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Journal articles on the topic 'Resource on Demand'

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Author: Grafiati

Published: 14 March 2023

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1

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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2

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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3

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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4

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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5

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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6

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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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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7

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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8

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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9

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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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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10

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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11

Walley, Paul, and Anna Jennison-Phillips. "A Study of Non-Urgent Demand to Identify Opportunities for Demand Reduction." Policing: A Journal of Policy and Practice 14, no. 2 (June 25, 2018): 542–54. http://dx.doi.org/10.1093/police/pay034.

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Abstract Police forces often struggle to meet all demands placed upon them because of budgetary and other resource constraints. UK police forces are now embarking on a series of initiatives to find ways of managing demand so that there is a closer match between the resource requirements and those available. This article presents an in-depth analysis of a sample of demand and response to non-urgent incidents from one UK police region, so that there is a better understanding of how demand can be either reduced or more efficiently met. The concept of ‘failure demand’ is introduced as a means of identifying ways to reduce resource consumption.

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12

François, B., B. Hingray, F. Hendrickx, and J. D. Creutin. "Storage water value as a signature of the climatological balance between resource and uses." Hydrology and Earth System Sciences Discussions 10, no. 7 (July 10, 2013): 8993–9025. http://dx.doi.org/10.5194/hessd-10-8993-2013.

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Abstract. Water is stored in reservoirs to adapt in time the availability of the water resource to the various water demands like hydropower production, irrigation or ecological constraints. Deterministic dynamic programming retrospectively identifies optimal reservoir operations that could have been achieved to balance resource and demand during a given time period in the ideal configuration where future inflows and demand are perfectly known. A by-product of dynamic programming is the estimation of the storage water value (SWV) which is the marginal value of the future benefits potentially obtained from an additional unit of water volume stored in a reservoir and which determines the optimal storage strategy. The SWV depends on the reservoir level and shows seasonal as well as inter-annual variations. This paper uses the SWV as an index of the adequacy between water resources and water demands for a simplified water resource system in a mountainous region in France. It characterizes how and why the adequacy and optimal strategy could change for this system if the climate and/or demand change. Changes in mean regional temperature (increase) and/or precipitation (decrease) are analyzed. The influence of the nature of water demand on the SWV is also described (energy production or minimum lake level maintenance). In the studied case, the adequacy between water resources and demand either improves or degrades depending on the considered future scenario. In all scenarios, the seasonality of SWV changes with for example earlier water storage is to efficiently satisfy increasing summer water demand.

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13

Hargreaves, John. "Resource Allocation: Optimisation for Tomorrow's Demand." Logistics Information Management 3, no. 3 (March 1990): 43–50. http://dx.doi.org/10.1108/eb007513.

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14

Spinner, Simon, Giuliano Casale, Fabian Brosig, and Samuel Kounev. "Evaluating approaches to resource demand estimation." Performance Evaluation 92 (October 2015): 51–71. http://dx.doi.org/10.1016/j.peva.2015.07.005.

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15

Hargreaves, John. "Resource Allocation: Optimisation for Tomorrow’s Demand." International Journal of Physical Distribution & Materials Management 19, no. 5 (May 1989): 18–25. http://dx.doi.org/10.1108/eum0000000000320.

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16

Smith, Alexander M., and Marilyn A. Brown. "Demand response: A carbon-neutral resource?" Energy 85 (June 2015): 10–22. http://dx.doi.org/10.1016/j.energy.2015.02.067.

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17

Roberts, Ivan, and Anthony Rush. "Understanding China's demand for resource imports." China Economic Review 23, no. 3 (September 2012): 566–79. http://dx.doi.org/10.1016/j.chieco.2011.05.004.

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18

Fu, Rao. "Application of Human Resource Demand Forecasting in Human Resource Planning." CREATIVE ECONOMY 5, no. 3 (2021): 70–78. http://dx.doi.org/10.47297/wspcewsp2516-251904.20210503.

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19

Jonnagadla, Manasa. "Minimizing the Cost for Resource Allocation from Multiple Cloud Providers." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1611–18. http://dx.doi.org/10.22214/ijraset.2021.39058.

