Готові списки джерел за темами / Resource on Demand

Добірка наукової літератури з теми "Resource on Demand"

Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Resource on Demand"

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Mondal, Sakib A. "Resource allocation problem under single resource assignment." RAIRO - Operations Research 52, no. 2 (April 2018): 371–82. http://dx.doi.org/10.1051/ro/2017035.

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We consider a NP-hard resource allocation problem of allocating a set of resources to meet demands over a time period at the minimum cost. Each resource has a start time, finish time, availability and cost. The objective of the problem is to assign resources to meet the demands so that the overall cost is minimum. It is necessary that only one resource contributes to the demand of a slot. This constraint will be referred to as single resource assignment (SRA) constraint. We would refer to the problem as the S_RA problem. So far, only 16-approximation to this problem is known. In this paper, we propose an algorithm with approximation ratio of 12.
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Kania, Eugene. "Supply and Demand." Mechanical Engineering 128, no. 02 (February 1, 2006): 25–26. http://dx.doi.org/10.1115/1.2006-feb-2.

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This article discusses key aspects of resource management for the successful completion of a project. The article highlights that decision-making is the difficult, but necessary, last step to arrive at a portfolio of projects that do not overload resources and clog the engineering pipeline. The four-step resource management process that a company has implemented helps management visualize and understand the effects of their project decisions. It also helps engineering managers identify resource shortages. The key to implementing this system is to build solid communication processes, get key organizational participation, and have the discipline to keep at it every month. The article also suggests that if a company is to use a software tool to facilitate and enable the process, keep it as simple and effective as the process itself.
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Dungey, Mardi, Renee Fry-McKibbin, and Verity Linehan. "Chinese resource demand and the natural resource supplier." Applied Economics 46, no. 2 (September 26, 2013): 167–78. http://dx.doi.org/10.1080/00036846.2013.835483.

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Ruff, Larry E. "Demand Response: Reality versus “Resource”." Electricity Journal 15, no. 10 (December 2002): 10–23. http://dx.doi.org/10.1016/s1040-6190(02)00401-3.

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Phaneuf, Daniel J. "Heterogeneity in Environmental Demand." Annual Review of Resource Economics 5, no. 1 (June 2013): 227–44. http://dx.doi.org/10.1146/annurev-resource-091912-151841.

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Rahayu, Puspita Puji. "Model Tuntutan Pekerjaan dan Sumber Daya Pekerjaan." JUDICIOUS 2, no. 2 (December 30, 2021): 214–18. http://dx.doi.org/10.37010/jdc.v2i2.603.

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Анотація:
Karakteristik kerja merupakan aspek penting dalam pekerjaan yang dapat memberikan dampak pada kesejahteraan karyawan. Karakteristik kerja dibagi menjadi dua, yaitu job demand dan job resources. JD-R model adalah penyempurnaan dari dua job stress model yang telah dikembangkan sebelumnya, yaitu demands-control model (DCM) (Karasek, 1979) dan effort-reward imbalance model (ERI) (Siegrist,1996). Penelitian ini menggunakan pendekatan kajian literature ini diharapkan mampu dijadikan kajian ataupun informasi yang dapat dijadikan sebagai dasar teoritis penelitian selanjutnya dalam job demand dan job resources model. Job resource dan job demand dapat dibedakan dengan perannya masing-masing. Pada job resource contohnya otonomi dapat membantu karyawan untuk mengatur dan menyelesaikan pekerjaan dengan nyaman dan umpan balik dapat membantu karyawan meningkatkan performa kerja. Kemudian dukungan sosial dari kolega dan mentor dari supervisor dapat meningkatkan motivasi dan dukungan emosional di pekerjaan. Di sisi lain, job demand seperti beban kerja, emosi, dan tuntutan fisik, adalah suatu hal yang memang ada di pekerjaan yang dapat di toleransi dengan peran job resource.
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Kalach, A. V., L. V. Rossikhina, E. B. Govorin, R. B. Golovkin, and P. V. Shumov. "Resource allocation models at resource quantity dependence on demand." IOP Conference Series: Materials Science and Engineering 537 (June 17, 2019): 032003. http://dx.doi.org/10.1088/1757-899x/537/3/032003.

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Lu, Xingguang. "A Human Resource Demand Forecasting Method Based on Improved BP Algorithm." Computational Intelligence and Neuroscience 2022 (March 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/3534840.

