Journal articles on the topic 'Resource allocations problems'
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1
Lin, Shan-Shan. "Due-Window Assignment and Resource Allocation Scheduling with Truncated Learning Effect and Position-Dependent Weights." Discrete Dynamics in Nature and Society 2020 (October 8, 2020): 1–7. http://dx.doi.org/10.1155/2020/9260479.
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This paper studies single-machine due-window assignment scheduling problems with truncated learning effect and resource allocation simultaneously. Linear and convex resource allocation functions under common due-window (CONW) assignment are considered. The goal is to find the optimal due-window starting (finishing) time, resource allocations and job sequence that minimize a weighted sum function of earliness and tardiness, due window starting time, due window size, and total resource consumption cost, where the weight is position-dependent weight. Optimality properties and polynomial time algorithms are proposed to solve these problems.
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Fu, Lei, Junmin Wang, Shiwu Wang, Hongxi Peng, and Zihan Gui. "Study of Water Resource Allocation and Optimization Considering Reclaimed Water in a Typical Chinese City." Sustainability 15, no. 1 (January 2, 2023): 819. http://dx.doi.org/10.3390/su15010819.
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Reclaimed water is considered to be an important alternative to freshwater to solve the imbalance between the supply and demand of regional water resources; it is also recognized as an effective tool for alleviating ecological problems caused by insufficient water flow. Yiwu City is a typical area experiencing a water shortage in southeastern China because the regional water resources are limited. In this study, the multiple water resource allocations in Yiwu City are optimized, the complex coupling model of multiple water resource allocation is established, and both the economic and ecological effects of multiple water resource allocation in Yiwu City are simulated and analyzed. The simulation results of optimizing the multiple water resource allocations show an efficient way of reclaimed water utilization in this typical Chinese city. In order to ensure the future economic and social development of Yiwu City, it is necessary to introduce reclaimed water into different fields, such as residential water, industrial water, agricultural water, and environmental water. Reclaimed water has also proven to have a high capability for pollutant control and reduction, which is also important to the ecology and environmental protection.
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Budish, Eric, Yeon-Koo Che, Fuhito Kojima, and Paul Milgrom. "Designing Random Allocation Mechanisms: Theory and Applications." American Economic Review 103, no. 2 (April 1, 2013): 585–623. http://dx.doi.org/10.1257/aer.103.2.585.
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Randomization is commonplace in everyday resource allocation. We generalize the theory of randomized assignment to accommodate multi-unit allocations and various real-world constraints, such as group-specific quotas (“controlled choice”) in school choice and house allocation, and scheduling and curriculum constraints in course allocation. We develop new mechanisms that are ex ante efficient and fair in these environments, and that incorporate certain non-additive substitutable preferences. We also develop a “utility guarantee” technique that limits ex post unfairness in random allocations, supplementing the ex ante fairness promoted by randomization. This can be applied to multi-unit assignment problems and certain two-sided matching problems. (JEL C78, D82)
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Etor, Comfort R., Ekpenyong E. Ekanem, and Mary A. Sule. "Access and Resource Allocation to Education in Nigeria." International Education Studies 13, no. 3 (February 18, 2020): 79. http://dx.doi.org/10.5539/ies.v13n3p79.
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Access to education in Nigeria has attracted the attention of many Nigerians due to rising number of children and youths who are unable to gain admission into schools to actualize their educational and career dreams. Besides, there are also the issues of equitable and adequate resources to the existing schools to ensure qualitative education at all levels in Nigeria. This is the trust of this paper, as it examines the significance of access to education, equal educational opportunities, resource allocation to education and criteria for such allocations. The paper also attempts to highlight some problems and prospects of access and resource allocation to education in Nigeria.
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Trang, Le Hong, and Hoang Huu Viet. "Optimally stable matchings for resource allocations." Vietnam Journal of Science and Technology 60, no. 2 (April 21, 2022): 257–69. http://dx.doi.org/10.15625/2525-2518/16107.
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The stable marriage problem (SMP) and its variants have received much attention in the literature due to their wide range of applications. One of their applications is resource allocation in network environments. In this paper, we consider two resource allocation problems. The first one is to maximize the performance of a system in the fog computing environment while maintaining a low cost. In the problem, the resource allocation task will be rewritten as the MAX-SMTI variant (i.e., finding the maximum cardinality stable matching of the stable marriage with ties and incomplete lists). It is then formulated under an integer linear program to solve. The best allocation can then be chosen to be the lowest cost one among stable matchings. In the same manner, another variant called MAX-HRT (i.e., finding the maximum cardinality stable matching of the hospital-residents with ties) is applied for the second application regarding the virtual machine allocation. By using MAX-SMTI and MAX-HRT models which are solved via integer linear programs, we aim to not only find stable matchings for the resource allocation problems but also maximum length matchings. Consequently, a maximum number of user requests should be served at a time. The models are implemented in C++ using the SCIP solver. Numerical experiments are conducted for large datasets and results are given to show the efficiency of the models.
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Glazebrook, K. D. "Bounds for discounted stochastic scheduling problems." Journal of Applied Probability 28, no. 4 (December 1991): 791–801. http://dx.doi.org/10.2307/3214682.
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Suppose that π is a policy for resource allocation in a stochastic environment and π ∗ is an optimal policy. Two existing procedures for policy evaluation are described and compared. Both of these evaluate π by means of upper bounds on R(π ∗) – R(π), the total reward lost when making resource allocations according to π rather than π∗. The bounds developed by these two methods are called Type 1 and Type 2. We demonstrate by example that neither of these procedures dominates the other in the sense of always yielding tighter bounds. A modification to Type 2 bounds is proposed resulting in an improved procedure which always dominates the Type 1 approach.
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Glazebrook, K. D. "Bounds for discounted stochastic scheduling problems." Journal of Applied Probability 28, no. 04 (December 1991): 791–801. http://dx.doi.org/10.1017/s0021900200042704.
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Suppose that π is a policy for resource allocation in a stochastic environment and π ∗ is an optimal policy. Two existing procedures for policy evaluation are described and compared. Both of these evaluate π by means of upper bounds on R(π ∗) – R(π), the total reward lost when making resource allocations according to π rather than π∗. The bounds developed by these two methods are called Type 1 and Type 2. We demonstrate by example that neither of these procedures dominates the other in the sense of always yielding tighter bounds. A modification to Type 2 bounds is proposed resulting in an improved procedure which always dominates the Type 1 approach.
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Cigler, Ludek, and Boi Faltings. "Symmetric Subgame Perfect Equilibria in Resource Allocation." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1326–32. http://dx.doi.org/10.1609/aaai.v26i1.8233.
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We analyze symmetric protocols to rationally coordinate on an asymmetric, efficient allocation in an infinitely repeated N-agent, C-resource allocation problems. (Bhaskar 2000) proposed one way to achieve this in 2-agent, 1-resource allocation games: Agents start by symmetrically randomizing their actions, and as soon as they each choose different actions, they start to follow a potentially asymmetric "convention" that prescribes their actions from then on. We extend the concept of convention to the general case of infinitely repeated resource allocation games with N agents and C resources. We show that for any convention, there exists a symmetric subgame perfect equilibrium which implements it. We present two conventions: bourgeois, where agents stick to the first allocation; and market, where agents pay for the use of resources, and observe a global coordination signal which allows them to alternate between different allocations. We define price of anonymity of a convention as the ratio between the maximum social payoff of any (asymmetric) strategy profile and the expected social payoff of the convention. We show that while the price of anonymity of the bourgeois convention is infinite, the market convention decreases this price by reducing the conflict between the agents.
