Literatura académica sobre el tema "Resource on Demand"
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Artículos de revistas sobre el tema "Resource on Demand"
Mondal, Sakib A. "Resource allocation problem under single resource assignment". RAIRO - Operations Research 52, n.º 2 (abril de 2018): 371–82. http://dx.doi.org/10.1051/ro/2017035.
Texto completoKania, Eugene. "Supply and Demand". Mechanical Engineering 128, n.º 02 (1 de febrero de 2006): 25–26. http://dx.doi.org/10.1115/1.2006-feb-2.
Texto completoDungey, Mardi, Renee Fry-McKibbin y Verity Linehan. "Chinese resource demand and the natural resource supplier". Applied Economics 46, n.º 2 (26 de septiembre de 2013): 167–78. http://dx.doi.org/10.1080/00036846.2013.835483.
Texto completoRuff, Larry E. "Demand Response: Reality versus “Resource”". Electricity Journal 15, n.º 10 (diciembre de 2002): 10–23. http://dx.doi.org/10.1016/s1040-6190(02)00401-3.
Texto completoPhaneuf, Daniel J. "Heterogeneity in Environmental Demand". Annual Review of Resource Economics 5, n.º 1 (junio de 2013): 227–44. http://dx.doi.org/10.1146/annurev-resource-091912-151841.
Texto completoRahayu, Puspita Puji. "Model Tuntutan Pekerjaan dan Sumber Daya Pekerjaan". JUDICIOUS 2, n.º 2 (30 de diciembre de 2021): 214–18. http://dx.doi.org/10.37010/jdc.v2i2.603.
Texto completoKalach, A. V., L. V. Rossikhina, E. B. Govorin, R. B. Golovkin y P. V. Shumov. "Resource allocation models at resource quantity dependence on demand". IOP Conference Series: Materials Science and Engineering 537 (17 de junio de 2019): 032003. http://dx.doi.org/10.1088/1757-899x/537/3/032003.
Texto completoLu, Xingguang. "A Human Resource Demand Forecasting Method Based on Improved BP Algorithm". Computational Intelligence and Neuroscience 2022 (29 de marzo de 2022): 1–9. http://dx.doi.org/10.1155/2022/3534840.
Texto completoShakil, Kashish Ara, Mansaf Alam y Samiya Khan. "A latency-aware max-min algorithm for resource allocation in cloud". International Journal of Electrical and Computer Engineering (IJECE) 11, n.º 1 (1 de febrero de 2021): 671. http://dx.doi.org/10.11591/ijece.v11i1.pp671-685.
Texto completoSpitz, Gabriel. "Flexibility in Resource Allocation and the Performance of Time-Sharing Tasks". Proceedings of the Human Factors Society Annual Meeting 32, n.º 19 (octubre de 1988): 1466–70. http://dx.doi.org/10.1177/154193128803201934.
Texto completoTesis sobre el tema "Resource on Demand"
Rainwater, Chase E. "Resource constrained assignment problems with flexible customer demand". [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024847.
Texto completoMiller, Benjamin Israel. "Estimating the Firm’s Demand for Human Resource Management Practices". Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/econ_diss/34.
Texto completoMiller, Benjamin Israel. "Estimating the firm's demand for human resource management practices". unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-11192008-141353/.
Texto completoTitle from file title page. Bruce E. Kaufman, committee chair; Barry T. Hirsch, Klara S. Peter, Hyeon J. Park, committee members. Description based on contents viewed Sept. 22, 2009. Includes bibliographical references (p. 159-165).
Muench, Andrew J. (Andrew James) 1970. "Redefining the aftermarket demand forecasting process using enterprise resource planning". Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/89924.
Texto completoIncludes bibliographical references (leaves 127-128).
by Andrew J. Muench.
S.M.
Cantwell, Marilyn L. "Resource and demand effects on elderly functionality and residential mobility /". The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487671108307051.
Texto completoLandi, Marco. "Bidirectional Metering Advancements and Applications to Demand Response Resource Management". Doctoral thesis, Universita degli studi di Salerno, 2014. http://hdl.handle.net/10556/1448.