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Abstract: Cloud computing provides streamlined tools for exceptional business efficiency. Cloud service providers typically offer two types of plans: reserved and on-demand. Restricted policies provide low-cost long-term contracting, while order contracts are expensive and ready for short periods. Cloud resources must be delivered wisely to meet current customer demands. Many current works rely on low-cost resource-reserved strategies, which may be under- or over-provisioning. Resource allocation has become a difficult issue due to unfairness causing high availability costs and cloud demand variability. That article suggests a hybrid approach to allocating cloud services to complex customer orders. The strategy was built in two stages: accommodation stages and a flexible structure. By treating each step as an optimization problem, we can reduce the overall implementation cost while maintaining service quality. Due to the uncertain nature of cloud requests, we set up a stochastic Optimization-based approach. Our technique is used to assign individual cloud resources and the results show its effectiveness. Keywords: Cloud computing, Resource allocation, Demand

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20

Chung, Wu-Chun, Tsung-Lin Wu, Yi-Hsuan Lee, Kuo-Chan Huang, Hung-Chang Hsiao, and Kuan-Chou Lai. "Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds." Applied Sciences 11, no. 1 (December 25, 2020): 149. http://dx.doi.org/10.3390/app11010149.

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Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

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21

Zheng, Jiafeng, and Ruijun Ma. "Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network." Computational Intelligence and Neuroscience 2021 (June 21, 2021): 1–10. http://dx.doi.org/10.1155/2021/6596548.

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Human resource planning is the prerequisite of human resource management, and the basic work of human resource planning is to predict human resource demand. Scientific and reasonable human resource demand forecasting results can provide important data support for enterprise human resource planning and strategic decision-making so that human resources management can play a better role in the realization of corporate goals. Because human resource demand is affected by many factors, there is a high degree of nonlinearity and uncertainty between each factor and personnel demand, as well as the incompleteness and inaccuracy of corporate human resource data. In this paper, the self-organizing feature mapping (SOM) artificial neural network prediction model is selected as the prediction model, and the input and output process of sample data is converted into the optimal solution process of the nonlinear function. In the application of the model, the human resource demand prediction index system is used as the input of the SOM neural network and the total number of employees in the enterprise is used as the output so that the problem of nonlinear fitting between human resource demand-influencing factors and human resource demand can be solved. Finally, through the empirical analysis of the enterprise, the model forecasting process is explained and the human resource demand forecast is realized.

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22

Zhu, Zhengfa, Jun Peng, Zhuofu Zhou, Xiaoyong Zhang, and Zhiwu Huang. "PSO-SVR-Based Resource Demand Prediction in Cloud Computing." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 2 (March 18, 2016): 324–31. http://dx.doi.org/10.20965/jaciii.2016.p0324.

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The essential of cloud computing is to offer elastic resources (such as CPU, memory, storage, and more) allocation to cloud customers on demand, and the resources are allocated dynamically in a pay-as-you-go fashion. In order to achieve this goal automatically while guaranteeing the performance of the application deployed in the cloud, a proactive resource scaling strategy is necessary for cloud providers. In this paper, we present an optimal resource usage prediction approach based on Support Vector Regression (SVR) that predicts resource demands from users in the near future. In order to improve the forecasting accuracy, Particle Swarm Optimization (PSO) is integrated in the model selection process for SVR to optimize the parameters of the model. Experiment results show that the prediction model achieves high accuracy and outperforms traditional SVR and Linear Regression (LR).

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23

Dr B Raghu, Dr V Khanaa, Niraja Jain,. "Probabilistic Model for Resource Demand Prediction in Cloud." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 5, 2021): 1766–71. http://dx.doi.org/10.17762/turcomat.v12i6.3908.

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Dynamic cloud infrastructure provisioning is possible with the virtualization technology. Cost, agility and time to market are the key elements of the cloud services. Virtualization is the software layer responsible for interaction with multiple servers, bringing entire IT resources together and provide standardized Virtual compute centers that drives the entire infrastructure. The increased pooling of shared resources helps in improving self-provisioning and automation of service delivery. Probabilistic model proposed in this article is based on the hypothesis that the accurate resource demand predictions can benefit in improving the virtualization layer efficiency. The probabilistic method, uses the laws of combinatorics. The probability space gives an idea about both the partial certainty and randomness of the variable. The method is popular in theoretical computer science. The probabilistic models provide the predictions considering the randomness of the variables. In the cloud environment there are multiple factors dynamically affecting the resource demand needs. The resource demand has a certain degree of certainty but the randomness of requirements. This further leads to decrease in risk related to leveraging cloud services. It accelerates development and implementation of cloud services that overall improves the services pertaining to SLA.

