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Artykuły w czasopismach na temat „Optimal allocation of resources under uncertainty”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Jaka, Zgajnar, and Kavcic Stane. "Optimal allocation of production resources under uncertainty: Application of the muliticriteria approach." Agricultural Economics (Zemědělská ekonomika) 62, No. 12 (2016): 556–65. http://dx.doi.org/10.17221/238/2015-agricecon.

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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 (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 re

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Balseiro, Santiago, Christian Kroer, and Rachitesh Kumar. "Online Resource Allocation under Horizon Uncertainty." ACM SIGMETRICS Performance Evaluation Review 51, no. 1 (2023): 63–64. http://dx.doi.org/10.1145/3606376.3593559.

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We study stochastic online resource allocation: a decision maker needs to allocate limited resources to stochastically-generated sequentially-arriving requests in order to maximize reward. At each time step, requests are drawn independently from a distribution that is unknown to the decision maker. Online resource allocation and its special cases have been studied extensively in the past, but prior results crucially and universally rely on the strong assumption that the total number of requests (the horizon) is known to the decision maker in advance. In many applications, such as revenue manag

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4

Hosseini, Hadi, Jesse Hoey, and Robin Cohen. "A Market-Based Coordination Mechanism for Resource Planning Under Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 2427–28. http://dx.doi.org/10.1609/aaai.v26i1.8428.

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Multiagent Resource Allocation (MARA) distributes a set of resources among a set of intelligent agents in order to respect the preferences of the agents and to maximize some measure of global utility, which may include minimizing total costs or maximizing total return. We are interested in MARA solutions that provide optimal or close-to-optimal allocation of resources in terms of maximizing a global welfare function with low communication and computation cost, with respect to the priority of agents, and temporal dependencies between resources. We propose an MDP approach for resource planning i

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5

Moslehi, Mahsa, Ram Rajagopal, and Felipe P. J. de Barros. "Optimal allocation of computational resources in hydrogeological models under uncertainty." Advances in Water Resources 83 (September 2015): 299–309. http://dx.doi.org/10.1016/j.advwatres.2015.06.014.

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Singh, Ajay. "Optimal Allocation of Resources for Increasing Farm Revenue under Hydrological Uncertainty." Water Resources Management 30, no. 7 (2016): 2569–80. http://dx.doi.org/10.1007/s11269-016-1306-x.

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7

Aziz, Haris, Peter Biro, Ronald De Haan, and Baharak Rastegari. "Pareto Optimal Allocation under Compact Uncertain Preferences." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1740–47. http://dx.doi.org/10.1609/aaai.v33i01.33011740.

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The assignment problem is one of the most well-studied settings in multi-agent resource allocation. Aziz, de Haan, and Rastegari (2017) considered this problem with the additional feature that agents’ preferences involve uncertainty. In particular, they considered two uncertainty models neither of which is necessarily compact. In this paper, we focus on three uncertain preferences models whose size is polynomial in the number of agents and items. We consider several interesting computational questions with regard to Pareto optimal assignments. We also present some general characterization and

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8

Gu, Jingda, Ying Li, Li Ju, Xianghong Zuo, and Yuheng Zhao. "Flexible resource optimal allocation strategy for distribution system resilience improvement under failure uncertainty." Journal of Physics: Conference Series 2703, no. 1 (2024): 012030. http://dx.doi.org/10.1088/1742-6596/2703/1/012030.

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Abstract Extreme disasters have led to huge economical losses to the power system and posed a huge threat to the stable supply of electric energy. Especially for traditional passive distribution network systems, failures of transmission lines, towers, and other equipment will cause large-scale power outages and huge economic losses. In order to improve the ability of the power system to deal with extreme weather disasters, this paper proposes an optimal configuration strategy for the distribution network backup power resource oriented to resilience improvement, considering the uncertainty of e

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Su, Xuan, Wenquan Dong, Jingyu Lu, Chen Chen, and Weixi Ji. "Dynamic Allocation of Manufacturing Resources in IoT Job Shop Considering Machine State Transfer and Carbon Emission." Sustainability 14, no. 23 (2022): 16194. http://dx.doi.org/10.3390/su142316194.