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Human resources are the first resource for enterprise development, and a reasonable human resource structure will increase the effectiveness of an enterprise’s human resource input and output. The reality is that even if an enterprise designs a human resource allocation plan in accordance with the corporate strategy, it is impossible for the enterprise to operate in full accordance with the plan during the operation process, so the human resource allocation plan only reflects the law of the enterprise’s human resource needs during the enterprise development process. Giving effective guidance to the specific work of human resources is difficult. It is impossible to carry out effective human resources structure adjustment to adapt to changes in human resources demand due to changes in corporate tactics, business, scale, and other factors, especially when the current domestic human resources market has not yet fully formed. This paper examines the impact of key factors such as the company’s business growth scale and production efficiency improvement on human resource needs with the goal of improving team structure, optimizing staff allocation, controlling labor costs, and improving efficiency and benefits. In this paper, we attempt to develop a human resource demand forecasting model based on business development and economic benefits and guided by intensive human resource development. We analyze and forecast the enterprise’s total human resource employment, personnel structure, and quality structure using this model. In light of this, this paper employs an improved BP neural network to construct a human resource demand forecasting system, resulting in a new quantitative forecasting method for human resource demand forecasting with strong theoretical significance. Simultaneously, the human resource demand forecasting system developed can enable enterprises to carry out personnel demand forecasting from the actual situation, making forecasting more applicable, flexible, and accurate, allowing enterprises to realize their strategies through reasonable human resource planning.
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Shakil, Kashish Ara, Mansaf Alam, and Samiya Khan. "A latency-aware max-min algorithm for resource allocation in cloud." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 671. http://dx.doi.org/10.11591/ijece.v11i1.pp671-685.

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Анотація:
Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns.
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Spitz, Gabriel. "Flexibility in Resource Allocation and the Performance of Time-Sharing Tasks." Proceedings of the Human Factors Society Annual Meeting 32, no. 19 (October 1988): 1466–70. http://dx.doi.org/10.1177/154193128803201934.

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The extent and nature of the ability to control the allocation of mental resources between the components of a dual task was investigated in three separate experiments. Using a variable priority (demand) methodology it was found that subjects could manipulate their performance level, however their ability to meet specific demand levels was limited. Training subjects under single or dual-task conditions using a wide range of task demand significantly improved dual task performance and degree of control over resource allocation as compared to performance following practice under a narrow range of task demands or under single task fixed demand conditions. Single task performance among all groups improved to the same degree. It was concluded that training subjects under a wide range of task demands increases the range of performance levels over which mental resources can be flexibly allocated for those tasks and improves time sharing performance. Implications for the design of training for complex task performance are discussed.
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Дисертації з теми "Resource on Demand"

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Rainwater, Chase E. "Resource constrained assignment problems with flexible customer demand." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024847.

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Miller, Benjamin Israel. "Estimating the Firm’s Demand for Human Resource Management Practices." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/econ_diss/34.

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This dissertation investigates two related aspects of firms’ choice of HRM practices. The first is why some firms expend a great deal of resources on HRM practices for each employee while others spend very little; the second is the extent to which firms’ bundles of HRM practices sort into general discrete employment systems. In order to empirically address these issues, this dissertation uses an economics-based theoretical approach. The key theoretical link to economics is to treat HRM as a separate factor input in the production process, which allows me to derive an HRM input demand function. This function expresses the firm’s per employee expenditures on HRM and their choice of HRM system as a function of prices and internal and external firm characteristics. Ordinary least squares, two-stage least squares and linear quantile analysis are used to empirically estimate the HRM demand function using a unique dataset of several hundred firms collected by the Bureau of National Affairs (BNA). The regression equation is found to be statistically significant, implying firms do have an identifiable demand for HRM practices. Second, there are nine independent variables which are found to be stable determinants of the demand for per employee expenditures on HRM practices. Regarding the existence of discrete employment systems, cluster analysis is used to determine if the sets of HRM practices adopted by these firms sort into identifiable types of HRM systems. The results show that there is a discrete set of four HRM systems; however, the HRM demand function does not predict which system a firm will choose.
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Miller, Benjamin Israel. "Estimating the firm's demand for human resource management practices." unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-11192008-141353/.

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Анотація:
Thesis (Ph. D.)--Georgia State University, 2008.
Title 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).
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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.

