Optimal Resource Allocation Model Research Articles

A route network resource allocation method based on multi-objective optimization and improved genetic algorithm

In order to solve the problem of imbalance between traffic demand and airspace capacity of high-altitude air route network, reduce unnecessary delay costs, and improve air route operation efficiency, the resource allocation problem of multi-objective air route network for CTOP program is studied. Taking the affected flights in the congested area of air routes as the research object, taking into account the constraints of actual flight operation, FCA time slot resource availability limit, FCA capacity limit, etc., aiming at minimizing the total delay time of each flight and maximizing the fairness of airlines, a multi-objective optimization model for air route network resource allocation is established, and an improved NSGA-II algorithm is designed to solve the model. Based on the actual operation data of air routes in East China, the Pareto optimal solution set is obtained and compared with the traditional RBS algorithm, the average delay time is reduced by 5.49% and the average fair loss degree is reduced by 66.76%. The results show that the proposed multi-objective optimization model and the improved NSGA-II algorithm have better performance, which can take into account the fairness of each airline on the basis of reducing the total delay cost, realize the allocation of optimal flight trajectories and time slot resources, and provide a reference scheme for air traffic control resource scheduling.

Deep reinforcement learning-based joint optimization model for vehicular task offloading and resource allocation

Deep reinforcement learning-based joint optimization model for vehicular task offloading and resource allocation

Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network.

In Vehicular Edge Computing Network (VECN) scenarios, the mobility of vehicles causes the uncertainty of channel state information, which makes it difficult to guarantee the Quality of Service (QoS) in the process of computation offloading and the resource allocation of a Vehicular Edge Computing Server (VECS). A multi-user computation offloading and resource allocation optimization model and a computation offloading and resource allocation algorithm based on the Deep Deterministic Policy Gradient (DDPG) are proposed to address this problem. Firstly, the problem is modeled as a Mixed Integer Nonlinear Programming (MINLP) problem according to the optimization objective of minimizing the total system delay. Then, in response to the large state space and the coexistence of discrete and continuous variables in the action space, a reinforcement learning algorithm based on DDPG is proposed. Finally, the proposed method is used to solve the problem and compared with the other three benchmark schemes. Compared with the baseline algorithms, the proposed scheme can effectively select the task offloading mode and reasonably allocate VECS computing resources, ensure the QoS of task execution, and have a certain stability and scalability. Simulation results show that the total completion time of the proposed scheme can be reduced by 24-29% compared with the existing state-of-the-art techniques.

Measuring and Mitigating Group Inequalities In Resource Allocation

Resource allocation, an integral part of socio-economic governance, profoundly influences individual prosperity and has the potential to mitigate or exacerbate socioeconomic disparities. This paper addresses the challenge of equitably allocating finite resources among individuals by answering two fundamental questions: (1) how to accurately measure and test group disparities and (2) how to optimally distribute resources while ensuring group fairness. We propose the Group Beneficiary Disparity (GBD) metric – an evaluation tool engineered to systematically gauge inequalities in a binary beneficiary/non-beneficiary context. The GBD provides decision-makers and planners with a powerful tool to audit social programs and optimize policies from a lens of group equality. We argue that utilitarian decision-makers cannot fully eliminate group disparities even when operating under social welfare constraints. To address this issue, we propose a new resource allocation optimization model, called A-FARM (Asymptotically Fair Allocation of Resources Model), with asymptotic group fairness guarantees. A-FARM partitions individuals into distinct, non-overlapping units and distributes resources among these units based on a utility-based allocation mechanism. Finally, we evaluate the performance of our proposed algorithm using both simulated and real-world data. Our results demonstrate that, A-FARM enables decision-makers to (1) achieve maximum efficiency under group fairness constraints and (2) perform a fairness-efficiency trade-off.

Optimal resource allocation model for COVID-19: a systematic review and meta-analysis