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The optimal allocation of manufacturing resources plays an essential role in the production process. However, most of the existing resource allocation methods are designed for standard cases, lacking a dynamic optimal allocation framework for resources that can guide actual production. Therefore, this paper proposes a dynamic allocation method for discrete job shop resources in the Internet of Things (IoT), which considers the uncertainty of machine states, and carbon emission. First, a data-driven job shop resource status monitoring framework under the IoT environment is proposed, considering

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10

Fu, Yinhuan, Mo Li, and Ping Guo. "Optimal Allocation of Water Resources Model for Different Growth Stages of Crops under Uncertainty." Journal of Irrigation and Drainage Engineering 140, no. 6 (2014): 05014003. http://dx.doi.org/10.1061/(asce)ir.1943-4774.0000724.

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Gao, Xueping, Yinzhu Liu, and Bowen Sun. "A joint-probabilistic programming method for water resources optimal allocation under uncertainty: a case study in the Beiyun River, China." Journal of Hydroinformatics 20, no. 2 (2017): 393–409. http://dx.doi.org/10.2166/hydro.2017.032.

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Abstract In recent years, the lower reaches of the Beiyun River have suffered from growing water resource shortages due to the reduction of upstream water resource and drying up of the stream channel. More reasonable and scientifically based water allocation plans should be developed and implemented; however, uncertainties exist regarding the determination of water supply availability and spillage of extra water. To assess and manage regional water shortage, the combined effects of multiple water supply sources as well as the joint probability of typical events should be considered. The joint

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Xu, Fang, Mengfan Yan, Lun Wang, and Shaojian Qu. "The Robust Emergency Medical Facilities Location-Allocation Models under Uncertain Environment: A Hybrid Approach." Sustainability 15, no. 1 (2022): 624. http://dx.doi.org/10.3390/su15010624.

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In emergency medical facilities location, the hierarchical diagnosis and treatment system plays an obvious role in the rational allocation of medical resources and improving the use efficiency of medical resources. However, few studies have investigated the operational mechanism of hierarchical medical systems in uncertain environments. To address this research gap, this paper proposes a hybrid approach for emergency medical facilities’ location-allocation. In the first stage, in order to concentrate on the utilization of medical resources, we choose alternative facility points from the whole

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Wang, Hang, Chenglong Zhang, and Ping Guo. "An Interval Quadratic Fuzzy Dependent-Chance Programming Model for Optimal Irrigation Water Allocation under Uncertainty." Water 10, no. 6 (2018): 684. http://dx.doi.org/10.3390/w10060684.

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In this study, an interval quadratic fuzzy dependent-chance programming (IQFDCP) model is proposed for irrigation water management under uncertainty. This model can deal with the interval and fuzzy uncertainties encountered in optimal irrigation water resources allocation problems. It takes optimal credibility level of system revenue as the objective function for addressing the conflict between maximum system revenue and the credibility level of the system revenue. By providing three scenarios of precipitation, the optimal solutions are obtained. The developed model is applied to a case study

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Zhang, Yunquan, and Peiling Yang. "Agricultural Productive Carrying Capacity Improve and Water Optimal Allocation under Uncertainty Based on Remote Sensing Data in Lancang County, Southwest China." Water 14, no. 22 (2022): 3641. http://dx.doi.org/10.3390/w14223641.

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Through the reasonable calculation of water resources, evaluating the irrigation carrying capacity of farmland under the constraints of water resources is crucial for optimizing the spatial distribution of agricultural production and ecology and rationally adjusting the scale of agricultural production. This paper proposes an optimization framework based on Type 2 fuzzy chance-constrained programming (T2FCCP) to solve the problem of regional water resources optimal allocation and evaluation of farmland irrigation carrying capacity under uncertain conditions. To illustrate the applicability of

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15

Gang, Jun, Jiuping Xu, and Yinfeng Xu. "Multiproject Resources Allocation Model under Fuzzy Random Environment and Its Application to Industrial Equipment Installation Engineering." Journal of Applied Mathematics 2013 (2013): 1–19. http://dx.doi.org/10.1155/2013/818731.

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This paper focuses on a multiproject resource allocation problem in a bilevel organization. To solve this problem, a bilevel multiproject resource allocation model under a fuzzy random environment is proposed. Two levels of decision makers are considered in the model. On the upper level, the company manager aims to allocate the company's resources to multiple projects to achieve the lowest cost, which include resource costs and a tardiness penalty. On the lower level, each project manager attempts to schedule their resource-constrained project, with minimization of project duration as the main

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Slavianov, A. "Analysis and practical application of resource distribution models." Bulletin of Science and Practice 4, no. 9 (2018): 228–44. https://doi.org/10.5281/zenodo.1418777.