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24

Mamdouh, Maged, Mostafa Ezzat, and Hesham Hefny. "Optimized Planning of Resources Demand Curve in Ground Handling based on Machine Learning Prediction." International Journal of Intelligent Systems and Applications 13, no. 1 (February 8, 2021): 1–16. http://dx.doi.org/10.5815/ijisa.2021.01.01.

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Determining the resource requirements at airports especially in-ground services companies is essential to successful planning in the future, which is represented in the resources demand curve according to the future flight schedule, through which staff schedules are created at the airport to cover the workload with ensuring the highest possible quality service provided. Given in the presence of variety service level agreements used on flight service vary according to many flight features, the resources assumption method makes planning difficult. For instance, flight position is not included in future flight schedule but it's efficacious in the identification of flight resources. In this regard, based on machine learning, we propose a model for building a resource demand curve for future flight schedules. It is divided into two phases, the first is the use of machine learning to predict resources of the service level agreement required on future flight schedules, and the second is the use of implement a resource allocation algorithm to build a demand curve based on predicted resources. This proposal could be applicable to airports that will provide efficient and realistic for the resources demand curve to ensure the resource planning does not deviate from the real-time resource requirements. the model has proven good accuracy when using one day of flights to measuring deviation between the proposed model predict demand curve when flights did not include the location feature and the actual demand curve when flights include location.

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25

Jalili, Hassan, and Pierluigi Siano. "Modeling of unforced demand response programs." International Journal of Emerging Electric Power Systems 22, no. 2 (February 18, 2021): 233–41. http://dx.doi.org/10.1515/ijeeps-2020-0208.

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Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

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26

Zhang, Yijie, Mingli Zhang, Haiju Hu, and Xiaolong He. "Research on Supply and Demand of Aged Services Resource Allocation in China: A System Dynamics Model." Systems 10, no. 3 (May 2, 2022): 59. http://dx.doi.org/10.3390/systems10030059.

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With the rapid growth of the elderly population of China in recent years, the service demands of older Chinese people continue to increase. The increasingly severe situation with respect to the elderly population is an important social problem that China will face for a long time into the future. It is urgent to solve the problem of how to scientifically carry out allocation planning of service resources for the aged and guide the effective supply of service resources. This paper analyzes the factors affecting service resources for the aged, divides China’s service resource supply and demand system into a supply subsystem, a demand subsystem, and a population and economy subsystem. Using system dynamics methods to analyze the causal relationship between variables and the state space method to build a mathematical model and perform simulation analysis, we research the the current situation of China’s service resources supply and demand balance for the aged. In addition, we put forward resource configuration optimization measures for the future allocation of service resources for the aged, providing a practical basis for future decision-making.

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27

Potter-Witter, Karen, and Carl W. Ramm. "Aspen Supply, Use, and Demand in Michigan." Northern Journal of Applied Forestry 9, no. 1 (March 1, 1992): 10–14. http://dx.doi.org/10.1093/njaf/9.1.10.

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Abstract Michigan's aspen resource has come under increased pressure in the last decade. USDA Forest Service survey and output data and Michigan Department of Natural Resources stumpage price and industry data were compiled and analyzed to determine the current and possible future state of the resource. Significant declines in acreage of 20- to 40-year-old aspen type were found. Analysis of real prices for aspen pulpwood showed significant increases for the decade 1978-1988. North. J. Appl. For. 9(1):10-14.

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28

Judi, J. Antony, F. Ezhil Mary Arasi, and Dr S. Govindarajan. "Utility Based Resource Allocation Model for Cloud Services." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 6, no. 3 (October 30, 2013): 855–60. http://dx.doi.org/10.24297/ijmit.v6i3.723.