BackgroundA lack of health resources is a common problem after the outbreak of infectious diseases, and resource optimization is an important means to solve the lack of prevention and control capacity caused by resource constraints. This study systematically evaluated the similarities and differences in the application of coronavirus disease (COVID-19) resource allocation models and analyzed the effects of different optimal resource allocations on epidemic control.MethodsA systematic literature search was conducted of CNKI, WanFang, VIP, CBD, PubMed, Web of Science, Scopus and Embase for articles published from January 1, 2019, through November 23, 2023. Two reviewers independently evaluated the quality of the included studies, extracted and cross-checked the data. Moreover, publication bias and sensitivity analysis were evaluated.ResultsA total of 22 articles were included for systematic review; in the application of optimal allocation models, 59.09% of the studies used propagation dynamics models to simulate the allocation of various resources, and some scholars also used mathematical optimization functions (36.36%) and machine learning algorithms (31.82%) to solve the problem of resource allocation; the results of the systematic review show that differential equation modeling was more considered when testing resources optimization, the optimization function or machine learning algorithm were mostly used to optimize the bed resources; the meta-analysis results showed that the epidemic trend was obviously effectively controlled through the optimal allocation of resources, and the average control efficiency was 0.38(95%CI 0.25–0.51); Subgroup analysis revealed that the average control efficiency from high to low was health specialists 0.48(95%CI 0.37–0.59), vaccines 0.47(95%CI 0.11–0.82), testing 0.38(95%CI 0.19–0.57), personal protective equipment (PPE) 0.38(95%CI 0.06–0.70), beds 0.34(95%CI 0.14–0.53), medicines and equipment for treatment 0.32(95%CI 0.12–0.51); Funnel plots and Egger’s test showed no publication bias, and sensitivity analysis suggested robust results.ConclusionWhen the data are insufficient and the simulation time is short, the researchers mostly use the constructor for research; When the data are relatively sufficient and the simulation time is long, researchers choose differential equations or machine learning algorithms for research. In addition, our study showed that control efficiency is an important indicator to evaluate the effectiveness of epidemic prevention and control. Through the optimization of medical staff and vaccine allocation, greater prevention and control effects can be achieved.

Construction of an intelligent optimization model for water resource allocation under drought conditions

Abstract In this paper, we first establish the model of optimal allocation of water resources within the region and convert it into a dynamic planning problem. Then, under drought conditions, the models of optimal allocation of intra-regional water resources among industries and different crops are established, respectively, and the water production function model is established using sensitivity coefficients. Under the condition of satisfying the water quantity opportunity constraints and with the objective of ensuring obtaining a higher system return, the water resources optimal allocation model based on interval multi-stage stochastic opportunity constraint planning is established by combining multi-stage planning, interval planning and stochastic opportunity constraint planning. A hybrid intelligent algorithm combining stochastic simulation, multilayer neural networks, and genetic algorithms is used to solve the model. A case study of optimal water resource allocation is used to verify the feasibility and validity of the proposed model and algorithm. Under the traditional water resource allocation scheme, the total water supply and water shortage of Nandan County are 0.97 and 0.58 billion m3, and the water shortage rate is 37.55%. Under the optimized water resources allocation scheme of this paper, the water shortage of Nandan County is 0.05 billion m³, and the water shortage rate is 6.09%. The model of this paper’s allocation scheme can effectively meet the water demand.

Water temperature and season length interact to explain a rare non‐linear ecogeographic cline in body size

AbstractAimAdult body size often exhibits patterns across large‐scale environmental gradients, creating ecogeographic clines. However, the form of body size clines varies across taxonomic groups, with linear and non‐linear patterns in body size observed in nature. Non‐linear body size clines have received less study, and questions remain about how environmental gradients interact to produce non‐linear clines. We examined the body size of the American horseshoe crab (Limulus polyphemus), a widely distributed marine arthropod, and evaluated the hypothesis that temperature and active season length can interact multiplicatively to result in a dome‐shaped distribution.LocationFourteen states in the United States of America and three Mexican states, representing the entire geographic range of the species.MethodsWe compiled environmental data and body size measurements from more than 49,000 individual horseshoe crabs. For each location, we extracted from the literature or calculated from raw data the mean male prosoma width and the mean female prosoma width. We applied a general additive modelling (GAM) approach to characterize the body size cline, test a hypothesis regarding temperature and season length, and explore evidence for the influence of additional environmental factors.ResultsModel results indicate temperature and season length could act multiplicatively to produce dome‐shaped clines, and these findings align with and quantify previous anecdotal reports of a strong dome‐shaped body size cline across latitude for horseshoe crabs.Main ConclusionsActive season length appears to become relatively more influential on horseshoe crab body size in the northern part of their range, while temperature effects per se appear to dominate in southern latitudes. For horseshoe crabs, the pattern of size variation is consistent with the predictions of Optimal Resource Allocation models, but more study is needed to elucidate mechanistic underpinnings. Considering climate change projections, results from our study suggest future shifts in horseshoe crab body sizes.