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Russia, despite external constraints, continues to implement various social, scientific, defence and other programs that require a significant number of diverse resources. From their rational distribution depends on the efficiency of enterprises and the effectiveness of projects. The article is devoted to the optimization of resources using the apparatus of mathematical modelling. In this paper, we give a brief overview of mathematical methods for optimizing resources. Particular attention is paid to models studied by the Lagrange method and to the methods of game theory. In the paper, two opt

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17

AKAN, Ercan, and Güler ALKAN. "Optimizing Shipbuilding Production Project Scheduling Under Resource Constraints Using Genetic Algorithms and Fuzzy Sets." Marine Science and Technology Bulletin 12, no. 3 (2023): 380–401. http://dx.doi.org/10.33714/masteb.1324266.

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This study explores the application of Genetic Algorithms (GA) in optimizing shipbuilding production processes in the presence of uncertain environments. The research addresses two key aspects: firstly, the integration of GA RCPSP (Resource-Constrained Project Scheduling Problem) with techniques for managing uncertainty in shipbuilding production; and secondly, the analysis of Pareto optimal solutions generated by GA to achieve optimal scheduling in the shipbuilding context. The proposed framework aims to minimize project completion time and maximize resource utilization by incorporating proba

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Kazeminajafabadi, Armita, and Mahdi Imani. "Optimal Joint Defense and Monitoring for Networks Security under Uncertainty: A POMDP-Based Approach." IET Information Security 2024 (May 27, 2024): 1–20. http://dx.doi.org/10.1049/2024/7966713.

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The increasing interconnectivity in our infrastructure poses a significant security challenge, with external threats having the potential to penetrate and propagate throughout the network. Bayesian attack graphs have proven to be effective in capturing the propagation of attacks in complex interconnected networks. However, most existing security approaches fail to systematically account for the limitation of resources and uncertainty arising from the complexity of attacks and possible undetected compromises. To address these challenges, this paper proposes a partially observable Markov decisio

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Jin, Lei, Haiyan Fu, Younggy Kim, Jiangxue Long, and Guohe Huang. "A Bi-Objective Pseudo-Interval T2 Linear Programming Approach and Its Application to Water Resources Management Under Uncertainty." Water 10, no. 11 (2018): 1545. http://dx.doi.org/10.3390/w10111545.

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In realistic water resource planning, fuzzy constraints can be violated but still allowed to certain acceptance degrees. To address this issue, in this study, a bi-objective pseudo-interval type 2 (T2) linear programming approach with a ranking order relation between the intervals is proposed for water system allocation. This developed approach can transform normal T2 fuzzy sets, including both trapezoidal and triangular types, into the bi-objective linear programming approach solved with the proposed algorithm with mathematical rigor, which improves the flexibility of the decision supports. T

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Golovin, Daniel, Andreas Krause, Beth Gardner, Sarah Converse, and Steve Morey. "Dynamic Resource Allocation in Conservation Planning." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1331–36. http://dx.doi.org/10.1609/aaai.v25i1.7795.

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Consider the problem of protecting endangered species by selecting patches of land to be used for conservation purposes. Typically, the availability of patches changes over time, and recommendations must be made dynamically. This is a challenging prototypical example of a sequential optimization problem under uncertainty in computational sustainability. Existing techniques do not scale to problems of realistic size. In this paper, we develop an efficient algorithm for adaptively making recommendations for dynamic conservation planning, and prove that it obtains near-optimal performance. We fur

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21

Xu, Wei, and Yinyun Yu. "Optimal Allocation Method of Discrete Manufacturing Resources for Demand Coordination between Suppliers and Customers in a Fuzzy Environment." Complexity 2018 (August 29, 2018): 1–18. http://dx.doi.org/10.1155/2018/1410957.