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Chen, Ye, Nikola Marković, Ilya O. Ryzhov, and Paul Schonfeld. "Data-Driven Robust Resource Allocation with Monotonic Cost Functions." Operations Research 70, no. 1 (January 2022): 73–94. http://dx.doi.org/10.1287/opre.2021.2145.
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Using Data to Allocate Resources Efficiently In city logistics systems, a fleet of vehicles is divided between service regions that function autonomously. Each region finds optimal routes for its own fleet and incurs costs accordingly. More vehicles lead to lower costs, but the trade-off is that fewer vehicles are left for other regions. Costs are difficult to quantify precisely because of demand uncertainty but can be estimated using data. The paper “Data-driven robust resource allocation with monotonic cost functions” by Chen, Marković, Ryzhov, and Schonfeld develops a principled risk-averse approach for two-stage resource allocation. The authors propose a new uncertainty model for decreasing cost functions and show how it can be leveraged to efficiently find resource allocations that demonstrably reduce the frequency of high-cost scenarios. This framework combines statistics and optimization in a novel way and is applicable to a general class of resource allocation problems, encompassing facility location, vehicle routing, and discrete-event simulation.
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Cigler, L., and B. Faltings. "Symmetric Subgame-Perfect Equilibria in Resource Allocation." Journal of Artificial Intelligence Research 49 (February 26, 2014): 323–61. http://dx.doi.org/10.1613/jair.4166.
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We analyze symmetric protocols to rationally coordinate on an asymmetric, efficient allocation in an infinitely repeated N-agent, C-resource allocation problems, where the resources are all homogeneous. Bhaskar proposed one way to achieve this in 2-agent, 1-resource games: Agents start by symmetrically randomizing their actions, and as soon as they each choose different actions, they start to follow a potentially asymmetric "convention" that prescribes their actions from then on. We extend the concept of convention to the general case of infinitely repeated resource allocation games with N agents and C resources. We show that for any convention, there exists a symmetric subgame-perfect equilibrium which implements it. We present two conventions: bourgeois, where agents stick to the first allocation; and market, where agents pay for the use of resources, and observe a global coordination signal which allows them to alternate between different allocations. We define price of anonymity of a convention as a ratio between the maximum social payoff of any (asymmetric) strategy profile and the expected social payoff of the subgame-perfect equilibrium which implements the convention. We show that while the price of anonymity of the bourgeois convention is infinite, the market convention decreases this price by reducing the conflict between the agents.
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Wang, Tianshu, and Peter Melchior. "Graph neural network-based resource allocation strategies for multi-object spectroscopy." Machine Learning: Science and Technology 3, no. 1 (February 7, 2022): 015023. http://dx.doi.org/10.1088/2632-2153/ac4d12.
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Abstract Resource allocation problems are often approached with linear programming techniques. But many concrete allocation problems in the experimental and observational sciences cannot or should not be expressed in the form of linear objective functions. Even if the objective is linear, its parameters may not be known beforehand because they depend on the results of the experiment for which the allocation is to be determined. To address these challenges, we present a bipartite graph neural network (GNN) architecture for trainable resource allocation strategies. Items of value and constraints form the two sets of graph nodes, which are connected by edges corresponding to possible allocations. The GNN is trained on simulations or past problem occurrences to maximize any user-supplied, scientifically motivated objective function, augmented by an infeasibility penalty. The amount of feasibility violation can be tuned in relation to any available slack in the system. We apply this method to optimize the astronomical target selection strategy for the highly multiplexed Subaru Prime Focus Spectrograph instrument, where it shows superior results to direct gradient descent optimization and extends the capabilities of the currently employed solver which uses linear objective functions. The development of this method enables fast adjustment and deployment of allocation strategies, statistical analyses of allocation patterns, and fully differentiable, science-driven solutions for resource allocation problems.
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Huang, Xiaoge, Xuesong Deng, Chengchao Liang, and Weiwei Fan. "Blockchain-Enabled Task Offloading and Resource Allocation in Fog Computing Networks." Wireless Communications and Mobile Computing 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/7518534.
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To address the data security and user privacy issues in the task offloading process and resource allocation of the fog computing network, a blockchain-enabled fog computing network task offloading model is proposed in this paper. Furthermore, to reduce the network utility which is defined as the total energy consumption of the fog computing network and the total delay of the blockchain network, a blockchain-enabled fog computing network task offloading and resource allocation algorithm (TR-BFCN) is proposed to jointly optimize the task offloading decision and resource allocation. Finally, the original nonconvex optimization problem is converted into two suboptimization problems, namely, task offloading decisions and computational resource allocations. Moreover, a two-stage Stackelberg game model is designed to obtain the optimal amount of purchased resource and the optimal resource pricing. Simulation results show that the proposed TR-BFCN algorithm can effectively reduce the network utility compared with other algorithms.
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Thabet, Saqr Khalil Saeed, Emmanuel Osei-Mensah, Omar Ahmed, Abegaz Mohammed Seid, and Olusola Bamisile. "Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation." Applied Sciences 12, no. 10 (May 17, 2022): 5047. http://dx.doi.org/10.3390/app12105047.
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During wireless video transmission, channel conditions can vary drastically. When the channel fails to support the transmission bit rate, the video quality degrades sharply. A pseudo-analog transmission system such as SoftCast relies on linear operations to achieve a linear quality transition over a wide range of channel conditions. When transmitting 3D videos over SoftCast, the following issues arise: (1) assigning the transmission power to texture and depth maps to obtain the optimal overall quality and (2) handling 3D video data traffic by dropping and re-allocating resources. This paper solves the pseudo-analog transmission resource allocation problem and improves the results by applying the optimal joint power allocation. First, the minimum and the target distortion optimization problems are formulated in terms of a power–bandwidth pair versus distortion. Then, a minimum distortion optimization algorithm iteratively computes all the possible resource allocations to find the optimal allocation based on the minimum distortion. Next, the three-dimensional target distortion problem is divided into two subproblems. In the power-distortion problem, to obtain a target distortion, the algorithm exhaustively solves the closed form of the power resource under a predefined upper-bound bandwidth. For the bandwidth-distortion problem, reaching a target distortion requires solving iteratively for the bandwidth resource closed form, given a predefined power. The proposed resource control scheme shows an improvement in transmission efficiency and resource utilization. At low power usage, the proposed method could achieve a PSNR gain of up to 1.5 dB over SoftCast and even a 1.789 dB gain over a distortion-resource algorithm, using less than 1.4% of the bandwidth.
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Wellman, M. P. "A Market-Oriented Programming Environment and its Application to Distributed Multicommodity Flow Problems." Journal of Artificial Intelligence Research 1 (August 1, 1993): 1–23. http://dx.doi.org/10.1613/jair.2.