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Анотація:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2003.
Includes bibliographical references (leaves 127-128).
by Andrew J. Muench.
S.M.
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5

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.

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Landi, 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.

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Анотація:
2012 - 2013
The 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.
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Hong, Seong-Jong. "Analysis of the Benefits of Resource Flexibility, Considering Different Flexibility Structures." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11185.

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We study the benefits of resource flexibility, considering two different flexibility structures. First, we want to understand the impact of the firm's pricing strategy on its resource investment decision, considering a partially flexible resource. Secondly, we study the benefits of a flexible resource strategic approach, considering a resource flexibility structure that has not been studied in the previous literature. First, we study the capacity investment decision faced by a firm that offers two products/services and that is a price-setter for both products/services. The products offered by the firm are of varying levels (complexities), such that the resources that can be used to produce the higher level product can also be used to produce the lower level one. Although the firm needs to make its capacity investment decision under high demand uncertainty, it can utilize this limited (downward) resource flexibility, in addition to pricing, to more effectively match its supply with demand. Sample applications include a service company, whose technicians are of different capabilities, such that a higher level technician can perform all tasks performed by a lower level technician; a firm that owns a main plant, satisfying both end-product and intermediate-product demand, and a subsidiary, satisfying the intermediate-product demand only. We formulate this decision problem as a two-stage stochastic programming problem with recourse, and characterize the structural properties of the firm's optimal resource investment strategy when resource flexibility and pricing flexibility are considered in the investment decision. We show that the firm's optimal resource investment strategy follows a threshold policy. This structure allows us to understand the impact of coordinated decision-making, when the resource flexibility is taken into account in the investment decision, on the firm's optimal investment strategy, and establish the conditions under which the firm invests in the flexible resource. We also study the impact of demand correlation on the firm's optimal resource investment strategy, and show that it may be optimal for the firm to invest in both flexible and dedicated resources when product demand patterns are perfectly positively correlated. Our results offer managerial principles and insights on the firm's optimal resource investment strategy as well as extend the newsvendor problem with pricing, by allowing for multiple resources (suppliers), multiple products, and resource pooling. Secondly, we study the benefits of a delayed decision making strategy under demand uncertainty, considering a system that satisfies two demand streams with two capacitated and flexible resources. Resource flexibility allows the firm to delay its resource allocation decision to a time when partial information on demands is obtained and demand uncertainty is reduced. We characterize the structure of the firm's optimal delayed resource allocation strategy. This characterization allows us to study how the revenue benefits of the delayed resource allocation strategy depend on demand and capacity parameters, and the length of the selling season. Our study shows that the revenue benefits of this strategy can be significant, especially when demand rates of the different types are close, while resource capacities are much different. Based on our analysis, we provide guidelines on the utilization of such strategies. Finally, we incorporate the uncertainty in demand parameters into our models and study the effectiveness of several delayed capacity allocation mechanisms that utilize the resource flexibility. In particular, we consider that demand forecasts are uncertain at the start of the selling season and are updated using a Bayesian framework as early demand figures are observed. We propose several heuristic capacity allocation policies that are easy to implement as well as a heuristic procedure that relies on a stochastic dynamic programming formulation and perform a numerical study. Our study determines the conditions under which each policy is effective.
Ph. D.
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8

Acharya, Gayatri. "Hydrological-economic linkages in water resource management." Thesis, University of York, 1998. http://etheses.whiterose.ac.uk/10809/.

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Juana, 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/.

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Xu, 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.

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Книги з теми "Resource on Demand"

1

Agency, Environment, ed. Resource demand management techniques for sustainable development. Bristol: Environment Agency, 1998.

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2

Khadr, Ali M. Nonrenewable resource allocation under intertemporally dependent demand. Oxford: Oxford Institute for Energy Studies, 1987.

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3

American 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.

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4

Rutledge, Patrice-Anne. WordPress on demand. Indianapolis, IN: Que, 2013.

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5

Systems, Adobe, ed. Adobe InDesign CS4: On demand. Indianapolis, Ind: Que Pub., 2008.

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6

Agency, Environment, ed. Resource demand management for sustainable development March 1998. Bristol: Environment Agency, 1998.

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7

Adobe Muse on demand. Indianapolis, IN: Que Pub., 2012.

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8

Hu, Zhaoguang, Xinyang Han, and 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.