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Discrete manufacturing products are often assembled from multiple parts through a series of discrete processes. How to effectively configure resources in a discrete manufacturing environment is an important research topic worthy of attention. Based on an in-depth analysis of the discrete manufacturing operation model and the manufacturing resource allocation process, this paper fully considers the uncertainty factors of the manufacturing resource customers and the interests of the manufacturing resource suppliers and proposes a bilevel planning model under a fuzzy environment that comprehensiv

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Khalturin, Roman A., Roman O. Sudorgin, and Nikita S. Akinshin. "Theoretical justification of the model of search for optimal solutions in complex resource management systems." International Journal of Advanced Studies 15, no. 1 (2025): 214–33. https://doi.org/10.12731/2227-930x-2025-15-1-356.

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Background.The relevance of the article is determined by the complexity and dynamism of resource allocation systems (RR systems), which include numerous time-varying elements, both within the system and in the external environment, and require organization into hierarchical subsystems. The uncertainty of the informational component and influencing factors necessitates the use of effective analytical tools based on decision theory under uncertainty for the objective assessment and management of such systems. Purpose. To develop a set of mathematical models for finding optimal solutions in compl

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23

Li, Y. P., G. H. Huang, and S. L. Nie. "A robust interval-based minimax-regret analysis approach for the identification of optimal water-resources-allocation strategies under uncertainty." Resources, Conservation and Recycling 54, no. 2 (2009): 86–96. http://dx.doi.org/10.1016/j.resconrec.2009.06.011.

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Migranov, Ayrat. "Modified Heuristic Task Allocation Algorithms for Mobile Robot Teams under Uncertainty." Informatics and Automation 24, no. 3 (2025): 884–913. https://doi.org/10.15622/ia.24.3.6.

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This study addresses the problem of task allocation among groups of mobile robots under conditions of parametric and stochastic uncertainty arising from sensor errors, environmental non-stationarity, and limited information about controlled objects. The primary objective is to adapt previously developed heuristic algorithms to real-world conditions, where sensor inaccuracies and incomplete knowledge of the environment are present. Three baseline approaches are considered: the ant colony algorithm, the Hopfield neural network, and the genetic algorithm. Each method is enhanced with specific mod

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Wang, Feiyue, Ziling Xie, Hui Liu, Zhongwei Pei, and Dingli Liu. "Multiobjective Emergency Resource Allocation under the Natural Disaster Chain with Path Planning." International Journal of Environmental Research and Public Health 19, no. 13 (2022): 7876. http://dx.doi.org/10.3390/ijerph19137876.

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Public safety and health cannot be secured without the comprehensive recognition of characteristics and reliable emergency response schemes under the disaster chain. Distinct from emergency resource allocation that focuses primarily on a single disaster, dynamic response, periodic supply, and assisted decision-making are necessary. Therefore, we propose a multiobjective emergency resource allocation model considering uncertainty under the natural disaster chain. Resource allocation was creatively combined with path planning through the proposed multiobjective cellular genetic algorithm (MOCGA)

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Grotzinger, Stephen, and Douglas Cooper. "Selecting a Cost-Effective Number of Samples To Use at Preselected Locations." Journal of the IEST 35, no. 1 (1992): 41–49. http://dx.doi.org/10.17764/jiet.2.35.1.m288x061848r4235.

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In the control of environmental contaminants, it is often useful to sample at preselected locations to determine concentrations and their means. These locations might be on a surface, throughout a room, or outdoors. Applications include air and water pollution control, industrial hygiene, and contamination control in industry. Contamination is a major cause of yield and reliability losses in the microelectronics industry. Sampling the cleanroom environment or sampling the product surfaces can help diagnose and prevent contamination problems, but sampling is becoming increasingly expensive. One

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Nyiwul, Linus. "Epidemic Control and Resource Allocation: Approaches and Implications for the Management of COVID-19." Studies in Microeconomics 9, no. 2 (2021): 283–305. http://dx.doi.org/10.1177/23210222211053751.

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The experience with COVID-19 underscores a classic public policy choice problem: how should policymakers determine how to allocate constrained budgets, limited equipment, under-resourced hospitals and stretched personnel to limit the spread of the virus. This article presents an overview of the general literature on resource allocation in epidemics and assess how it informs our understanding of COVID-19. We highlight the peculiarities of the pandemic that call for a rethinking of existing approaches to resource allocation. In particular, we analyse how the experience of COVID-19 informs our un

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Ahmad, Firoz, Shafiq Ahmad, Mazen Zaindin, and Ahmad Yusuf Adhami. "A Robust Neutrosophic Modeling and Optimization Approach for Integrated Energy-Food-Water Security Nexus Management under Uncertainty." Water 13, no. 2 (2021): 121. http://dx.doi.org/10.3390/w13020121.