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Market price systems constitute a well-understood class of mechanisms that under certain conditions provide effective decentralization of decision making with minimal communication overhead. In a market-oriented programming approach to distributed problem solving, we derive the activities and resource allocations for a set of computational agents by computing the competitive equilibrium of an artificial economy. WALRAS provides basic constructs for defining computational market structures, and protocols for deriving their corresponding price equilibria. In a particular realization of this approach for a form of multicommodity flow problem, we see that careful construction of the decision process according to economic principles can lead to efficient distributed resource allocation, and that the behavior of the system can be meaningfully analyzed in economic terms.
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Goldberg, Paul W., Alexandros Hollender, and Warut Suksompong. "Contiguous Cake Cutting: Hardness Results and Approximation Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1990–97. http://dx.doi.org/10.1609/aaai.v34i02.5570.
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We study the fair allocation of a cake, which serves as a metaphor for a divisible resource, under the requirement that each agent should receive a contiguous piece of the cake. While it is known that no finite envy-free algorithm exists in this setting, we exhibit efficient algorithms that produce allocations with low envy among the agents. We then establish NP-hardness results for various decision problems on the existence of envy-free allocations, such as when we fix the ordering of the agents or constrain the positions of certain cuts. In addition, we consider a discretized setting where indivisible items lie on a line and show a number of hardness results strengthening those from prior work.
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Behrman, Jere R. "Intra-family Distribution in Developing Countries." Pakistan Development Review 33, no. 3 (September 1, 1994): 253–96. http://dx.doi.org/10.30541/v33i3pp.253-296.
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Intra-household allocations appear to be quite important in the determination of time use, human resource investments, and intra- and inter-generational transfers in developing countries. The nature of such allocations has important implications for the efficiency, equity, and efficacy of the micro and macro-economic policies. In the past decade and a half, there has been substantial progress in modelling intrahousehold allocations in ways that lead to testable propositions despite enormous data limitations regarding the nature of the allocation of unobserved variables and the impact of unobserved heterogeneous endowments. The parent -child exchange literature is a subset of these studies that advances in two dimensions by allowing children to have different preferences from their parents' and by incorporating a broader notion of interactions, including the attention provided by the children to their parents. Yet this literature has most of the problems that are indicated with regard to the unified household preference models. The exchange literature to date has assumed away het" erogeneity in endowments which plays such a critical role in the studies that assume the unified household preferences. It is also silent on how human resource investments enter into the relations between parents and children. The collective models of household behaviour emphasise that different household members. usually husband and wife. may have different preferences and a different command over resources. Interesting theoretical results have been derived concerning the conditions under which the income-sharing rules and the allocation of non-assignable goods can be derived. But there are many limitations in this literature. The literature on the collective models of household behaviours is static and gives little consideration to the dynamic processes and learning.
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Honda, Aoi, and Mario Köppen. "A Fairness Relation Based on the Asymmetric Choquet Integral and Its Application in Network Resource Allocation Problems." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/725974.
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The recent problem of network resource allocation is studied where pairs of users could be in a favourable situation, given that the allocation scheme is refined by some add-on technology. The general question here is whether the additional effort can be effective with regard to the user’s experience of fairness. The computational approach proposed in this paper to handle this question is based on the framework of relational optimization. For representing different weightings for different pairs of users, the use of a fuzzy measure appears to be reasonable. The generalized Choquet integrals are discussed from the viewpoint of representing fairness and it is concluded that the asymmetric Choquet integral is the most suitable approach. A binary relation using the asymmetric Choquet integral is proposed. In case of a supermodular fuzzy measure, this is a transitive and cycle-free relation. The price of fairness with regard to a wireless channel allocation problem taking channel interference into account is experimentally studied and it can be seen that the asymmetric on relation actually selects allocations that perform on average between maxmin fairness and proportional fairness, and being more close to maxmin fairness as long as channel interference is not high.
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Marinescu, Marian V., Taraneh Sowlati, and Thomas C. Maness. "The development of a timber allocation model using data envelopment analysis." Canadian Journal of Forest Research 35, no. 10 (October 1, 2005): 2304–15. http://dx.doi.org/10.1139/x05-141.
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A timber allocation model using data envelopment analysis (DEA) was developed to test the capability of DEA to assist with multicriteria timber allocation decisions. The resulting DEA Timber Allocation model is able to allocate forest stands, referred to as stewardship units, to different forest products companies without the need for weighing or prioritizing the allocation criteria. The allocation procedure was demonstrated in a case considering two allocation criteria: profit and employment. The allocation generated by the model was compared with random, profit-based, and employment-based allocations. The results showed that the model was capable of producing practical solutions and balancing the two allocation criteria. However, adding other allocation criteria was complicated by procedural concerns. Despite its current limitations, the model opens the door to future applications of DEA in forest resource allocation problems.
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Goldberg, Paul, Alexandros Hollender, and Warut Suksompong. "Contiguous Cake Cutting: Hardness Results and Approximation Algorithms." Journal of Artificial Intelligence Research 69 (September 22, 2020): 109–41. http://dx.doi.org/10.1613/jair.1.12222.
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We study the fair allocation of a cake, which serves as a metaphor for a divisible resource, under the requirement that each agent should receive a contiguous piece of the cake. While it is known that no finite envy-free algorithm exists in this setting, we exhibit efficient algorithms that produce allocations with low envy among the agents. We then establish NP-hardness results for various decision problems on the existence of envy-free allocations, such as when we fix the ordering of the agents or constrain the positions of certain cuts. In addition, we consider a discretized setting where indivisible items lie on a line and show a number of hardness results extending and strengthening those from prior work. Finally, we investigate connections between approximate and exact envy-freeness, as well as between continuous and discrete cake cutting.
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Clausen, Anders, Aisha Umair, Yves Demazeau, and Bo Nørregaard Jørgensen. "Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization." Energies 13, no. 11 (June 9, 2020): 2961. http://dx.doi.org/10.3390/en13112961.
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Resource allocation problems are at the core of the smart grid where energy supply and demand must match. Multi-objective optimization can be applied in such cases to find the optimal allocation of energy resources among consumers considering energy domain factors such as variable and intermittent production, market prices, or demand response events. In this regard, this paper considers consumer energy demand and system-wide energy constraints to be individual objectives and optimization variables to be the allocation of energy over time to each of the consumers. This paper considers a case in which multi-objective optimization is used to generate Pareto sets of solutions containing possible allocations for multiple energy intensive consumers constituted by commercial greenhouse growers. We consider the problem of selecting a final solution from these Pareto sets, one of maximizing the social welfare between objectives. Social welfare is a set of metrics often applied to multi-agent systems to evaluate the overall system performance. We introduce and apply social welfare ordering using different social welfare metrics to select solutions from these sets to investigate the impact of the type of social welfare metric on the optimization outcome. The results of our experiments indicate how different social welfare metrics affect the optimization outcome and how that translates to general resource allocation strategies.