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Minimizing Resource allocation problems under the demand and price uncertainty in cloud computing environments is the motivation to explore a resource provisioning strategy for cloud consumers. In this paper a utilization-based optimal cloud (UBOC) algorithm is proposed to minimize the total cost for provisioning resources in a certain time period. To make an optimal decision, the demand uncertainty from cloud consumer side and price uncertainty from cloud providers are taken into account to adjust the tradeoff between on-demand and oversubscribed costs. Using this UBOC user can share cloud resources and pay based on the usage and the results show that this algorithm can minimize the total cost under uncertainty. It also provisions the resources to remove the demand uncertainty.

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Jain, Nirupa. "Energy Crisis, Resource Scarcity, and Innovations in Biofuels." Journal of Advanced Research in Alternative Energy, Environment and Ecology 07, no. 02 (June 19, 2020): 4–7. http://dx.doi.org/10.24321/2455.3093.202007.

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Natural resources are scarce and there is a need to explore new resources to meet the rising demand for energy. There is also a need to meet sustainable development goals through proper planning and strategy. Fossil fuel has a finite supply and there will be a huge gap between demand and supply due to rising demand. There is a need to develop biofuel to replace fossil fuel. This paper gives a discussion about current issues and future directions in these sectors.

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Lin, Ping, and Xiang Bin Zhang. "The Inverse Optimal Allocation Model of Manufacturing Resource for Small and Medium-Sized Manufacturing Enterprises in Grid Environment." Applied Mechanics and Materials 273 (January 2013): 22–27. http://dx.doi.org/10.4028/www.scientific.net/amm.273.22.

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Almost each product is produced by more than one enterprise, so resource sharing and cooperation become more and more important. Small and medium-size manufacturing enterprises sometimes have not enough resources to complete the orders and they have to look for cooperators to meet the market demands. Inverse optimization model of linear programming is suitable for solving resource optimal allocation for small and medium-sized manufacturing enterprises in dynamic grid. This paper establishes an inverse optimization model of a linear programming for grid resources management. To meet market demand, conditional on optimal production and product portfolios, this model adjusts the parameters in resources optimal allocation, and reverses the process of optimization. This inverse optimal model helps the enterprise node to adjust its structure and the number of resources allocation to better meet market demand and maximize the interests according to its budget.

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31

Schönherr, S. "Resource allocation on condition of random resource demand of network activities." Optimization 16, no. 5 (January 1985): 715–31. http://dx.doi.org/10.1080/02331938508843070.

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32

Rainwater, Chase, Joseph Geunes, and H. Edwin Romeijn. "Resource-Constrained Assignment Problems with Shared Resource Consumption and Flexible Demand." INFORMS Journal on Computing 26, no. 2 (May 2014): 290–302. http://dx.doi.org/10.1287/ijoc.2013.0565.

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33

Saleem, Arfa, Imran Mahmood, Hessam Sarjoughian, Hasan Arshad Nasir, and Asad Waqar Malik. "A Water Evaluation and Planning-based framework for the long-term prediction of urban water demand and supply." SIMULATION 97, no. 5 (January 31, 2021): 323–45. http://dx.doi.org/10.1177/0037549720984250.

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Increased usage and non-efficient management of limited resources has created the risk of water resource scarcity. Due to climate change, urbanization, and lack of effective water resource management, countries like Pakistan are facing difficulties coping with the increasing water demand. Rapid urbanization and non-resilient infrastructures are the key barriers in sustainable urban water resource management. Therefore, there is an urgent need to address the challenges of urban water management through effective means. We propose a workflow for the modeling and simulation of sustainable urban water resource management and develop an integrated framework for the evaluation and planning of water resources in a typical urban setting. The proposed framework uses the Water Evaluation and Planning system to evaluate current and future water demand and the supply gap. Our simulation scenarios demonstrate that the demand–supply gap can effectively be dealt with by dynamic resource allocation, in the presence of assumptions, for example, those related to population and demand variation with the change of weather, and thus work as a tool for informed decisions for supply management. In the first scenario, 23% yearly water demand is reduced, while in the second scenario, no unmet demand is observed due to the 21% increase in supply delivered. Similarly, the overall demand is fulfilled through 23% decrease in water demand using water conservation. Demand-side management not only reduces the water usage in demand sites but also helps to save money, and preserve the environment. Our framework coupled with a visualization dashboard deployed in the water resource management department of a metropolitan area can assist in water planning and effective governance.