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Natural resources are a boon for human beings, and their conservation for future uses is indispensable. Most importantly, energy-food-water security (EFWS) nexus management is the utmost need of our time. An effective managerial policy for the current distribution and conservation to meet future demand is necessary and challenging. Thus, this paper investigates an interconnected and dynamic EFWS nexus optimization model by considering the socio-economic and environmental objectives with the optimal energy supply, electricity conversion, food production, water resources allocation, and CO2 emis

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Ahmad, Firoz, Shafiq Ahmad, Mazen Zaindin, and Ahmad Yusuf Adhami. "A Robust Neutrosophic Modeling and Optimization Approach for Integrated Energy-Food-Water Security Nexus Management under Uncertainty." Water 13, no. 2 (2021): 121. http://dx.doi.org/10.3390/w13020121.

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Natural resources are a boon for human beings, and their conservation for future uses is indispensable. Most importantly, energy-food-water security (EFWS) nexus management is the utmost need of our time. An effective managerial policy for the current distribution and conservation to meet future demand is necessary and challenging. Thus, this paper investigates an interconnected and dynamic EFWS nexus optimization model by considering the socio-economic and environmental objectives with the optimal energy supply, electricity conversion, food production, water resources allocation, and CO2 emis

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Mobley, William, Kayode O. Atoba, and Wesley E. Highfield. "Uncertainty in Flood Mitigation Practices: Assessing the Economic Benefits of Property Acquisition and Elevation in Flood-Prone Communities." Sustainability 12, no. 5 (2020): 2098. http://dx.doi.org/10.3390/su12052098.

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Adopting effective flood mitigation practices for repetitive flood events in the United States continues to play a prominent role in preventing future damage and fostering resilience to residential flooding. Two common mitigation practices for reducing residential flood risk consist of raising an existing structure to or above base flood elevation (BFE) and acquiring chronically damaged properties in flood prone areas and restoring them back to serve their natural functions as green open spaces. However, due to data accuracy limitations, decision makers are faced with the challenge of identify

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Ajofoyinbo, Abayomi M., and Kehinde O. Orolu. "Optimal Allocation of Radio Resource in Cellular LTE Downlink Based on Truncated Dynamic Programming under Uncertainty." International Journal of Communications, Network and System Sciences 05, no. 02 (2012): 111–20. http://dx.doi.org/10.4236/ijcns.2012.52015.

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Garcia, Christopher, Ghaith Rabadi, and Femida Handy. "Dynamic resource allocation and coordination for high-load crisis volunteer management." Journal of Humanitarian Logistics and Supply Chain Management 8, no. 4 (2018): 533–56. http://dx.doi.org/10.1108/jhlscm-02-2018-0019.

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Purpose Every year volunteers play a crucial role in disaster responses around the world. Volunteer management is known to be more complex than managing a paid workforce, and this is only made worse by the uncertainty of rapidly changing conditions of crisis scenarios. The purpose of this paper is to address the critical problem of assigning tasks to volunteers and other renewable and non-renewable resources simultaneously, particularly under high-load conditions. These conditions are described by a significant mismatch between available volunteer resources and demands or by frequent changes i

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Zayas-Cabán, Gabriel, and Hyun-Soo Ahn. "DYNAMIC CONTROL OF A SINGLE-SERVER SYSTEM WHEN JOBS CHANGE STATUS." Probability in the Engineering and Informational Sciences 32, no. 3 (2017): 353–95. http://dx.doi.org/10.1017/s0269964817000213.

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From health care to maintenance shops, many systems must contend with allocating resources to customers or jobs whose initial service requirements or costs change when they wait too long. We present a new queueing model for this scenario and use a Markov decision process formulation to analyze assignment policies that minimize holding costs. We show that the classic cμ rule is generally not optimal when service or cost requirements can change. Even for a two-class customer model where a class 1 task becomes a class 2 task upon waiting, we show that additional orderings of the service rates are

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Zhao, Yun, Rui Zhang, Heping Shu, et al. "Study on Optimal Allocation of Water Resources Based on Uncertain Multi-Objective Fuzzy Model: A Case of Pingliang City, China." Water 16, no. 15 (2024): 2099. http://dx.doi.org/10.3390/w16152099.