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Jiang, Jiafu, Linyu Tang, Ke Gu, and WeiJia Jia. "Secure Computing Resource Allocation Framework For Open Fog Computing." Computer Journal 63, no. 4 (January 30, 2020): 567–92. http://dx.doi.org/10.1093/comjnl/bxz108.
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Abstract Fog computing has become an emerging environment that provides data storage, computing and some other services on the edge of network. It not only can acquire data from terminal devices, but also can provide computing services to users by opening computing resources. Compared with cloud computing, fog devices can collaborate to provide users with powerful computing services through resource allocation. However, as many of fog devices are not monitored, there are some security problems. For example, since fog server processes and maintains user information, device information, task parameters and so on, fog server is easy to perform illegal resource allocation for extra benefits. In this paper, we propose a secure computing resource allocation framework for open fog computing. In our scheme, the fog server is responsible for processing computing requests and resource allocations, and the cloud audit center is responsible for auditing the behaviors of the fog servers and fog nodes. Based on the proposed security framework, our proposed scheme can resist the attack of single malicious node and the collusion attack of fog server and computing devices. Furthermore, the experiments show our proposed scheme is efficient. For example, when the number of initial idle service devices is 40, the rejection rate of allocated tasks is 10% and the total number of sub-tasks is changed from 150 to 200, the total allocation time of our scheme is only changed from 15 ms to 25 ms; additionally, when the task of 5000 order matrix multiplication is tested on 10 service devices, the total computing time of our scheme is $\sim$250 s, which is better than that of single computer (where single computer needs more than 1500 s). Therefore, our proposed scheme has obvious advantages when it faces some tasks that require more computational cost, such as complex scientific computing, distributed massive data query, distributed image processing and so on.
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Florentine, Robert. "Assessing Drug and Alcohol Treatment Needs of General and Special Populations: Conceptual, Empirical and Inferential Issues." Journal of Drug Issues 24, no. 3 (July 1994): 445–62. http://dx.doi.org/10.1177/002204269402400306.
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“Needs assessments” have become increasingly important in guiding resource allocations to publicly funded drug and alcohol treatment programs. Although technical progress in conducting needs assessments in recent years has occurred, virtually all approaches used to identify treatment needs of general and special populations suffer from conceptual, empirical, and inferential problems. This article clarifies the more important problems and offers recommendations about how to improve needs assessments.
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AI, SHIZHONG, RONG DU, and QIYING HU. "DYNAMIC OPTIMAL BUDGET ALLOCATION FOR INTEGRATED MARKETING CONSIDERING PERSISTENCE." International Journal of Information Technology & Decision Making 09, no. 05 (September 2010): 715–36. http://dx.doi.org/10.1142/s021962201000407x.
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Aiming at forming dynamic optimal integrated marketing policies, we build a budget allocation model considering both current effects and sustained ones. The model includes multiple time periods and multiple marketing tools which interact through a common resource pool as well as through delayed cross influences on each other's sales, reflecting the nature of "integrated marketing" and its dynamics. In our study, marginal analysis is used to illuminate the structure of optimal policy. We derive some analytical results which have managerial implications and give strong supports to empirical actions in marketing. Our results are consistent with those in some resource allocations problems, but different from those in the others. We illustrate the application of our model by demonstrating a case of a beauty salon in Xi'an, China. We show how to optimally allocate budgets between different sales promotion components so as to maximize the profits of beauty product and service companies.
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Alsubai, Shtwai, Harish Garg, and Abdullah Alqahtani. "A Novel Hybrid MSA-CSA Algorithm for Cloud Computing Task Scheduling Problems." Symmetry 15, no. 10 (October 18, 2023): 1931. http://dx.doi.org/10.3390/sym15101931.
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Recently, the dynamic distribution of resources and task scheduling has played a critical role in cloud computing to achieve maximum storage and performance. The allocation of computational tasks in the cloud is a complicated process that can be affected by some factors, such as available network bandwidth, makespan, and cost considerations. However, these allocations are always non-symmetric. Therefore, it is crucial to optimize available bandwidth for efficient cloud computing task scheduling. In this research, a novel swarm-based task scheduling with a security approach is proposed to optimize the distribution of tasks using available resources and encode cloud information during task scheduling. It can combine the Moth Swarm Algorithm (MSA) with the Chameleon Swarm Algorithm (CSA) for the task scheduling process and utilizes the Polymorphic Advanced Encryption Standard (P-AES) for information security of cloud scheduled tasks. The approach offers a new perspective for utilizing swarm intelligence algorithms to optimize cloud task scheduling. The integration of MSA and CSA with P-AES enables the approach to provide efficient and secure task scheduling by exploiting the strengths of used algorithms. The study evaluates the performance of the proposed approach in terms of the degree of imbalance, makespan, resource utilization, cost, average waiting time, response time, throughput, latency, execution time, speed, and bandwidth utilization. The simulation is carried out using a wide range of tasks from 1000 to 5000. The results show that the approach provides an innovative solution to the challenges of task scheduling in cloud environments and improves the performance of cloud services in terms of effectiveness and security measures.
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R, Anitha, and C. Vidya Raj. "A Comprehensive Survey on SLA Compliant Energy Aware Resource Allocation in Cloud Datacenters." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 335. http://dx.doi.org/10.23956/ijermt.v6i6.290.
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Cloud Computing has achieved immense popularity due to its unmatched benefits and characteristics. With its increasing popularity and round the clock demand, cloud based data centers often suffer with problems due to over-usage of resources or under-usage of capable servers that ultimately leads to wastage of energy and overall elevated cost of operation. Virtualization plays a key role in providing cost effective solution to service users. But on datacenters, load balancing and scheduling techniques remain inevitable to provide better Quality of Service to the service users and maintenance of energy efficient operations in datacenters. Energy-Aware resource allocation and job scheduling mechanisms in VMs has helped datacenter providers to reduce their cost incurrence through predictive job scheduling and load balancing. But it is quite difficult for any SLA oriented systems to maintain equilibrium between QoS and cost incurrence while considering their legal assurance of quality, as there should not be any violations in their service agreement. This paper presents some state-of-the-art works by various researchers and experts in the arena of cloud computing systems and particularly emphasizes on energy aware resource allocations, job scheduling techniques, load balancing and price prediction methods. Comparisons are made to demonstrate usefulness of the mechanisms in different scenarios.
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Zhang, Miao, Yao Zhang, Qian Cen, and Shixun Wu. "Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network." International Journal of Distributed Sensor Networks 18, no. 6 (June 2022): 155013292211043. http://dx.doi.org/10.1177/15501329221104330.
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Machine learning techniques, especially deep learning algorithms have been widely utilized to deal with different kinds of research problems in wireless communications. In this article, we investigate the secrecy rate maximization problem in a non-orthogonal multiple access network based on deep learning approach. In this non-orthogonal multiple access network, the base station intends to transmit two integrated information: a confidential information to user 1 (the strong user) and a broadcast information to user 1 and user 2. In addition, there exists an eavesdropper that intends to decode the confidential information due to the broadcast nature of radio waves. Hence, we formulate the optimization problem as a secrecy rate maximization problem. We first solve this problem by employing convex optimization technique, then we generate the training, validation, and test dataset. We propose a deep neural network–based approach to learn to optimize the resource allocations. The advantages of the proposed deep neural network are the capabilities to achieve low complexity and latency resource allocations. Simulation results are provided to show that the proposed deep neural network approach is capable of reaching near-optimal secrecy rate performance with significantly reduced computational time, when compared with the benchmark conventional approach.