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Chen, Lihua, Liying Wang, and Yingjie Lan. "Auction models with resource pooling in modern supply chain management." Modern Supply Chain Research and Applications 1, no. 2 (May 13, 2019): 120–34. http://dx.doi.org/10.1108/mscra-01-2019-0001.

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Purpose In this paper, the main focus is on supply and demand auction systems with resource pooling in modern supply chain from a theoretical modeling perspective. The supply and demand auction systems in modern supply chains among manufacturers and suppliers serve as information sharing mechanisms. The purpose of this paper is to match the supply and demand such that a modern supply chain can achieve incentive compatibility and economic efficiency. The authors design such a supply and demand auction system that can integrate resources to efficiently match the supply and demand. Design/methodology/approach The authors propose three theoretic models of modern supply chain auctions with resource pooling according to the Vickrey auction principle. They are supply auction model with demand resource pooling, demand auction model with supply resource pooling, and double auction model with demand and supply resource pooling. For the proposed auction models, the authors present three corresponding algorithms to allocate resources in the auction process by linear programming, and study the incentive compatibility and define the Walrasian equilibriums for the proposed auction models. The authors show that the solutions of the proposed algorithms are Walrasian equilibriums. Findings By introducing the auction mechanism, the authors aim to realize the following three functions. First is price mining: auction is an open mechanism with multiple participants. Everyone has his own utility and purchasing ability. So, the final price reflects the market value of the auction. Second is dynamic modern supply chain construction: through auction, firm can find appropriate partner efficiently. Third is resources integration: in business practices, especially in modern supply chain auctions, auctioneers can integrate resources and ally buyers or sellers to gain more efficiency in auctions. Originality/value In the paper, the authors propose three theoretic models and corresponding algorithms of modern supply chain auctions with resource pooling according using the Vickrey auction principle, which achieves three functions: price mining, dynamic modern supply chain construction and resources integrating. Besides, these proposed models are much closer to practical settings and may have potential applications in modern supply chain management.

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Lee, Seungmi, and Jinho Kim. "Analytical Assessment for System Peak Reduction by Demand Responsive Resources Considering Their Operational Constraints in Wholesale Electricity Market." Energies 11, no. 12 (November 30, 2018): 3343. http://dx.doi.org/10.3390/en11123343.

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It has been four years since the introduction of the Demand Response (DR) market in Korea. Although the DR market has been steadily increasing resource capacity and payments for demand resources, it cannot efficiently utilize DR resources under the current domestic policy system. In addition, research on the level at which the demand resource capacity registered in the DR market contributes to the reduction of peak demand is scarce. In this article, an algorithm that calculates the annual peak demand reduction contribution is developed considering the operational characteristics of DR resources in order to utilize the demand resources at a meaningful level. Also the peak demand reduction contribution is simulated for the next 15 years based on the historical electricity demand pattern in Korea and analyze the sensitivity of policy variables that affect the contribution of peak reduction. Finally, the critical policy constraint that affects the contribution of DR resources is identified and a method is proposed to improve the peak reduction contribution of DR resources. Related departments that operate the demand market can use this research as a reference for market operations.

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Bhatia, Abhinav, Pradeep Varakantham, and Akshat Kumar. "Resource Constrained Deep Reinforcement Learning." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 610–20. http://dx.doi.org/10.1609/icaps.v29i1.3528.

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In urban environments, resources have to be constantly matched to the “right” locations where customer demand is present. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in ERS (Emergency Response Systems); vehicles (cars, bikes among others) have to be matched to docking stations to reduce lost demand in shared mobility systems. Such problems are challenging owing to the demand uncertainty, combinatorial action spaces and constraints on allocation of resources (e.g., total resources, minimum and maximum number of resources at locations and regions).Existing systems typically employ myopic and greedy optimization approaches to optimize resource allocation. Such approaches typically are unable to handle surges or variances in demand patterns well. Recent work has demonstrated the ability of Deep RL methods in adapting well to highly uncertain environments. However, existing Deep RL methods are unable to handle combinatorial action spaces and constraints on allocation of resources. To that end, we have developed three approaches on top of the well known actor-critic approach, DDPG (Deep Deterministic Policy Gradient) that are able to handle constraints on resource allocation. We also demonstrate that they are able to outperform leading approaches on simulators validated on semi-real and real data sets.