An optimised resource allocation scheme based on NOMA-SWIPT in cooperative network

ABSTRACT A cooperative network could improve energy efficiency by combining non-orthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT). This work proposes a novel model for optimal resource allocation for specified users through combined time switching and power splitting protocol. The system uses an amplified and forward-based energy harvesting protocol to broadcast the data in the Nakagami-m fading channel. An algorithm that provides the power coefficients for all NOMA users as a Levy Aquila optimiser is exploited to maximise the signal-to-noise ratio for far and near users. Further, the parameters of combined time switching and power splitting are brought by a game theoretic approach, which supports better energy harvesting in relay nodes with improved energy efficiency and high outage probability. The simulated results show that the employed scheme’s energy efficiency has increased by 11.3%, improved from the GS-Dinkelbach algorithm.

A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

The advent of cloud computing has dramatically altered how information is stored and retrieved. However, the effectiveness and speed of cloud-based applications can be significantly impacted by inefficiencies in the distribution of resources and task scheduling. Such issues have been challenging, but machine and deep learning methods have shown great potential in recent years. This paper suggests a new technique called Deep Q-Networks and Actor-Critic (DQNAC) models that enhance cloud computing efficiency by optimizing resource allocation and task scheduling. We evaluate our approach using a dataset of real-world cloud workload traces and demonstrate that it can significantly improve resource utilization and overall performance compared to traditional approaches. Furthermore, our findings indicate that deep reinforcement learning (DRL)-based methods can be potent and effective for optimizing cloud computing, leading to improved cloud-based application efficiency and flexibility.

Методика стратегического управления проектами

The article presents strategic project management methodology developed to meet the relevant requirements and challenges faced by organizations in current business environment. The methodology includes an algo-rithm for strategic project management, a system of indicators for assessing the results of project implementa-tion and a mathematical model for optimal resource allocation. The relevance of the methodology develop-ment is determined by the importance of strategic project management in current conditions of instability, un-certainty and innovations implementation necessity. It enables organizations to effectively plan, analyze, moni-tor and coordinate project activities to achieve their overall strategic objectives. The study also provides an ex-tended list of prospective project management principles, which served as the basis for the development of an algorithm and a system of indicators for evaluating the results of such activities. The latter are categorized into groups, including assessment of the achievement of strategic objectives, the quality of task performance, the use of resources and the level of risks. The mathematical model for optimal resource allocation enables effi-cient resource management in projects, taking into account requirements and limitations. The said model is based on minimizing the use of available capacity while meeting the availability restrictions for each project task.

Optimal resource allocation model in disaster situations for maximizing the value of operational process resiliency and continuity

Organizations need to apply resilience and business continuity in industry to protect themselves against the crises and destructive events. Also, the growing expansion of competition in the global market and the increasing crisis in the world have increased the importance of optimal resource allocation. With the optimal resource allocation, huge losses and damages to organizations are prevented. The problem of resource allocation can be raised alongside the criteria of process resilience and continuity. Therefore, organizations change their perspective from focusing solely on reducing vulnerability to increasing resilience and continuity against to accidents in crises and destructive situations. The objective of this paper is proposed a mathematical model for optimal resource allocation with the aim of minimizing the lack of process resilience and maximizing the process continuity. So, the organization can continue to operate with available resources in times of crisis and lack of resources. Also, main activities and processes are not interrupted by crises and destructive events. After solving the model using a case study (textile industry), the results of the model were described and it was found that destructive events were recovered before the tolerance threshold and crisis and destructive events did not interrupt activities and processes.

A Regional Water Resource Allocation Model Based on the Human–Water Harmony Theory in the Yellow River Basin

Considering the issues of water scarcity, water environment deterioration, and unreasonable allocation of water resources in the urban area of the Yellow River Basin, this paper introduces the human–water harmony theory to the allocation of regional water resources. Based on an analysis of the structural characteristics of the regional water resource system, the harmonious water resource allocation (HWRA) model—which includes three sub-systems (i.e., the water service system, ecological environmental system, and economic and social system)—is established. In addition, considering the uncertain factors in the HWRA model, the inexact fuzzy multi-objective programming (IFMOP) method is used to solve the model, aiming at achieving the minimum amount of regional water scarcity, the minimum amount of sewage discharge, and the maximum total economic benefit. A case study of water resource allocation of Binzhou, a city located in the Yellow River Basin, is conducted to validate the model. The model solution results show that the water resource system in Binzhou in 2025 and 2035 could be optimized after harmonious allocation, especially in terms of the water service and ecological environmental systems. Compared with the optimal water resource allocation (OWRA) model, the HWRA model has a more scientific water supply structure, and a smaller amount of sewage discharge. The HWRA model solves the variables using an interval number, so it can flexibly and scientifically reflect the decision-making process.