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Wu, J., and E. H. Durfee. "Resource-Driven Mission-Phasing Techniques for Constrained Agents in Stochastic Environments." Journal of Artificial Intelligence Research 38 (July 30, 2010): 415–73. http://dx.doi.org/10.1613/jair.3004.
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Because an agent's resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such as power or payload restrictions), the competing needs of other agents for the same resources, and the stochastic nature of the environment. Such agents can, in general, achieve more of their objectives if they can use --- and even create --- opportunities to change which resources they hold at various times. Driven by resource constraints, the agents could break their overall missions into an optimal series of phases, optimally reconfiguring their resources at each phase, and optimally using their assigned resources in each phase, given their knowledge of the stochastic environment. In this paper, we formally define and analyze this constrained, sequential optimization problem in both the single-agent and multi-agent contexts. We present a family of mixed integer linear programming (MILP) formulations of this problem that can optimally create phases (when phases are not predefined) accounting for costs and limitations in phase creation. Because our formulations multaneously also find the optimal allocations of resources at each phase and the optimal policies for using the allocated resources at each phase, they exploit structure across these coupled problems. This allows them to find solutions significantly faster(orders of magnitude faster in larger problems) than alternative solution techniques, as we demonstrate empirically.
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Bagade, Vaishnavi, Dnyaneshwar Bankar, Neha Chaudhari, Anand Gajra, and Anandrao R. Deshmukh. "Optimization of Resource Allocations to Maximize Benefit Cost Ratio of the Irrigation System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2033–42. http://dx.doi.org/10.22214/ijraset.2022.42748.
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Abstract: Water resources projects are very complex in nature, requires huge financial investment and requires to consider socioeconomic, political, environmental aspects apart from technical aspects. There are many techniques evolved over the years to solve complex water resource problems. Pimpalgaon Dhale medium irrigation project system is considered in this project is located in Barshi taluka of Solapur district. It is planned to irrigate an ICA of 2400 ha of 6 villages namely Pimpalgaon, Pangaon, Yawali, Sakat, Undegaon and Irle. Dam is completed in 2008 however distribution system is still incomplete. The system is optimized to calculate maximize net benefit from the crops subjected to various constraints (viz. water availability, land availability, male and female labour availability, capital availability etc,). Single objective linear programming model is formulated and constraints are written and solver program of MS excel is used to derive maximum net benefit from the irrigation system under consideration. Benefit Cost ratio is calculated and compared it with that calculated adopting conventional methodology. The data required for model formulation is adopted from various sources such as Government reports/ documents, reports available on websites, research papers etc. The constraints such as capital availability within irrigation system during kharif and rabi season, female labour hours availability during kharif and rabi season limits the area under crops as well as net benefits however there is substantial increase in area under irrigation and net benefits from the irrigation system. The Solver program of MS Excel is very useful and convenient to use for solving linear programming. Keywords: Water Resource Projects, Maximize net benefit, Benefit Cost Ratio, Linear programming.,
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Fang, Cheng, Andrew J. Wang, and Brian C. Williams. "Chance-constrained Static Schedules for Temporally Probabilistic Plans." Journal of Artificial Intelligence Research 75 (December 7, 2022): 1323–72. http://dx.doi.org/10.1613/jair.1.13636.
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Time management under uncertainty is essential to large scale projects. From space exploration to industrial production, there is a need to schedule and perform activities. given complex specifications on timing. In order to generate schedules that are robust to uncertainty in the duration of activities, prior work has focused on a problem framing that uses an interval-bounded uncertainty representation. However, such approaches are unable to take advantage of known probability distributions over duration. In this paper we concentrate on a probabilistic formulation of temporal problems with uncertain duration, called the probabilistic simple temporal problem. As distributions often have an unbounded range of outcomes, we consider chance-constrained solutions, with guarantees on the probability of meeting temporal constraints. By considering distributions over uncertain duration, we are able to use risk as a resource, reason over the relative likelihood of outcomes, and derive higher utility solutions. We first demonstrate our approach by encoding the problem as a convex program. We then develop a more efficient hybrid algorithm whose parent solver generates risk allocations and whose child solver generates schedules for a particular risk allocation. The child is made efficient by leveraging existing interval-bounded scheduling algorithms, while the parent is made efficient by extracting conflicts over risk allocations. We perform numerical experiments to show the advantages of reasoning over probabilistic uncertainty, by comparing the utility of schedules generated with risk allocation against those generated from reasoning over bounded uncertainty. We also empirically show that solution time is greatly reduced by incorporating conflict-directed risk allocation.
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You, Peng-Sheng, Chun-Chieh Lee, and Yi-Chih Hsieh. "Bandwidth allocation and pricing problem for a duopoly market." Yugoslav Journal of Operations Research 21, no. 1 (2011): 65–78. http://dx.doi.org/10.2298/yjor1101065y.
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This research discusses the Internet service provider (ISP) bandwidth allocation and pricing problems for a duopoly bandwidth market with two competitive ISPs. According to the contracts between Internet subscribers and ISPs, Internet subscribers can enjoy their services up to their contracted bandwidth limits. However, in reality, many subscribers may experience the facts that their on-line requests are denied or their connection speeds are far below their contracted speed limits. One of the reasons is that ISPs accept too many subscribers as their subscribers. To avoid this problem, ISPs can set limits for their subscribers to enhance their service qualities. This paper develops constrained nonlinear programming to deal with this problem for two competitive ISPs. The condition for reaching the equilibrium between the two competitive firms is derived. The market equilibrium price and bandwidth resource allocations are derived as closed form solutions.
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Alfeld, Louis Edward, Colleen S. Pilliod, and James R. Wilkins. "The Virtual Shipyard: A Simulation Model of the Shipbuilding Process." Journal of Ship Production 14, no. 01 (February 1, 1998): 33–40. http://dx.doi.org/10.5957/jsp.1998.14.1.33.
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This paper describes a unique software program that simulates the dynamic complexities of the ship construction process. The program, called ShipBuild™, was developed by Decision Dynamics, Inc. (DDI) under a Small Business Innovation Research (SBIR) contract sponsored by NAVSEA. The program greatly simplifies the planning and replanning process, making it easy to create a good production plan and keep it current. This simulation model of the shipyard production process captures both the essential physical shipbuilding activities and the essential management decision making activities that support the physical production processes. The application consists of two independent submodels, a simulation capability and a results viewer component. The first submodel identifies the overall shipyard facility and manpower resources and the second identifies the construction tasks required to build a ship. The submodels interact to calculate the specific allocation of resources over time necessary to produce the ship. The output generated from the program provides the durations and manhour loadings of elements of the ship construction process based upon dynamic resource availability. The output (unlike other scheduling programs for which durations are typically input and resource allocations an output) provides both schedule and resource use. Task durations are calculated based upon the manhour requirements, the number of people assigned and their productivity. Output generated by the application can assist Program Managers and Design Engineers in analyzing the manhour cost and schedule impacts of alternative designs and construction sequences. The program can also help to quantify the cost and schedule impact of delay and disruption as well as assist in identifying the most effective management actions to overcome such problems.