Texto completoThe power grid is an electric system capable of performing electricity generation, transmission, distribution and control. Nowadays it has been subjected to a deep transformation, which will reshape it completely. In fact, growing electricity demand and consequent increase of power losses in transmission and distribution grids, the increase in prices of fossil fuels and the diffusion of renewable resources, the need for a more effective and efficient grid management and use of energy, the availability of new technologies to be integrated into the grid, they all push for a modernization of the power grid. Integrating technology and approaches typical of different areas (i.e. power systems, ICT, measurements, automatic controls), the aim is to build a grid capable of engulfing all types of sources and loads, capable of efficiently deliver electricity automatically adapting to changes in generation and demand, ultimately empowering customers with new and advanced services. This paradigm is known as Smart Grid. In this context, the role of measurement theories, techniques and instrumentation is a fundamental one: the automatic management and control of the grid is a completely unfeasible goal without a timely and reliable picture of the state of the electric network. For this reason, a metering infrastructure (including sensors, data acquisition and process system and communication devices and protocols) is needed to the development of a smarter grid. Among the features of such an infrastructure are the ability to execute accurate and real‐time measurements, the evaluation of power supply quality and the collection of measured data and its communication to the system operator. Moreover, a so defined architecture can be extended to all kinds of energy consumption, not only the electricity ones. With the development of an open energy market, an independent entity could be put in charge of the execution of measurements on the grid and the management of the metering infrastructure: in this way, “certified” measurements will be guaranteed, ensuring an equal treatment of all grid and market users. In the thesis, different aspects relative to measurement applications in the context of a Smart Grid have been covered. A smart meter prototype to be installed in customers’ premises has been realized: it is an electricity meter also capable of interfacing with gas and hot water meters, acting as a hub for monitoring the overall energy consumption. The realized prototype is based on an ARM Cortex M3 microcontroller architecture (precisely, the ST STM32F103), which guarantees a good compromise among cost, performance and availability of internal peripherals. Advanced measurement algorithms to ensure accurate bidirectional measurements even in non‐sinusoidal conditions have been implemented in the meter software. Apart from voltage and current transducer, the meter embeds also a proportional and three binary actuators: through them is possible to intervene directly on the monitored network, allowing for load management policies implementation. Naturally the smart meter is only functional if being a part of a metering and communication infrastructure: this allows not only the collection of measured data and its transmission to a Management Unit, which can so build an image of the state of the network, but also to provide users with relevant information regarding their consumptions and to realize load management policies. In fact, the realized prototype architecture manages load curtailments in Demand Response programs relying on the price of energy and on a cost threshold that can be set up by the user. Using a web interface, the user can verify his own energy consumptions, manage contracts with the utility companies and eventually his participation in DR programs, and also manually intervene on his loads. In the thesis storage systems, of fundamental importance in a Smart Grid Context for the chance they offer of decoupling generation and consumption, have been studied. They represent a key driver towards an effective and more efficient use of renewable energy sources and can provide the grid with additional services (such as down and up regulation). In this context, the focus has been on li‐ion batteries: measurement techniques for the estimation of their state of life have been realized. Since batteries are becoming increasingly important in grid operation and management, knowing the degradation they are subjected has a relevant impact not only on grid resource planning (i.e. substitution of worn off devices and its scheduling) but also on the reliability in the services based on batteries. The implemented techniques, based on Fuzzy logic and neural networks, allow to estimate the State of Life of li‐ion batteries even for variation of the external factors influencing battery life (temperature, discharge current, DoD). Among the requisites a Smart Grid architecture has, is the integration into the grid of Electric Vehicles. EVs include both All Electric Vehicles and Plug‐in Hybrid Electric Vehicles and have been considered by governments and industry as sustainable means of transportation and, therefore, have been the object of intensive study and development in recent years. Their number is forecasted to increase considerably in the next future, with alleged consequences on the power grid: while charging, they represent a consistent additional load that, if not properly managed, could be unbearable for the grid. Nonetheless, EVs can be also a resource, providing their locally stored energy to the power grid, thus realizing useful ancillary services. The paradigm just described is usually referred to as Vehicle‐to‐Grid (V2G). Being the storage systems onboard the EVs based on li‐ion batteries, starting from the measurement and estimation techniques precedently introduced, aim of the thesis work will be the realization of a management systems for EV fleets for the provision of V2G services. Assuming the system model in which the aggregator not only manages such services, but can also be the owner of the batteries, the goal is to manage the fleets so to maximize battery life, and guarantee equal treatment to all the users participating in the V2G program. [edited by author]
XII n.s.
Hong, Seong-Jong. "Analysis of the Benefits of Resource Flexibility, Considering Different Flexibility Structures". Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11185.
Texto completoPh. D.
Acharya, Gayatri. "Hydrological-economic linkages in water resource management". Thesis, University of York, 1998. http://etheses.whiterose.ac.uk/10809/.