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Hwang, Dawsen, Patrick Jaillet, and Vahideh Manshadi. "Online Resource Allocation Under Partially Predictable Demand." Operations Research 69, no. 3 (May 2021): 895–915. http://dx.doi.org/10.1287/opre.2020.2017.

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Masoudi, Nahid, and Marc Santugini. "Resource extraction under heterogeneous growth in demand." Resource and Energy Economics 42 (November 2015): 16–30. http://dx.doi.org/10.1016/j.reseneeco.2015.06.003.

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Elshkaki, Ayman, T. E. Graedel, Luca Ciacci, and Barbara K. Reck. "Resource Demand Scenarios for the Major Metals." Environmental Science & Technology 52, no. 5 (January 30, 2018): 2491–97. http://dx.doi.org/10.1021/acs.est.7b05154.

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Sun, Zheng, Quan Shi, Yadong Wang, and Qiang Wang. "Bayes Decision Model for Maintenance Resource Demand." IOP Conference Series: Earth and Environmental Science 170 (July 2018): 032023. http://dx.doi.org/10.1088/1755-1315/170/3/032023.

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Matyssek, R., R. Agerer, D. Ernst, J. C. Munch, W. Oßwald, H. Pretzsch, E. Priesack, H. Schnyder, and D. Treutter. "The Plant's Capacity in Regulating Resource Demand." Plant Biology 7, no. 6 (November 2005): 560–80. http://dx.doi.org/10.1055/s-2005-872981.

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Chakravorty, Ujjayant, and Darrell L. Krulce. "Heterogeneous Demand and Order of Resource Extraction." Econometrica 62, no. 6 (November 1994): 1445. http://dx.doi.org/10.2307/2951755.

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Kojima, Fuhito. "Efficient resource allocation under multi-unit demand." Games and Economic Behavior 82 (November 2013): 1–14. http://dx.doi.org/10.1016/j.geb.2013.06.005.

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Li, Zhigang, Xin Zhang, Junwei Zheng, Zhenduo Zhang, and Pengyu Wan. "Challenge or Hindrance? The Dual Path Effect of Perceived Task Demand on In-Role Performance and Work Fatigue." International Journal of Environmental Research and Public Health 19, no. 23 (November 23, 2022): 15561. http://dx.doi.org/10.3390/ijerph192315561.

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The evidence for the existence of perceived task demand is paradoxical. The purpose of the present study is to explore whether perceived task demand is a challenge or a hindrance stressor. To achieve this research purpose, based on conservation of resources theory, a conceptual model is developed that utilizes both a resource acquisition path and a resource depletion path. Using the experience-sampling method, over five consecutive days, 370 matched data were collected via mobile phone from 74 full-time employees in mainland China. The results show that perceived task demand has the characteristics of both challenge and hindrance stressors. On the one hand, perceived task demand enhances employees’ cognitive engagement, thereby facilitating task performance (resource acquisition path). On the other hand, perceived task demand boosts employees’ cognitive strain, thereby increasing work fatigue and decreasing in-role performance (resource depletion path). This research offers a comprehensive understanding of perceived task demand and provides strategies for task demand management.

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Backman, Charles A., and Yuri Blam. "Fiber availability in Russia and outlook for trade." Forestry Chronicle 73, no. 4 (August 1, 1997): 434–43. http://dx.doi.org/10.5558/tfc73434-4.

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The forest resources of Russia are discussed in terms of two broad regions, focusing on their ability to support harvest. Long-term economic harvest levels are identified. The ability of the Russian forest resource to support export of wood fiber is examined through scenario analyses linking capital, forest resource, and domestic demand. Key words: Russia, forest resource, AAC, trade, capital, domestic demand

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Kwag, Hyung-Geun, and Jin-O. Kim. "Optimal combined scheduling of generation and demand response with demand resource constraints." Applied Energy 96 (August 2012): 161–70. http://dx.doi.org/10.1016/j.apenergy.2011.12.075.