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Choi, Jin-young, Minkyoung Cho, and Jik-Soo Kim. "Employing Vertical Elasticity for Efficient Big Data Processing in Container-Based Cloud Environments." Applied Sciences 11, no. 13 (July 4, 2021): 6200. http://dx.doi.org/10.3390/app11136200.
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Recently, “Big Data” platform technologies have become crucial for distributed processing of diverse unstructured or semi-structured data as the amount of data generated increases rapidly. In order to effectively manage these Big Data, Cloud Computing has been playing an important role by providing scalable data storage and computing resources for competitive and economical Big Data processing. Accordingly, server virtualization technologies that are the cornerstone of Cloud Computing have attracted a lot of research interests. However, conventional hypervisor-based virtualization can cause performance degradation problems due to its heavily loaded guest operating systems and rigid resource allocations. On the other hand, container-based virtualization technology can provide the same level of service faster with a lightweight capacity by effectively eliminating the guest OS layers. In addition, container-based virtualization enables efficient cloud resource management by dynamically adjusting the allocated computing resources (e.g., CPU and memory) during the runtime through “Vertical Elasticity”. In this paper, we present our practice and experience of employing an adaptive resource utilization scheme for Big Data workloads in container-based cloud environments by leveraging the vertical elasticity of Docker, a representative container-based virtualization technique. We perform extensive experiments running several Big Data workloads on representative Big Data platforms: Apache Hadoop and Spark. During the workload executions, our adaptive resource utilization scheme periodically monitors the resource usage patterns of running containers and dynamically adjusts allocated computing resources that could result in substantial improvements in the overall system throughput.
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Bruwer, Juan Pierre, and Jean-Pierre Rossouw. "The Influence of Perceived Human Resource Risk Factors on Financial Problems Faced by Overberg District Municipalities." Journal of Economics and Behavioral Studies 11, no. 1(J) (March 10, 2019): 48–54. http://dx.doi.org/10.22610/jebs.v11i1(j).2747.
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Abstract: Municipalities are at the forefront of South Africa’s developmental agenda. To assist in the foregoing, the municipalities perform the primary task of service delivery - mandated by the Constitution of South Africa. The efficiency of the municipal service delivery function is depended on municipal funds, as derived from various sources. Apart from these funds, municipalities also receive funding through National and Provincial grant allocations, in addition. Notwithstanding the aforementioned most South African municipalities are experiencing financial problems which may adversely affect their relevant service delivery tasks. A possible contributor to the financial problems of South African municipalities is that of human resources (HR) risk factors. For this study, the emphasis was placed on testing whether perceived HR risk factors had any influence on the financial problems experienced in Overberg District Municipalities. Empirical research was conducted through the means of collecting quantitative primary data from 106 respondents, all of whom had to adhere to relevant delineation criteria. Stemming from the results, it was found that perceived HR risk factors did not have any statistically significant influence on the financial problems experienced in Overberg District Municipalities.
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Pata, Arminda, and Ana Moura. "Applying Metaheuristics to Minimize Work-Related Musculoskeletal Disorders." International Journal of Technology and Human Interaction 14, no. 2 (April 2018): 17–34. http://dx.doi.org/10.4018/ijthi.2018040102.
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This article covers the topic of planning and organization of work, which is one of the biggest problems is to establish the most appropriate allocations between human and technical resources, according to the characteristics that define and characterize each individual. These adjustments to decision-making regarding the characteristics of a new larger workforce is a challenge for human resource managers and researchers working to provide well-being and quality of life improvements for employees. The problem of work-related musculoskeletal disorders, coupled with the aging of the active population, may increase the number of citizens with permanent disabilities. Given the complexity and uniqueness of the problems, a decision support system that uses some metaheuristic approaches is presented. The result is a hybrid approach that gives the best solution according to several parameters defined by the decision-maker. Computational results of real problem instances are presented, proving that in most cases, the optimal solution is achieved.
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Boadu, Richard Okyere, Peter Agyei-Baffour, and Anthony Kwaku Edusei. "Data accuracy and completeness of monthly midwifery returns indicators of Ejisu Juaben Health Directorate of Ghana." International Journal of Public Health Science (IJPHS) 8, no. 1 (December 8, 2018): 106. http://dx.doi.org/10.11591/ijphs.v8i1.15934.
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<span lang="EN-US">The broad range of activities contained in the provision of Primary Health Care (PHC) places a burden on providers to make optimal use of limited resources to achieve maximal health benefit to the population served. All too often, ad hoc decisions and personal preferences guide PHC resource allocations, making accountability for results impossible. Problems constraining Routine Health Information System (RHIS) performance in low-income countries include: poor data quality; limited use of available information; weaknesses in how data are analyzed and poor RHIS management practices. This study sought to investigate these constraints.</span><span> A non-experimental before and after study involving bassline assessment of data accuracy and completeness, application of innovative strategies such as mentoring and coaching of Health Information Officers in data quality improvement process. Coincidentally, the intervention </span><span lang="EN-US">improved both data accuracy and completeness performance significantly among the participating facilities. The outstanding performance may be attributed to management’s new orientation and growing interest towards quality data. Engaging frontline staff in data quality improvement work and provision of regular feedback leads to improvement in data accuracy and completeness. This has implications for decision-making and resource allocation, especially in low-income countries, where the routine health information management system relies heavily on paper work</span><span lang="EN-US">.</span>
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Andani Mohammed, Adam, and Abdallah Mpawenimana Saidi. "Public-Private Partnership: Ghanaian Perspective of Urban Water Supply." International Journal of Engineering & Technology 7, no. 3.18 (August 2, 2018): 49. http://dx.doi.org/10.14419/ijet.v7i3.18.16672.
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In Ghana, tariff of utilities including water is put below cost recovery levels such that government is unable to cope with the challenges in water supply. As such, the government resorts to private partnership to fund the gaps in the urban water supply in the country. The study adopted an integrative multi-stage critical review of relevant literature on public-private partnership in urban water supply. As such articles published between 2006 and 2017 were selected by specific inclusion criteria. Relevant articles on the topic were identified through references and citations. The paper examines the status and trend of water supply, reforms and management of urban water supply in Ghana. The study found problems like poor water supply system, inadequate human and economic resources as well as poor resource allocations and urban water management.
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Kim, Beom-Su, Tae-Eung Sung, and Ki-Il Kim. "An NS-3 Implementation and Experimental Performance Analysis of IEEE 802.15.6 Standard under Different Deployment Scenarios." International Journal of Environmental Research and Public Health 17, no. 11 (June 4, 2020): 4007. http://dx.doi.org/10.3390/ijerph17114007.