Texto completoJuana, James Sharka. "Efficiency and equity considerations in modeling inter-sectoral water demand in South Africa". Pretoria : [S.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-06062008-140425/.
Texto completoXu, Dongsheng. "Resource allocation among multiple stochastic demand classes in express delivery chains /". View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?IELM%202007%20XU.
Texto completoLibros sobre el tema "Resource on Demand"
Agency, Environment, ed. Resource demand management techniques for sustainable development. Bristol: Environment Agency, 1998.
Buscar texto completoKhadr, Ali M. Nonrenewable resource allocation under intertemporally dependent demand. Oxford: Oxford Institute for Energy Studies, 1987.
Buscar texto completoAmerican Medical Association. Physician Manpower Clearinghouse., ed. Physician manpower: A resource guide. [Chicago, IL]: Physician Manpower Clearinghouse, Center for Health Policy Research, American Medical Association, 1987.
Buscar texto completoRutledge, Patrice-Anne. WordPress on demand. Indianapolis, IN: Que, 2013.
Buscar texto completoSystems, Adobe, ed. Adobe InDesign CS4: On demand. Indianapolis, Ind: Que Pub., 2008.
Buscar texto completoAgency, Environment, ed. Resource demand management for sustainable development March 1998. Bristol: Environment Agency, 1998.
Buscar texto completoAdobe Muse on demand. Indianapolis, IN: Que Pub., 2012.
Buscar texto completoHu, Zhaoguang, Xinyang Han y Quan Wen. Integrated Resource Strategic Planning and Power Demand-Side Management. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37084-7.
Texto completoAgency, Environment. Resource demand management techniques for sustainable development: March 1998. Bristol: Environment Agency, 1998.
Buscar texto completoHu, Zhaoguang. Integrated Resource Strategic Planning and Power Demand-Side Management. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Buscar texto completoCapítulos de libros sobre el tema "Resource on Demand"
Kounev, Samuel, Klaus-Dieter Lange y Jóakim von Kistowski. "Resource Demand Estimation". En Systems Benchmarking, 365–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41705-5_17.
Texto completoDerks, R. "Demand Management". En Integrated Electricity Resource Planning, 475–84. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1054-9_26.
Texto completoChandrakanth, M. G. "Demand Side Economics of Micro-irrigation". En Water Resource Economics, 125–38. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2479-2_9.
Texto completoLloyd Owen, David. "Demand Management and Resource Recovery". En Global Water Funding, 317–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49454-4_9.
Texto completoRajegopal, Shan, Philip McGuin y James Waller. "Map resource capacity and demand". En Project Portfolio Management, 175–84. London: Palgrave Macmillan UK, 2007. http://dx.doi.org/10.1057/9780230206496_9.
Texto completoFrisch, Jean-Romain. "General Table of Demand/Resource Stresses". En Future Stresses for Energy Resources, 29–32. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4209-7_3.
Texto completoRosenfeld, Arthur H. "Policy: Integrated Resource Planning to Optimize Energy Services". En Global Energy Demand in Transition, 251. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-1048-6_23.
Texto completoSiddiqui, Mumtaz y Thomas Fahringer. "Semantics-Based Activity Synthesis: Improving On-Demand Provisioning and Planning". En Grid Resource Management, 179–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11579-0_8.
Texto completoGrigg, Neil S. "Demand for Water, Water Services, and Ecosystem Services". En Integrated Water Resource Management, 207–25. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/978-1-137-57615-6_11.
Texto completoRyoo, Jeong-dong y Shivendra S. Panwar. "Resource Optimization in Video-On-Demand Networks". En Multimedia Communications and Video Coding, 125–31. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0403-6_16.
Texto completoActas de conferencias sobre el tema "Resource on Demand"
Kowli, Anupama S. y George Gross. "Incorporation of demand response resources in resource investment analysis". En 2009 IEEE Bucharest PowerTech (POWERTECH). IEEE, 2009. http://dx.doi.org/10.1109/ptc.2009.5282141.
Texto completoGrohmann, Johannes, Nikolas Herbst, Simon Spinner y Samuel Kounev. "Self-Tuning Resource Demand Estimation". En 2017 IEEE International Conference on Autonomic Computing (ICAC). IEEE, 2017. http://dx.doi.org/10.1109/icac.2017.19.
Texto completoDavis, Allen L. y Robert C. Brawn. "General Purpose Demand Allocator (DALLOC)". En Joint Conference on Water Resource Engineering and Water Resources Planning and Management 2000. Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40517(2000)190.