Nonconvex resource allocation for inelastic enterprise applications deployment into the cloud via particle swarm optimization

The advantages of cloud computing attract a large number of enterprises to deploy their applications into the cloud, thereby reducing their own operating costs. This paper considers deploying inelastic applications into the cloud and proposes an optimal resource allocation model. The deployment functions for inelastic applications are nonconvex (e.g., sigmoidal), then the resource allocation model becomes a hard nonconvex optimization problem. The traditional gradient-based resource allocation algorithm cannot effectively achieve the global optimum. Therefore, this paper applies particle swarm optimization (PSO) method to design a resource allocation scheme. This scheme can not only effectively solve the resource allocation problem of deploying inelastic enterprise applications into the cloud, but also solve the hard problem of deploying multi-class applications into the cloud when the enterprise can support both elastic and inelastic applications. We also compare the performance of the proposed PSO-based resource allocation scheme with some other methods and illustrate some numerical examples to verify the effectiveness and superiority of the proposed resource allocation scheme.

The optimal allocation method of resources based on the construction of rural ecological civilization

Owing to the low rate of utilization of traditional rural resources, it is impossible to allocate resources in areas with a complex agricultural structure. Therefore, this work designs a new method of resource allocation based on the construction of rural ecological civilization. In this work the optimal model of resource allocation under the construction of the rural ecological civilization is designed, the matrix of data of the evaluation index is established and the dimensionless processing is carried out. According to the gray correlation model, the degree of correlation of each evaluation index is calculated, and then the evaluation obstacle factors are calculated, to obtain the evaluation index of the optimal allocation of rural resources. On this basis, objectives of optimization of construction resources are achieved, the principles of optimization of resources are clarified, and finally the optimal allocation of resources based on the construction of rural ecological civilization is realized. The applicability of this method is verified by example analysis. The rate of utilization of rural resources under this method is high. When the regional structure is complex, it can also maintain a good resource allocation effect, demonstrating that research results are of practical importance.

DEA model for partial centralization resource allocation among independent subset of DMUs

This paper proposes the concept of partial centralization in view of the problem of partition and group governance in management practice, and based on this, an optimization model of independent subset incentive resource allocation is constructed. From the perspective of the decision makers of each independent subset, the model takes into account the different attributes of resources and incorporates the homogeneous decision-making units outside the subset into the reference set to allocate resources based on the principle of efficiency first and fairness. The purpose is to improve the output of each decision-making unit in the subset and reduce the myopia effect by rationally optimizing the allocation of resources. The constructed model can directly compute resource optimization results without model retransformation. Numerical examples show that by applying this model, the efficiency value of each decision-making unit after optimizing resource allocation is greater than or equal to the original efficiency value. Finally, it is applied to analyze the optimal allocation of resources of 30 banks in Taiwan. The results show that this method can better realize the optimal allocation of bank resources and stimulate the resource allocation of non-effective decision-making units, which has important application significance.

Economic efficient use of soilless techniques to maximize benefits for farmers

<abstract> <p>The main challenge of the agricultural sector is the increasing pressure on natural resources, mainly water and arable land. Consequently, an urgent imperative arises to explore technological advancements that can boost food production in alignment with the growing demands. The Soilless Production System (SPS) emerges as a proficient approach for managing irrigation water, thereby making a significant contribution to food security. This research focuses on the efficient use of SPS and identifies the best economic use of the soilless techniques for different crops within an area characterized by limited land and water availability. The database for the study was generated through a farm survey to investigate the benefits of adopting SPSs as a sustainable agricultural practice. A linear programming approach was applied to develop an optimization model for resource allocation and crop mix selection, considering the development opportunities through the SPSs. Different scenarios were applied in the model. The results proved that adopting SPSs is a sustainable irrigation practice, since the technique promotes water use efficiency, generates profitability, and conserves the associated natural resources. The SPSs ensure sustainable use of water resources by increasing water use efficiency. The hydroponics cultivation system had 11 ± 1.7 times higher yields but required 82 ± 11 times more energy in comparison to the lettuce crop produced by the conventional production system (CPS). The result of the optimal solution shows that the total revenue of scenarios of the study is 109% of the revenue of the original value of the largest farmer. The water resources and the investment cost as constraints in the model are totally used, but the operational costs in the optimal solutions are 74% of the total operational cost in the original data. The optimal solution showed the importance of using computerized systems in which the control of the fertigation is better.</p> </abstract>