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Various simulation studies for wireless body area networks (WBANs) based on the IEEE 802.15.6 standard have recently been carried out. However, most of these studies have applied a simplified model without using any major components specific to IEEE 802.15.6, such as connection-oriented link allocations, inter-WBAN interference mitigation, or a two-hop star topology extension. Thus, such deficiencies can lead to an inaccurate performance analysis. To solve these problems, in this study, we conducted a comprehensive review of the major components of the IEEE 802.15.6 standard and herein present modeling strategies for implementing IEEE 802.15.6 MAC on an NS-3 simulator. In addition, we configured realistic network scenarios for a performance evaluation in terms of throughput, average delay, and power consumption. The simulation results prove that our simulation system provides acceptable levels of performance for various types of medical applications, and can support the latest research topics regarding the dynamic resource allocation, inter-WBAN interference mitigation, and intra-WBAN routing.
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Gross, Jörg, and Carsten K. W. De Dreu. "Individual solutions to shared problems create a modern tragedy of the commons." Science Advances 5, no. 4 (April 2019): eaau7296. http://dx.doi.org/10.1126/sciadv.aau7296.
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Alone and together, climatic changes, population growth, and economic scarcity create shared problems that can be tackled effectively through cooperation and coordination. Perhaps because cooperation is fragile and easily breaks down, societies also provide individual solutions to shared problems, such as privatized healthcare or retirement planning. But how does the availability of individual solutions affect free-riding and the efficient creation of public goods? We confronted groups of individuals with a shared problem that could be solved either individually or collectively. Across different cost-benefit ratios of individually versus collectively solving the shared problem, individuals display a remarkable tendency toward group-independent, individual solutions. This “individualism” leads to inefficient resource allocations and coordination failure. Introducing peer punishment further results in wasteful punishment feuds between “individualists” and “collectivists.” In the presence of individual solutions to shared problems, groups struggle to balance self-reliance and collective efficiency, leading to a “modern tragedy of the commons.”
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Iizuka, Toshiaki, and Hitoshi Shigeoka. "Is Zero a Special Price? Evidence from Child Health Care." American Economic Journal: Applied Economics 14, no. 4 (October 1, 2022): 381–410. http://dx.doi.org/10.1257/app.20210184.
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Do consumers react differently to zero prices? We test the presence of a zero-price effect in child health care and find that a zero price is special as it boosts demand discontinuously. A zero price affects resource allocations by encouraging healthier children to use more services and exacerbates behavioral hazard by increasing inappropriate use of antibiotics. A co-payment, of as small as US$2 per visit, alleviates these problems without substantially increasing financial risk. However, a zero price may be used to boost demand for highly cost-effective treatments. Zero and non-zero prices should be strategically chosen to achieve specific goals. (JEL G22, H75, I11, I13, I18, J13)
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Jarvis, Peter, Shawn Wolfe, Francis Enomoto, Robert Nado, and Maarten Sierhuis. "A Centralized Multi-Agent Negotiation Approach to Collaborative Air Traffic Resource Management Planning." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 2 (July 11, 2010): 1787–92. http://dx.doi.org/10.1609/aaai.v24i2.18814.
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Demand and capacity imbalances in the US national airspace are resolved using traffic management initiatives designed, in current operations, with little collaboration with the airspace users. NASA and its partners have developed a new collaborative concept of operations that requires the users and airspace service provider to work together to choose initiatives that better satisfy the business needs of the users while also ensuring safety to the same standard as today. In this paper, we describe an approach to implementing this concept through a software negotiation framework underpinned by technology developed in the artificial intelligence community. We describe our exploration of peer-to-peer negotiation and how the number of conversation threads and the time sensitivity of offer acceptance led us to a centralized approach. The centralized approach uses hill climbing to evaluate airport slot allocations from a user perspective and a linear programming solver to seek solutions compatible across the user community. Our experiments with full sized problems identify the potential operational benefits as well as limitations, and where future research needs to be focused.
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Jin, Lei, Haiyan Fu, Younggy Kim, Li Wang, Huiyan Cheng, and Guohe Huang. "The α-Representation Inexact T2 Fuzzy Sets Programming Model for Water Resources Management of the Southern Min River Basin under Uncertainty." Symmetry 10, no. 11 (November 2, 2018): 579. http://dx.doi.org/10.3390/sym10110579.
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In this study, a new α-representation of the inexact T2 fuzzy sets programming model (α-RITF2SP) is proposed for water resources planning under uncertainties. The developed model is a hybrid of inexact type-2 (T2) fuzzy sets (FSs) optimal programming based on α-cuts and α-planes (z-slices) algorithms, and both can be used in problems of water resource management without a loss of generality. Therefore, this novel method represents a complementary method to combine both of these algorithms to make inexact T2FSs methods easier and faster for users under uncertainties. This method provides not only a reduction in computations when computing fuzzy functions, but also a map of the primary variable of T2FSs in the secondary membership function. The α-RITF2SP is applied to a real case study of the operation of water resource systems in the southern Min River basin that involves three cities: Xiamen, Zhangzhou, and Quanzhou. The results of the water allocations and benefits have been analyzed. The symptoms of results indicate the higher profit with a higher wastewater treatment rate. It also represents that this model has enhanced abilities and it is relevant and practical as well as can serve as a guide for the development of this regional economy.
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Chen, Min, Songhao Shang, and Wei Li. "Integrated Modeling Approach for Sustainable Land-Water-Food Nexus Management." Agriculture 10, no. 4 (April 2, 2020): 104. http://dx.doi.org/10.3390/agriculture10040104.
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Population growth, rapid urbanization, changing diets, and economic development are among the major driving factors of increased demand for water, food and land. In this study, an integrated model was developed for managing land-water-food nexus. A water footprint-based fuzzy fractional programming (WFFP) is developed for optimizing resource allocations toward sustainable food and water security under the agricultural, food, socioeconomic, and natural resource constraints. By calculating the blue and green water footprint of each crop, optimum food requirements were converted into optimal cropping options. The WFFP method can tackle ratio optimization problems associated with fuzzy information, in which fuzzy possibilistic programming is integrated into a linear fractional programming framework. The method is applied to a case study of the Three (Yangtze-Yellow-Lantsang) Rivers Headwaters Region of China. The results can provide the basis for water and agricultural policies formulation and land-water-food nexus management in the study region.
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Lee, Byungwook, Seungwoo Hwang, Pan-Gyu Kim, Gunwhan Ko, Kiwon Jang, Sangok Kim, Jong-Hwan Kim, et al. "Introduction of the Korea BioData Station (K-BDS) for sharing biological data." Genomics & Informatics 21, no. 1 (March 31, 2023): e12. http://dx.doi.org/10.5808/gi.22073.
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A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.
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Song, Shaoyi, Tingjie Lv, and Xia Chen. "Load Balancing for Future Internet: An Approach Based on Game Theory." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/959782.
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In recent years, countries all over the world consider the future internet as the country’s strategic development directions, so projects about future internet have been launched by these countries. Load balancing algorithms and job allocations are main research problems in areas of resource management of future internet. In this paper, we introduce a load balancing model for future internet. We formulate the static load balancing problem in the model proposed above as noncooperative game among users and cooperative game among processors. Based on this model, we derive a load balancing algorithm for computing center. Finally, we execute the algorithm presented in this paper with another three algorithms for comparison purpose. The advantages of our algorithm are better scalability to the model, improving system performance, and low cost on maintaining system information.