Texto completoEbneyousef, Sepideh y Saeed Ghazanfari-Rad. "Cloud Resource Demand Prediction to Achieve Efficient Resource Provisioning". En 2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE, 2022. http://dx.doi.org/10.1109/icspis56952.2022.10043900.
Texto completoZhang, Ying, Gang Huang, Xuanzhe Liu y Hong Mei. "Integrating Resource Consumption and Allocation for Infrastructure Resources on-Demand". En 2010 IEEE International Conference on Cloud Computing (CLOUD). IEEE, 2010. http://dx.doi.org/10.1109/cloud.2010.11.
Texto completo"Traffic Engineering, Resource Allocation, and QoS". En 2006 IEEE First International Workshop on Bandwidth on Demand. IEEE, 2006. http://dx.doi.org/10.1109/bod.2006.320795.
Texto completoSilva, Thiciane Suely Couto, Fabio Gomes Rocha y Rodrigo Pereira dos Santos. "Resource Demand Management in Java Ecosystem". En SBSI'19: XV Brazilian Symposium on Information Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3330204.3330212.
Texto completoAndersen, Johannes y Roger Powell. "DMA Structured State-Estimation for Demand Monitoring". En Joint Conference on Water Resource Engineering and Water Resources Planning and Management 2000. Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40517(2000)214.
Texto completoShah, Amip, Ratnesh Sharma, Cullen Bash, Manish Marwah, Tom Christian, Chandrakant Patel y Kiara Corrigan. "IT-Enabled Resource Management". En ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90083.
Texto completoHariharan, Smitha y Venkat Allada. "Uncertain Demand Driven Resource Platform Design for a Service Center". En ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81191.
Texto completoInformes sobre el tema "Resource on Demand"
Kalsi, Karanjit, Tess L. Williams, Laurentiu D. Marinovici, Marcelo A. Elizondo y Jianming Lian. Loads as a Resource: Frequency Responsive Demand. Office of Scientific and Technical Information (OSTI), noviembre de 2015. http://dx.doi.org/10.2172/1375378.
Texto completoKalsi, Karanjit, Jacob Hansen, Jason C. Fuller, Laurentiu D. Marinovici, Marcelo A. Elizondo, Tess L. Williams, Jianming Lian y Yannan Sun. Loads as a Resource: Frequency Responsive Demand. Office of Scientific and Technical Information (OSTI), diciembre de 2015. http://dx.doi.org/10.2172/1375379.
Texto completoKalsi, Karanjit, Jianming Lian, Laurentiu D. Marinovici, Marcelo A. Elizondo, Wei Zhang y Christian Moya. Loads as a Resource: Frequency Responsive Demand. Office of Scientific and Technical Information (OSTI), octubre de 2014. http://dx.doi.org/10.2172/1375380.
Texto completoEto, Joseph H., Nancy Jo Lewis, David Watson, Sila Kiliccote, David Auslander, Igor Paprotny y Yuri Makarov. Demand Response as a System Reliability Resource. Office of Scientific and Technical Information (OSTI), diciembre de 2012. http://dx.doi.org/10.2172/1172114.
Texto completoSwiler, Laura, Teresa Portone y Walter Beyeler. Uncertainty analysis of Resource Demand Model for Covid-19. Office of Scientific and Technical Information (OSTI), mayo de 2020. http://dx.doi.org/10.2172/1630395.
Texto completoKang, Shian C. Learning to Predict Demand in a Transport-Resource Sharing Task. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2015. http://dx.doi.org/10.21236/ad1009057.
Texto completoSatchwell, Andrew y Ryan Hledik. Analytical Frameworks to Incorporate Demand Response in Long-term Resource Planning. Office of Scientific and Technical Information (OSTI), diciembre de 2013. http://dx.doi.org/10.2172/1164372.
Texto completoFrazier, Christopher, Daniel Krofcheck, Jared Gearhart y Walter Beyeler. Integrated Resource Supply-Demand-Routing Model for the COVID-19 Crisis. Office of Scientific and Technical Information (OSTI), mayo de 2020. http://dx.doi.org/10.2172/1763531.
Texto completoRaab, J. y M. Schweitzer. Public involvement in integrated resource planning: A study of demand-side management collaboratives. Office of Scientific and Technical Information (OSTI), febrero de 1992. http://dx.doi.org/10.2172/10146196.
Texto completoRaab, J. y M. Schweitzer. Public involvement in integrated resource planning: A study of demand-side management collaboratives. Office of Scientific and Technical Information (OSTI), febrero de 1992. http://dx.doi.org/10.2172/5241653.
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