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9

Agency, Environment. Resource demand management techniques for sustainable development: March 1998. Bristol: Environment Agency, 1998.

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Hu, Zhaoguang. Integrated Resource Strategic Planning and Power Demand-Side Management. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Частини книг з теми "Resource on Demand"

1

Kounev, Samuel, Klaus-Dieter Lange, and Jóakim von Kistowski. "Resource Demand Estimation." In Systems Benchmarking, 365–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41705-5_17.

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Derks, R. "Demand Management." In Integrated Electricity Resource Planning, 475–84. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1054-9_26.

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Chandrakanth, M. G. "Demand Side Economics of Micro-irrigation." In Water Resource Economics, 125–38. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2479-2_9.

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Lloyd Owen, David. "Demand Management and Resource Recovery." In Global Water Funding, 317–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49454-4_9.

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Rajegopal, Shan, Philip McGuin, and James Waller. "Map resource capacity and demand." In Project Portfolio Management, 175–84. London: Palgrave Macmillan UK, 2007. http://dx.doi.org/10.1057/9780230206496_9.

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Frisch, Jean-Romain. "General Table of Demand/Resource Stresses." In Future Stresses for Energy Resources, 29–32. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4209-7_3.

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Rosenfeld, Arthur H. "Policy: Integrated Resource Planning to Optimize Energy Services." In Global Energy Demand in Transition, 251. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-1048-6_23.

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Siddiqui, Mumtaz, and Thomas Fahringer. "Semantics-Based Activity Synthesis: Improving On-Demand Provisioning and Planning." In Grid Resource Management, 179–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11579-0_8.

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Grigg, Neil S. "Demand for Water, Water Services, and Ecosystem Services." In Integrated Water Resource Management, 207–25. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/978-1-137-57615-6_11.

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Ryoo, Jeong-dong, and Shivendra S. Panwar. "Resource Optimization in Video-On-Demand Networks." In Multimedia Communications and Video Coding, 125–31. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0403-6_16.

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Тези доповідей конференцій з теми "Resource on Demand"

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Kowli, Anupama S., and George Gross. "Incorporation of demand response resources in resource investment analysis." In 2009 IEEE Bucharest PowerTech (POWERTECH). IEEE, 2009. http://dx.doi.org/10.1109/ptc.2009.5282141.

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Grohmann, Johannes, Nikolas Herbst, Simon Spinner, and Samuel Kounev. "Self-Tuning Resource Demand Estimation." In 2017 IEEE International Conference on Autonomic Computing (ICAC). IEEE, 2017. http://dx.doi.org/10.1109/icac.2017.19.

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Davis, Allen L., and Robert C. Brawn. "General Purpose Demand Allocator (DALLOC)." In 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.

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Ebneyousef, Sepideh, and Saeed Ghazanfari-Rad. "Cloud Resource Demand Prediction to Achieve Efficient Resource Provisioning." In 2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE, 2022. http://dx.doi.org/10.1109/icspis56952.2022.10043900.

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Zhang, Ying, Gang Huang, Xuanzhe Liu, and Hong Mei. "Integrating Resource Consumption and Allocation for Infrastructure Resources on-Demand." In 2010 IEEE International Conference on Cloud Computing (CLOUD). IEEE, 2010. http://dx.doi.org/10.1109/cloud.2010.11.

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"Traffic Engineering, Resource Allocation, and QoS." In 2006 IEEE First International Workshop on Bandwidth on Demand. IEEE, 2006. http://dx.doi.org/10.1109/bod.2006.320795.

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Silva, Thiciane Suely Couto, Fabio Gomes Rocha, and Rodrigo Pereira dos Santos. "Resource Demand Management in Java Ecosystem." In SBSI'19: XV Brazilian Symposium on Information Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3330204.3330212.

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Andersen, Johannes, and Roger Powell. "DMA Structured State-Estimation for Demand Monitoring." In 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.

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Shah, Amip, Ratnesh Sharma, Cullen Bash, Manish Marwah, Tom Christian, Chandrakant Patel, and Kiara Corrigan. "IT-Enabled Resource Management." In ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90083.