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Dolgov, D. A., and E. H. Durfee. "Resource Allocation Among Agents with MDP-Induced Preferences." Journal of Artificial Intelligence Research 27 (December 26, 2006): 505–49. http://dx.doi.org/10.1613/jair.2102.
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Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources. We present an algorithm that simultaneously solves the resource-allocation and the policy-optimization problems. This allows us to avoid explicitly representing utilities over exponentially many resource bundles, leading to drastic (often exponential) reductions in computational complexity. We then use this algorithm in the context of self-interested agents to design a combinatorial auction for allocating resources. We empirically demonstrate the effectiveness of our approach by showing that it can, in minutes, optimally solve problems for which a straightforward combinatorial resource-allocation technique would require the agents to enumerate up to 2^100 resource bundles and the auctioneer to solve an NP-complete problem with an input of that size.
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Kundapur, Rashmi, Anusha Rashmi, Sunhitha Velamala, Sumit Aggarwal, Kalpita Shringarpure, Rakhal Gaitonde, and Bhavesh Modi. "Assessment of Challenges and Opportunities and Identification of Approaches and Innovations in COVID-19 Pandemic Management by Different States in India: A Qualitative Approach." Journal of Epidemiology and Global Health 12, no. 1 (January 3, 2022): 74–84. http://dx.doi.org/10.1007/s44197-021-00022-4.
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Abstract Objective The primary objective of the study was to compare the challenges in implementing various COVID-19-related public health strategies and activities between the selected high health index and low health index states. The secondary objective was to identify the differently managed mechanisms adopted by the health-care delivery system across the states to maintain their functioning during the COVID-19 pandemic. Setting Eight states were divided into two groups; based on their health index and vulnerability index ranking—Kerala, Maharashtra, Gujarat, and Karnataka in top four (Group 1) and Delhi, Tripura, Rajasthan, and Orissa in bottom four states (Group 2). Results There was lack of private sector involvement in both the groups of the states, more so in Group 2. Although transport-related issues were similar in both groups, lack of provision of vehicles for transport for carrying out various COVID and non-COVID activities seemed to be more prominent in Group 2. More obstacles related to infrastructure were observed in Group 1 states. In terms of innovations, commonalities lay in convergence of multiple departments for monitoring, contact tracing, essential supplies, and transportation. Both groups managed routine health services and fund allocation with nearly equal vigour. Major challenges faced were related to human resource, policy management, transportation, routine health services, data management, and infrastructure. HR-related challenges in top four states included confusion due to frequent change in guidelines, unclear micro-containment, and testing guidelines. Discharge guidelines and SOPs related to home isolation of slum dwellers, inter-departmental cooperation and coordination issues faced in greater proportion in top four states; issues with fund allocation for local needs were faced by the Group 2 states. Innovations implemented to meet hurdles faced during the pandemic could be categorized under heads of ‘human resource’, ‘community actions’, ‘policy management’, ‘inter-departmental coordination’, ‘use of technology and media’, and ‘fund allocations’. There was private–public partnership; use of other human resource for health-care delivery; use of technology for health-care delivery was seen in all states but more so in Group 1 states. Conclusion States with higher health index and lower vulnerability index, i.e., Group 1 states faced fewer challenges than those in Group 2. Innovative measures taken at local level to tackle problems posed by the pandemic were unique to the situations presented to them and helped control the disease as effectively as they could.
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M, Sumathi, Niranjana B, Akshaya C, Ajitha M, and Bhavadharanee M. "Round Robin Based Efficient Resource Allocation and Utilization in an Organization." International Research Journal of Multidisciplinary Technovation 2, no. 2 (March 30, 2020): 16–22. http://dx.doi.org/10.34256/irjmt2023.
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In an organization, resource allocation to a request is a complex task. Traditionally, resource allocation is done through manually with high time consumption. Similarly, collision is occurring for allocating a single resource to multiple requests. Thus, leads to complex problems and slow-down the working process. The existing resource allocation technique, allocate resources continuously to a specific request and omit another request. This kind of allocation technique also leads to lots of critical issues. That is the non-allocated process never gets a resource. To overcome these issues, the Round Robin based Resource allocation and Utilization technique is proposed in this work. The Round Robin technique allocates resources to the request in an efficient with equal priority. Similarly, the proposed technique reduces collision and takes less time for mapping a resource with a request. The experimental results shows improved accuracy than the traditional resource allocation technique.
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Sathish, Kuppani, and A. Rama Mohan Reddy. "Resource Allocation Mechanism with New Models for Grid Environment." International Journal of Grid and High Performance Computing 5, no. 2 (April 2013): 1–26. http://dx.doi.org/10.4018/jghpc.2013040101.
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Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.
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Fahturizal, Indra Maulana. "A Systematic Literature Review of Implementation Quality Control Circle in Manufacturing and Services Industries." IJIEM - Indonesian Journal of Industrial Engineering and Management 1, no. 3 (October 31, 2020): 144. http://dx.doi.org/10.22441/ijiem.v1i3.10232.
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A quality control circle (QCC) is a group of workers who do the same or similar work, who meet regularly to identify, analyze, and solve work-related problems. This paper is a literature review on the role of QCC in enhancing work safety in the manufacturing and services industry. This paper explores how quality control circles (QCC) enhance work safety in a firm manufacturing environment. The method used is reviewing inquiry papers that use QCC consistently. The findings of this study can be considered as a roadmap for the successful implementation of QCC in all industries. The results obtained contribute to advances on QCC research for identifying that the concepts sustainable of quality manufacturing and service industries can enable quality to have positive impacts on all the sustainability dimensions in an integrated way, and also supporting the implementation of the quality plan in the following aspects: continues improvement; quality management; management improvement. In essence, these research results will help management make crucial decisions and resource allocations required to make the QCC implementation a succes.
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Djafar, Fazrin. "TINJAUAN PENJADWALAN WAKTU DAN TENGA KERJA PADA PEKERJAAN REHABILITASI DAN REKONSTRUKSI BERAT JEMBATAN SOKLAT." Jurnal Teknik Sipil, Arsitek, Perencanaan Wilayah (J-TSIAP) 1, no. 1 (January 28, 2022): 1–20. http://dx.doi.org/10.37971/j-tsiap.v1i1.49.
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Projects in the construction industry are temporary activities that last for a limited period of time, with certain resource allocations and aim to carry out tasks whose targets have been clearly outlined. In connection with this, a project planning method is needed which is expected to coordinate various activities in a job so that the project can be completed within the specified timeframe. The purpose of this research is to plan the most appropriate time, optimize the use of labor for each work item and find out the obstacles faced in the implementation of the Soklat Bridge Heavy Rehabilitation & Reconstruction project. The method that can be used in overcoming problems in time and manpower planning is to use the Crash Program, and use CPM (critical path method) or commonly known as Network Planning Diagram. This method is made to get the normal project life and the critical path that clarifies what activities will be shortened. From the results of the crash program experiment for 3 (three) times, the shortest work duration was 220 calendar days, namely the 3rd crash program.