how and why decisions were made. As a professional doctorate thesis, the aim is to provide a practical aid to people with responsibility for resource allocation in universities.
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Weikard, Hans-Peter. "Property rights and resource allocation in an overlapping generations model." Universität Potsdam, 1997. http://opus.kobv.de/ubp/volltexte/2006/854/.

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The paper is an enquiry into dynamic social contract theory. The social contract defines the rules of resource use. An intergenerational social contract in an economy with a single exhaustible resource is examined within a framework of an overlapping generations model. It is assumed that new generations do not accept the old social contract, and access to resources will be renegotiated between any incumbent generation and their successors. It turns out that later generations will be in an unfortunate position regardless of their bargaining power.
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Hosny, Hoda Mohamed. "Resource allocation and decision support in academic planning : a proposed model." Thesis, University of Leeds, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.291287.

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Messer, Alan. "A market model for controlled resource allocation in distributed operating systems." Thesis, City, University of London, 1999. http://openaccess.city.ac.uk/20134/.

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This thesis explores the potential for providing processes with control over their resource allocation in a general-purpose distributed system. Rather than present processes with blind explicit control or leave the decision to the operating system, a compromise, called process-centric resource allocation is proposed whereby processes have informed control of their resource allocation, while the operating system ensures fair consumption. The motivations for this approach to resource allocation and its background are reviewed culminating in the description of a set of desired attributes for such a system. A three layered architecture called ERA is then proposed and presented in detail. The lowest layer, provides a unified framework for processes to choose resources, describe their priority and describes the range of available resources. A resource information mechanism, used to support choices of distributed resources then utilises this framework. Finally, experimental demonstrations of process-centric resource allocation are used to illustrate the third layer. This design and its algorithms together provide a resource allocation system wherein distributed resources are shared fairly amongst competing processes which can choose their resources. The system allows processes to mimic traditional resource allocations and perform novel and beneficial resource optimisations. Experimental results are presented indicating that this can be achieved with low overhead and in a scalable fashion.
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Poole, Geoffrey Candler. "Modeling Forest Dynamics Based on Stand Level Resource Allocation." DigitalCommons@USU, 1989. https://digitalcommons.usu.edu/etd/6447.

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An ecologically based model of forest succession is presented. In the model, trees compete for a share of limited growth resources available from their environment. Competition is reflected by each tree's effect on the resource pool and is not explicitly modeled. Model parameters were fit to field data from subalpine forests of the Rocky Mountains. A technique for estimating model parameters from understory-tolerance rankings and silvical characteristics of each species is also presented. The model's output was consistent with our current understanding of forest dynamics. Emergent properties of the model also mimicked natural processes such as self-thinning, release, and maximum stand basal area as a function of species present and site quality.
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Kircheis, Robert. "On the Solution of State Constrained Optimal Control Problems in Economics." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-2195.

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In this work we examine a state constrained resource allocation model a with finite time horizon. Therefore, we use the necessary conditions of the Pontrjagin's Maximum Principle to find candidates for the solution and verify them later on using the sufficient conditions given by the duality concept of Klötzler. Moreover, we proof that the solution of the corresponding infinite horizon model does not fulfill the overtaking criterion of Weizsäcker.

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Quayyum, Zahidul. "Developing a needs-based resource allocation model for health care expenditure in Bangladesh." Thesis, University of Aberdeen, 2012. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=194789.

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The allocations of health care resources in Bangladesh are not based on the needs of the population. Equality in health care expenditure can be achieved by the use of needs-based resource allocation formulae. Applying such methods in Bangladesh can provide an essential guideline to achieve equality in resource allocation. This thesis examines the prospect of developing a needs-based allocation of health care resources. It attempts to address the counterfactual question of what would have been the allocation to each district had the needs of the population been accounted for. Two alternative approaches are considered. The first uses a simple capitation formula in which weights for the adjustment of the current allocation are generated directly based on the relative values of proxies for needs. The second approach predicts adjustment weights from the estimation of a standard econometric model of needs, controlling for a range of determinants including individual, household and district characteristics. Important predictors of current allocation were found to be the number of hospital beds and health workers rather than need factors. Important predictors of needs include demographic and socio-economic characteristics. The findings suggest that a needs-based allocation can be developed for Bangladesh. This research provides an alternative approach to generating weights showing systematic relationships between the need adjustment factors. The robustness of the methods used will be sensitive to the quality of the data and the assumptions of the models. As these approaches are based on sound economic analysis and are open to independent assessment, they will help to inform policy debate and can reduce the influence of politically motivated allocations. A gradual process of implementation and regular review of the methods used would be a way forward. Future areas of research may include: re-analysing data at smaller area level and use of different components of allocations.
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Imbeah, William Kweku Ansah. "Assessment of the effectiveness of the advanced programmatic risk analysis and management model (apram) as a decision support tool for construction projects." Texas A&M University, 2003. http://hdl.handle.net/1969.1/5813.