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Анотація:
As information technology becomes more widespread, access to real-time data regarding the supply and demand for resources is becoming central to the management of physical infrastructures. Current information systems for resource management are mostly static, so an underlying challenge in the management of resources is how to overcome time-lags in information transfer for efficiently measuring demand and then adequately provisioning a supply of resources to meet that demand. Particularly in the context of ‘smart cities’, a new framework is required for information management in the context of resource management. In this paper, we propose an information architecture consisting of life-cycle design, scalable and flexible resource microgrids, pervasive sensing, data analytics and visualization, and policy-based autonomous control that directly links real-time demand patterns to the state of various supply-side management systems. This integrated supply-demand information system is used to obtain a more holistic view of information flow through the infrastructure. We show that such an architecture is more efficient at information transfer than existing systems, and posit that this increased efficiency in turn enables improved management of resources within the infrastructure because of the availability of a higher number of degrees of freedom in the management of the infrastructure. The paper concludes by illustrating the applicability of the proposed architecture for a case study of two large-scale infrastructures.
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Hariharan, Smitha, and Venkat Allada. "Uncertain Demand Driven Resource Platform Design for a Service Center." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81191.

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Service centers can be viewed as facilities wherein service calls from customers relating to various service families cascade into work packages involving a set of service tasks. A service family is defined as a set of service type variants that have similar or common service tasks and hence may use a common set of resources (called the resource platform) over a given time horizon. In this paper, we present a methodology for determining cost-effective and robust resource platform configuration(s) for a given set of service families offered by a service center. The robust resource platform would be able to handle demand fluctuations from various service types within reasonable limits over a given planning horizon. Several successful studies have been reported in the manufacturing domain on successful application of product platforms to generate customizable product variants with cost advantages. In this paper, we extend the product platform concept to the service domain. The critical parts of the proposed Service Resource Platforming System (SRPS) methodology are: (1) generate rough cut resource selection using dynamic programming and create a resource schedule using linear programming; (2) generate final resource selection using uncertainty linear programming model proposed by Ben-Tal et al. (2003); and (3) construct resource platform using resource sub-sequence clustering concept. The objective for the rough cut and final resource selections is the maximization of the difference between the benefits and costs associated with in-house service processing and outsourcing. The proposed SRPS methodology is applied to an industry motivated problem.
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Звіти організацій з теми "Resource on Demand"

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Kalsi, Karanjit, Tess L. Williams, Laurentiu D. Marinovici, Marcelo A. Elizondo, and Jianming Lian. Loads as a Resource: Frequency Responsive Demand. Office of Scientific and Technical Information (OSTI), November 2015. http://dx.doi.org/10.2172/1375378.

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Kalsi, Karanjit, Jacob Hansen, Jason C. Fuller, Laurentiu D. Marinovici, Marcelo A. Elizondo, Tess L. Williams, Jianming Lian, and Yannan Sun. Loads as a Resource: Frequency Responsive Demand. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1375379.

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Kalsi, Karanjit, Jianming Lian, Laurentiu D. Marinovici, Marcelo A. Elizondo, Wei Zhang, and Christian Moya. Loads as a Resource: Frequency Responsive Demand. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1375380.

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Eto, Joseph H., Nancy Jo Lewis, David Watson, Sila Kiliccote, David Auslander, Igor Paprotny, and Yuri Makarov. Demand Response as a System Reliability Resource. Office of Scientific and Technical Information (OSTI), December 2012. http://dx.doi.org/10.2172/1172114.

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Swiler, Laura, Teresa Portone, and Walter Beyeler. Uncertainty analysis of Resource Demand Model for Covid-19. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1630395.

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Kang, Shian C. Learning to Predict Demand in a Transport-Resource Sharing Task. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ad1009057.

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Satchwell, Andrew, and Ryan Hledik. Analytical Frameworks to Incorporate Demand Response in Long-term Resource Planning. Office of Scientific and Technical Information (OSTI), December 2013. http://dx.doi.org/10.2172/1164372.

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Frazier, Christopher, Daniel Krofcheck, Jared Gearhart, and Walter Beyeler. Integrated Resource Supply-Demand-Routing Model for the COVID-19 Crisis. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1763531.

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Raab, J., and M. Schweitzer. Public involvement in integrated resource planning: A study of demand-side management collaboratives. Office of Scientific and Technical Information (OSTI), February 1992. http://dx.doi.org/10.2172/10146196.

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Raab, J., and M. Schweitzer. Public involvement in integrated resource planning: A study of demand-side management collaboratives. Office of Scientific and Technical Information (OSTI), February 1992. http://dx.doi.org/10.2172/5241653.

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