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Water shortages are serious in northwest China due to the level of social and economic development, engineering, resource shortages, and other factors being restricted, so the conflict between supply and demand for water resources is prominent in different regions and different water use sectors. Therefore, Pingliang City was selected as the research object in this study. The membership function was introduced, and an interactive algorithm to correct model parameters based on the fairness constraint was derived. An uncertain multi-objective fuzzy programming model was also established. The res

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Mohamed, Ali, Alkhalaf, Senjyu, and Hemeida. "Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm." Sustainability 11, no. 23 (2019): 6550. http://dx.doi.org/10.3390/su11236550.

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This article offers a multi-objective framework for an optimal mix of different types of distributed energy resources (DERs) under different load models. Many renewable and non-renewable energy resources like photovoltaic system (PV), micro-turbine (MT), fuel cell (FC), and wind turbine system (WT) are incorporated in a grid-connected hybrid power system to supply energy demand. The main aim of this article is to maximize environmental, technical, and economic benefits by minimizing various objective functions such as the annual cost, power loss and greenhouse gas emission subject to different

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Румянцева, А. А., А. Ю. Береснева, and М. М. Федорова. "Solving the problem of resource allocation using dynamic programming." Экономика и предпринимательство, no. 9(146) (January 22, 2023): 1323–26. http://dx.doi.org/10.34925/eip.2022.146.9.264.

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Динамическое программирование является одним из основных направлений современной математической теории управления. Сущность подхода динамического программирования состоит в том, что конкретную задачу управления «погружают» в более широкий класс задач, которые характеризуются рядом параметров; затем с помощью «принципа оптимальности» определяется основное рекуррентное соотношение, связывающее задачи этого класса. При условии выполнения некоторых предположений, находится решение широкого класса задач, а решение конкретной задачи получается, как частный случай. Метод динамического программировани

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LUBASHEVSKIY, VASILY, TARO KANNO, and KAZUO FURUTA. "RESOURCE REDISTRIBUTION METHOD FOR SHORT-TERM RECOVERY OF SOCIETY AFTER LARGE-SCALE DISASTERS." Advances in Complex Systems 17, no. 05 (2014): 1450026. http://dx.doi.org/10.1142/s021952591450026x.

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Recovery of society after a large-scale disaster generally consists of two phases, short- and long-term recoveries. The main goal of the short-term recovery is to bounce the damaged system back to the operating standards enabling residents in damaged cities to survive, and fast supply with vital resources to them is one of its important elements. We propose a general principle by which the required redistribution of vital resources between the affected and neighboring cities can be efficiently implemented. The short-term recovery is a rescuer operation where uncertainty in evaluating the state

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Alam, Muhammad Morshed, Sayma Akter Trina, Tamim Hossain, Shafin Mahmood, Md Sanim Ahmed, and Muhammad Yeasir Arafat. "Variations in Multi-Agent Actor–Critic Frameworks for Joint Optimizations in UAV Swarm Networks: Recent Evolution, Challenges, and Directions." Drones 9, no. 2 (2025): 153. https://doi.org/10.3390/drones9020153.

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Autonomous unmanned aerial vehicle (UAV) swarm networks (UAVSNs) can efficiently perform surveillance, connectivity, computing, and energy transfer services for ground users (GUs). These missions require trajectory planning, UAV-GUs association, task offloading, next-hop selection, and resource allocation, including transmit power, bandwidth, timeslots, caching, and computing resources, to enhance network performance. Owing to the highly dynamic topology, limited resources, stringent quality of service requirements, and lack of global knowledge, optimizing network performance in UAVSNs is very

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Hao, Ning, Peixuan Sun, Luze Yang, Yu Qiu, Yingzi Chen, and Wenjin Zhao. "Optimal Allocation of Water Resources and Eco-Compensation Mechanism Model Based on the Interval-Fuzzy Two-Stage Stochastic Programming Method for Tingjiang River." International Journal of Environmental Research and Public Health 19, no. 1 (2021): 149. http://dx.doi.org/10.3390/ijerph19010149.