Decision Optimization for Water and Electricity Shared Resources Based on Fusion Swarm Intelligence

As one of the most important water conservancy projects, reservoirs use water resources to achieve essential functions, such as irrigation, flood control, and power generation, by intercepting rivers. As climate extremes and global warming increase, the world’s water reserves are being tested, and reservoir operators are being challenged. This paper investigates the optimal allocation of shared resources for hydropower to achieve rational decisions for reservoir operations. Firstly, a power resource model is constructed based on the real hydroelectric generator theory. Furthermore, based on the established power resource model combined with the influence of weather type and multi-region heterogeneous demand, this paper constructs a multi-objective hydropower shared resource allocation optimization model, with the lowest hydropower resource supply cost and the shortest time hydropower resource supply time as the optimization objectives. Secondly, for the problem that the traditional population intelligence algorithm easily falls into the local optimum when solving complex problems, the improvement of the MOPSO algorithm is completed by introducing the Levy flight strategy and differential evolution. Finally, simulation experiments were carried out, and cutting-edge algorithms, such as the GA algorithm and WOA algorithm, were selected for simulation comparison to verify the effectiveness of the constructed model and algorithm. The simulation results show that the research in this paper can contribute to effective decision-making for reservoir operators and promote intelligent reservoir operation.

Higher education resource optimal allocation and relationship with the biopharma and biotech industry

Current educational resource allocation methods achieve resource allocation through a simple linear constraint approach to a single resource restriction, and the single resource allocation restriction causes the allocation results to easily fall into a local optimal solution, which not only has low allocation efficiency but also cannot allocate all the resources effectively, leading to the waste of educational resources of a certain category or several categories in pharma industries. To address the above problems, the algorithm of optimal allocation of higher education resources based on the relationship with the biopharma and biotech industry is proposed. In view of the above problems, an algorithm for optimizing the allocation of higher education resources biopharma and biotech industry is proposed. By establishing a multidimensional analysis model of educational resource data, the resource data are analyzed and processed to determine the factors affecting resource allocation. Combined with the actual establishment of the resource allocation optimization model, NSGA-II is used to solve the problem, and the optimal allocation scheme is obtained. The application of the proposed configuration algorithm to reduce the standard deviation of the resource discrepancy coefficient to 0.134 is demonstrated through simulation experiments, which can effectively reduce the discrepancy between resources.

Reliability and Resource Allocation and Recovery of Urban Transportation System Considering the Virus Transmission

The urban transport system is an integral part of a city and is essential for the proper functioning of other urban functional systems. To improve the resilience of urban transport systems under the background of the spreading COVID-19 epidemic, this paper predicts the number of patients of various types at each stage of epidemic development based on an improved infectious disease model for Wuhan and verifies the validity of the model using statistical methods. Then, a system reliability model is developed from the perspective of controlling the spread of the virus and reducing economic losses, and the optimal time points for urban traffic closure and recovery are determined. Finally, a resource allocation optimization model was developed to determine the number and location of resource allocation points which based on 19 hospitals to avoid the further spread of the virus. The results give a valuable reference for enhancing the resilience of urban transport systems and improving their performance in all phases.

Optimization of Operating Hydrogen Storage System for Coal–Wind–Solar Power Generation

To address the severity of the wind and light abandonment problem and the economics of hydrogen energy production and operation, this paper explores the problem of multi-cycle resource allocation optimization of hydrogen storage systems for coal–wind–solar power generation. In view of the seriousness of the problem of abandoning wind and photovoltaic power and the economy of hydrogen production and operation, the node selection and scale setting issues for hydrogen production and storage, as well as decision-making problems such as the capacity of new transmission lines and new pipelines and route planning, are studied. This research takes the satisfaction of energy supply as the basic constraint and constructs a multi-cycle resource allocation optimization model for an integrated energy system, aiming to achieve the maximum benefit of the whole system. Using data from Inner Mongolia, where wind abandonment and power limitation are severe, and Beijing and Shanxi provinces, where hydrogen demand is high, this paper analyzes the benefits of the hydrogen storage system for coal–wind–solar power generation, and explores the impact of national subsidy policies and technological advances on system economics.