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Construction projects are complicated and fraught with so many risks that many projects are unable to meet pre-defined project objectives. Managers of construction projects require decision support tools that can be used to identify, analyze and implement measures that can mitigate the effects of project risks. Several risk analysis techniques have been developed over the years to enable construction project managers to make useful decisions that can improve the chances of project success. These risk analysis techniques however fail to simultaneously address risks relating to cost, schedule and quality. Also, construction projects may have scarce resources and construction managers still bear the responsibility of ensuring that project goals are met. Certain projects require trade-offs between technical and managerial risks and managers need tools that can help them do this. This thesis evaluates the usefulness of the Advanced Programmatic Risk Analysis and Management Model (APRAM) as a decision support tool for managing construction projects. The development of a visitor center in Midland, Texas was used as a case study for this research. The case study involved the implementation of APRAM during the concept phase of project development to determine the best construction system that can minimize the expected cost of failure. A risk analysis performed using a more standard approach yielded an expected cost of failure that is almost eight times the expected cost of failure yielded by APRAM. This study concludes that APRAM is a risk analysis technique that can minimize the expected costs of failure by integrating project risks of time, budget and quality through the allocation of resources. APRAM can also be useful for making construction management decisions. All identified component or material configurations for each alternative system however, should be analyzed instead of analyzing only the lowest cost alternative for each system as proposed by the original APRAM model. In addition, it is not possible to use decision trees to determine the optimal allocation of management reserves that would mitigate managerial problems during construction projects. Furthermore, APRAM does not address the issue of safety during construction and assumes all identifiable risks can be handled with money.
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Books on the topic "Resource allocation model"

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Chambers, Jay G. Measuring resources in education: From accounting to the resource cost model approach. [Washington, DC]: U.S. Dept. of Education, Office of Educational Research and Improvement, National Center for Education Statistics, 1999.

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Kakungu, Frank. Resource allocation model for the constituency development fund. Lusaka, Zambia: Zambia Institute of Policy Analysis and Research (ZIPAR), 2013.

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Atkeson, Andrew. Efficiency and equality in a simple model of efficient unemployment insurance. Cambridge, MA (1050 Massachusetts Avenue, Cambridge, MA 02138): National Bureau of Economic Research, 1993.

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Oregon. Legislative Assembly. Joint Legislative Audit Committee. Review of the resource allocation model of the Department of Higher Education. [Salem, Or.]: The Committee, 2000.

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Bargain, Olivier. Is the collective model of labor supply useful for tax policy analysis? a simulation exercise. Bonn, Germany: IZA, 2005.

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Allocation models: Specification, estimation, and applications. Cambridge, Mass: Ballinger, 1986.

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Capital income taxation and resource allocation. Amsterdam: North-Holland, 1987.

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Naoki, Katoh, ed. Resource allocation problems: Algorithmic approaches. Cambridge, Mass: MIT Press, 1988.

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Conrad, Jon M. Resource economics. 2nd ed. Cambridge: Cambridge University Press, 2010.

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Corless, Martin J. AIMD dynamics and distributed resource allocation. Philadelphia: Society for Industrial and Applied Mathematics, 2016.

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Book chapters on the topic "Resource allocation model"

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Langholtz, Harvey J., Antoinette T. Marty, Christopher T. Ball, and Eric C. Nolan. "The Optimal Model: Linear Programming." In Resource-Allocation Behavior, 9–17. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1131-1_2.

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Stańczak, Sławomir, Marcin Wiczanowski, and Holger Boche. "Chapter 4: Network Model." In Resource Allocation in Wireless Networks, 75–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11818762_4.

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Stańczak, Sławomir, Marcin Wiczanowski, and Holger Boche. "Network Model." In Fundamentals of Resource Allocation in Wireless Networks, 85–117. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-79386-1_4.

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Li, Chenzhao, and Sankaran Mahadevan. "Sensitivity Analysis for Test Resource Allocation." In Model Validation and Uncertainty Quantification, Volume 3, 143–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0_14.

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Sánchez, Joaquín F., Juan P. Ospina, Carlos Collazos, Henry Avendaño, Emiro De-la-Hoz-Franco, Zhoe Comas-Gonzalez, and N. Vanesa Landero. "Resource Allocation Model for a Computer System." In Lecture Notes in Electrical Engineering, 490–500. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53021-1_50.

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Trettel, Brenda S., and John L. Yeager. "Linking Strategic Planning, Priorities, Resource Allocation, and Assessment." In Increasing Effectiveness of the Community College Financial Model, 81–94. New York: Palgrave Macmillan US, 2011. http://dx.doi.org/10.1057/9780230120006_6.

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Radhakrishnan, A., and K. Saravanan. "Energy Aware Resource Allocation Model for IaaS Optimization." In Studies in Big Data, 51–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73676-1_3.

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Murugesan, G., and C. Chellappan. "Resource Allocation for Grid Applications: An Economy Model." In Lecture Notes in Electrical Engineering, 439–49. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9419-3_34.

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Kruzslicz, Ferenc. "A New Exact Resource Allocation Model with Hard and Soft Resource Constraints." In Operations Research Proceedings 2002, 223–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55537-4_36.