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Feng, Jian, Weiliang Zhang, and Sang-Bing Tsai. "Construction of a Multimedia-Based University Ideological and Political Big Data Cloud Service Teaching Resource Sharing Model." Mathematical Problems in Engineering 2021 (December 29, 2021): 1–12. http://dx.doi.org/10.1155/2021/9907630.

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In this paper, the intelligent education cloud service platform is first constructed in view of the high cost consumption in the process of resource sharing scheduling in colleges and universities. Secondly, the hierarchical education resource sharing grid model is proposed. Specifically, according to the characteristics of the educational resource grid, the key factors affecting the performance of copy creation strategy are analysed, and a dynamic copy creation strategy is proposed. A multiresource equity distribution mechanism based on the concept of resource sharing equity is further proposed. The mechanism establishes a planning model according to the limited task resource demand and the amount of resources shared by the user at different times so that the global cumulative share vector of superior resources meets the dictionary order optimally. The simulation experiment shows that the grid sharing model proposed in this paper has better performance on the educational cloud service platform. The proposed resource allocation mechanism has achieved good results in ensuring the fair distribution of resources and ensuring high resource utilization when resource sharing users put forward multiple groups of time-changing resource demands.

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Li, Tao, Sha Qiu, Shuxin Mao, Rui Bao, and Hongbing Deng. "Evaluating Water Resource Accessibility in Southwest China." Water 11, no. 8 (August 16, 2019): 1708. http://dx.doi.org/10.3390/w11081708.

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The accessibility, quantity, and quality of water resources are the basic requirements for guaranteeing water resource security. Research into regional water resource accessibility will contribute to improving regional water resource security and effective water resource management. In this study, we used a water resource accessibility index model considering five spatial factors to evaluate the grid-scale water resource accessibility and constructed the spatial pattern of water resource accessibility in Southwest China. Then, we analyzed the coupling coordination degree between county-level water resource accessibility and eco-socio-economic water demand elements. The water resource accessibility showed obvious regional differences, and the overall trend gradually decreased from Southeast to Northwest. The coupling coordination degree between county-level water resource accessibility and eco-socio-economic water demand elements was between 0.26 and 0.84, and was relatively low overall, whereas the counties (districts) with high coordination, moderate coordination, low coordination, reluctant coordination, and incoordination accounted for 0.92%, 5.31%, 21.06%, 59.71%, and 13.00% of total counties (districts), respectively. Therefore, the Southwest region needs to further strengthen the construction of its agricultural irrigation facilities, protect the water resources, and coordinate the relationship between water resource management and water demand elements to comprehensively guarantee regional sustainable development.

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Chen, Xiao-Jun, Jing Zhang, Jun-Huai Li, and Xiang Li. "Resource reconstruction algorithms for on-demand allocation in virtual computing resource pool." International Journal of Automation and Computing 9, no. 2 (April 2012): 142–54. http://dx.doi.org/10.1007/s11633-012-0627-3.

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Zanne, Amy E., Steven S. Lower, Zoe G. Cardon, and Colin M. Orians. "15N partitioning in tomato: vascular constraints versus tissue demand." Functional Plant Biology 33, no. 5 (2006): 457. http://dx.doi.org/10.1071/fp05299.

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Allocation of root-derived resources is influenced by tissue demand; however, vascular pathways mediate resource flow from roots to shoots. In vascularly constrained plants (i.e. sectored plants), effects of vascular connections likely limit homogenous resource delivery, especially when environmental resource distribution is patchy. Here, we quantify relative roles of vascular connections, demands by different leaves (i.e. by leaf age and size), and molecule size of transported N compounds (effective sectoriality: nitrate v. ammonium) on allocation of 15N in the sectored tomato (Solanum lycopersicum L.). Vascular connections were the strongest predictor of both accumulation (amount per leaf; P<0.0001) and δ (estimate of concentration; P<0.0001) 15N values in mature leaves, but young expanding leaves did not show such dramatically sectored uptake (accumulation: P=0.0685; δ: P=0.0455), suggesting that sectoriality is less strong in young expanding tissue, especially in the youngest leaf. In patchy environments sectoriality, then, should have large consequences for the ability of a plant to allocate resources in mature tissue; however, young leaves do not appear to experience such strong vascular constraints when building new tissue.

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