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In this work, based on the upper line of water resources utilization and the bottom line of water environmental quality of “Three Lines, Single Project”, a fuzzy optimization method was introduced into the Tingjiang River water resources optimal allocation and eco-compensation mechanism model, which is based on the interval two-stage (ITS) stochastic programming method. In addition, a Tingjiang River water resources allocation and eco-compensation mechanism model based on the interval fuzzy two-stage (IFTS) optimization method was also constructed. The objective functions of both models were t

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Kheirkhah, Ali Reza, Carlos Frederico Meschini Almeida, Nelson Kagan, and Jonatas Boas Leite. "Optimal Probabilistic Allocation of Photovoltaic Distributed Generation: Proposing a Scenario-Based Stochastic Programming Model." Energies 16, no. 21 (2023): 7261. http://dx.doi.org/10.3390/en16217261.

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The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minim

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Ali, Ziad M., Ibrahim Mohamed Diaaeldin, Shady H. E. Abdel Aleem, Ahmed El-Rafei, Almoataz Y. Abdelaziz, and Francisco Jurado. "Scenario-Based Network Reconfiguration and Renewable Energy Resources Integration in Large-Scale Distribution Systems Considering Parameters Uncertainty." Mathematics 9, no. 1 (2020): 26. http://dx.doi.org/10.3390/math9010026.

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Renewable energy integration has been recently promoted by many countries as a cleaner alternative to fossil fuels. In many research works, the optimal allocation of distributed generations (DGs) has been modeled mathematically as a DG injecting power without considering its intermittent nature. In this work, a novel probabilistic bilevel multi-objective nonlinear programming optimization problem is formulated to maximize the penetration of renewable distributed generations via distribution network reconfiguration while ensuring the thermal line and voltage limits. Moreover, solar, wind, and l

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Park, Jungho, Hadi El-Amine, and Nevin Mutlu. "An Exact Algorithm for Large-Scale Continuous Nonlinear Resource Allocation Problems with Minimax Regret Objectives." INFORMS Journal on Computing 33, no. 3 (2021): 1213–28. http://dx.doi.org/10.1287/ijoc.2020.0999.

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We study a large-scale resource allocation problem with a convex, separable, not necessarily differentiable objective function that includes uncertain parameters falling under an interval uncertainty set, considering a set of deterministic constraints. We devise an exact algorithm to solve the minimax regret formulation of this problem, which is NP-hard, and we show that the proposed Benders-type decomposition algorithm converges to an [Formula: see text]-optimal solution in finite time. We evaluate the performance of the proposed algorithm via an extensive computational study, and our results

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Hevia, Constantino, Norman V. Loayza, and Claudia Meza-Cuadra. "Industrial Policies vs Public Goods under Asymmetric Information." Economia 46, no. 91 (2023): 39–52. http://dx.doi.org/10.18800/economia.202301.003.

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This paper presents an analytical framework that captures the informational problems and tradeoffs that policy makers face when choosing between public goods (e.g., infrastructure) and industrial policies (e.g., firm or sector-specific subsidies). The paper first provides a discussion of the literature on industrial policies. It then presents an illustrative model, where the economy consists of a set of firms that vary by productivity and a government that can support firms through general or targeted expenditures. The paper examines the cases of full and asymmetric information on firm product

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Huang, Yurui, Minjing Zhao, and Kangwei Zhao. "Analysis of Agricultural Resource Allocation and Market Response Based on Different Crop Yield Data." Highlights in Business, Economics and Management 51 (February 27, 2025): 39–47. https://doi.org/10.54097/kfppk689.

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In the context of promoting rural revitalization, a crop planting strategy was developed for the diverse farmland and rich crop varieties in North China, aiming to adapt to market demand, and land characteristics, and respond to market dynamics and uncertainty. This paper developed a comprehensive planting strategy model that not only provides scientific decision support but also has flexibility and applicability, allowing for adjustments based on actual conditions. The model used the ideal point method and the maximum-minimum model to solve the dual-objective planning problem and evaluated th

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Zhang, Hao, Wei He, Haihong Xu, et al. "Investigating a Water Resource Allocation Model by Using Interval Fuzzy Two-Stage Robust Planning for the Yinma River Basin, Jilin Province, China." Water 13, no. 21 (2021): 2974. http://dx.doi.org/10.3390/w13212974.