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Atamturktur, S., and G. Stevens. "Validation of Strongly Coupled Models: A Framework for Resource Allocation." In Model Validation and Uncertainty Quantification, Volume 3, 25–32. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04552-8_3.

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Conference papers on the topic "Resource allocation model"

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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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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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Xu, Yifan, Pan Xu, Jianping Pan, and Jun Tao. "A Unified Model for the Two-stage Offline-then-Online Resource Allocation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/581.

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With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation process. The process consists of an offline phase and another sequential online phase, and both phases compete for the same set of resources. In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework. Our model assumes non-uniform and known arrival distributions for online agents in the second online phase, which can be learned from historical data. We propose a parameterized linear programming (LP)-based algorithm, which is shown to be at most a constant factor of 1/4 from the optimal. Experimental results on the real dataset show that our LP-based approaches outperform the LP-agnostic heuristics in terms of robustness and effectiveness.
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YILDIRIM, GUNGOR, and YETKIN TATAR. "Alternative Resource Allocation Model for Dynamic Resource Sharing WSN Systems." In Fifth International Conference on Advances in Computing, Electronics and Communication - ACEC 2017. Institute of Research Engineers and Doctors, 2017. http://dx.doi.org/10.15224/978-1-63248-121-4-04.

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Ge, Honglei, and Nan Liu. "Inequity measures in relief resource allocation model." In 2011 2nd IEEE International Conference on Emergency Management and Management Sciences (ICEMMS). IEEE, 2011. http://dx.doi.org/10.1109/icemms.2011.6015773.

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Blaiech, Khalil, Omar Mounaouar, Omar Cherkaoui, and Ludovic Beliveau. "Runtime Resource Allocation Model over Network Processors." In 2014 IEEE International Conference on Cloud Engineering (IC2E). IEEE, 2014. http://dx.doi.org/10.1109/ic2e.2014.33.

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Chen, Wen-Zhi. "Resource allocation model based on Dijkstra's algorithm." In 2015 International Workshop on Materials, Manufacturing Technology, Electronics and Information Science. WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813109384_0040.

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Haji, Maryam, Luca Agostoni, Paolo Gullo, Houshang Darabi, and Mauro Mancini. "An improved multi-mode resource allocation and project scheduling model." In 2008 IEEE International Conference on Automation Science and Engineering (CASE 2008). IEEE, 2008. http://dx.doi.org/10.1109/coase.2008.4626461.

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He, Fujun, Takehiro Sato, and Eiji Oki. "Optimization Model for Backup Resource Allocation in Middleboxes." In 2018 IEEE 7th International Conference on Cloud Networking (CloudNet). IEEE, 2018. http://dx.doi.org/10.1109/cloudnet.2018.8549318.

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Win, Thu Rein, Tin Tin Yee, and Ei Chaw Htoon. "Optimized Resource Allocation Model in Cloud Computing System." In 2019 International Conference on Advanced Information Technologies (ICAIT). IEEE, 2019. http://dx.doi.org/10.1109/aitc.2019.8920852.

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Reports on the topic "Resource allocation model"

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Castanon, David A., and Jerry M. Wohletz. Model Predictive Control for Dynamic Unreliable Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada409519.

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Labunskaya, A. D., B. A. Mukhin, P. G. Kolyadin, V. A. Kosyrev, E. A. Chepurnaya, and A. L. Shevchenko. The model of optimal allocation of resources of the warring parties on the basis of genetic algorithms (MOPP PS). OFERNIO, March 2020. http://dx.doi.org/10.12731/ofernio.2020.24495.

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Iyer, Ananth V., Steven R. Dunlop, Olga Senicheva, Dutt J. Thakkar, Ruier Yan, Karthikeyan Subramanian, Suraj Vasu, Gokul Siddharthan, Juily Vasandani, and Srijan Saurabh. Improve and Gain Efficiency in Winter Operations. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317312.

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This report analyzes the current service level of winter operations in Indiana and explores opportunities to optimize performance. We analyze data regarding winter operations managed by INDOT and provide specific quantified estimates of opportunities to improve efficiency while also managing costs. For our exploration, we use data provided by INDOT sources, qualitative insights from interviews with INDOT personnel, literature survey data and benchmarking information, salt and supplier data analysis, and simulation. As part of our research, we developed a simulation model to visually represent the impact of alternate management of trucks for snow removal and a dashboard to understand the impact. Our analysis suggests the following: (1) opportunities exist to coordinate salt delivery by suppliers and combine local city salt purchases with INDOT’s purchases to save costs, (2) adjusting routes will reduce deadhead, (3) understanding truck maintenance and truck locations improves performance, and (4) incorporating critical locations into snow route planning will meet service thresholds. These insights provide implementable recommendation initiatives to improve winter operations performance. The simulation tool developed in this project simulates various weather events to draw insights and determine appropriate resource allocations and opportunities for improving operational efficiency. The report thus provides a quantifiable approach to winter operations that can improve the overall service level and efficiency of the process.
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