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This study introduces a fuzzy method to construct the interval fuzzy two-stage robust (ITSFR) water resource optimal allocation model based on the interval two-stage robust (ITSR) water resource optimal allocation model. Optimal economic benefit was considered the objective function, and the number of available water resources, sewage treatment capacity, reuse water treatment capacity, and total pollutant control were considered as the constraints. Under three five-year planning periods (2015–2020, 2020–2025, and 2025–2030) and according to the allocation levels of dry, flat, and abundant wate

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Karpets, Eleonora. "Simulation Modeling as a Tool for Resource Management in Conditions of Information Uncertainty." Cybernetics and Computer Technologies, no. 4 (December 18, 2024): 43–49. https://doi.org/10.34229/2707-451x.24.4.4.

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Introduction. The article proposes the use of the probabilistic-automaton modeling method to optimize the allocation of financial resources from the Compulsory State Social Insurance Fund for Unemployment (hereinafter referred to as the Fund). This approach aims to increase funding in favor of active labor market policy measures. Objective. The purpose of the proposed methods is to identify approaches that, under conditions of macroeco-nomic decline, enable the exploration of financial support scenarios for the national labor market. By optimally utilizing resources, this would contribute to r

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Wang, Yi, Yu-yuan Li, and Wen Guo. "Original Article .The Optimal Allocation of Investment between Antivirals and Vaccines for Influenza Pandemic Preparedness Planning." Infection International 1, no. 1 (2012): 25–33. http://dx.doi.org/10.1515/ii-2017-0004.

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Abstract Objectives To investigate that given a fixed amount of financial resources, what is the optimal combination of vaccine and antiviral stockpiles in terms of minimizing the attack rate. Methods Mathematic modeling was used to simulate the dynamics that with fixed influenza pandemic budget. Different budget conditions were observed if the combination changed. Framework between vaccines and antivirals was introduced by taking into account the uncertainty in vaccine and antiviral efficacy. Results Given a fixed budget, different budget allocations between vaccines and antivirals stockpile

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Deng, Qiang. "A Combined OCBA–AIC Method for Stochastic Variable Selection in Data Envelopment Analysis." Mathematics 12, no. 18 (2024): 2913. http://dx.doi.org/10.3390/math12182913.

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This study introduces a novel approach to enhance variable selection in Data Envelopment Analysis (DEA), especially in stochastic environments where efficiency estimation is inherently complex. To address these challenges, we propose a game cross-DEA model to refine efficiency estimation. Additionally, we integrate the Akaike Information Criterion (AIC) with the Optimal Computing Budget Allocation (OCBA) technique, creating a hybrid method named OCBA–AIC. This innovative method efficiently allocates computational resources for stochastic variable selection. Our numerical analysis indicates tha

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Boutmir, Yassine, Rachid Bannari, Achraf Touil, Mouhsene Fri, and Othmane Benmoussa. "Dependent-Chance Goal Programming for Sustainable Supply Chain Design: A Reinforcement Learning-Enhanced Salp Swarm Approach." Sustainability 17, no. 13 (2025): 6079. https://doi.org/10.3390/su17136079.

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The Sustainable Supply Chain Network Design Problem (SSCNDP) is to determine the optimal network configuration and resource allocation that achieve the trade-off among economic, environmental, social, and resilience objectives. The Sustainable Supply Chain Network Design Problem (SSCNDP) involves determining the optimal network configuration and resource allocation that allows trade-off among economic, environmental, social, and resilience objectives. This paper addresses the SSCNDP under hybrid uncertainty, which combines objective randomness got from historical data, and subjective beliefs i

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Luo, Junren, Xiang Ji, Wei Gao, Wanpeng Zhang, and Shaofei Chen. "Goal Recognition Control under Network Interdiction Using a Privacy Information Metric." Symmetry 11, no. 8 (2019): 1059. http://dx.doi.org/10.3390/sym11081059.

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Goal recognition (GR) is a method of inferring the goals of other agents, which enables humans or AI agents to proactively make response plans. Goal recognition design (GRD) has been proposed to deliberately redesign the underlying environment to accelerate goal recognition. Along with the GR and GRD problems, in this paper, we start by introducing the goal recognition control (GRC) problem under network interdiction, which focuses on controlling the goal recognition process. When the observer attempts to facilitate the explainability of the actor’s behavior and accelerate goal recognition by

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