Optimal Allocation Research Articles

Research on the path of technological innovation and resource allocation optimization of state-owned enterprises from the perspective of ecological environment and biomechanics

In the evolving global landscape, competition among nations has increasingly centered on the allocation of innovation resources, which are crucial for enterprises to implement development strategies under supply-side reforms. The efficiency and rationality of resource allocation directly affect the innovation capacity and operational performance of enterprises. This paper examines the relationship between technological innovation and resource allocation in state-owned enterprises through the lens of the ecological environment, employing data from multiple sources and utilizing the Data Envelopment Analysis (DEA) model to assess resource allocation efficiency. The study reveals that the proportion of R&D personnel invested in enterprises in China increased significantly from 46.35% to 73.38%, while the proportion in research institutions and universities declined to 11.48% and 11.36%, respectively. These shifts underscore the growing dominance of state-owned enterprises in driving technological innovation. The findings highlight that aligning technological innovation mechanisms with ecological sustainability not only enhances innovation capabilities and competitiveness but also accelerates industrial development and contributes to the sustainable growth of the national economy.

Decision-Making Model for Life Cycle Management of Aircraft Components

This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework’s effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model’s versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework’s effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model’s robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments.

Security enhancement for NOMA-PON with 2D cellular automata and Turing pattern cascading scramble aided fixed-point extended logistic chaotic encryption

The non-orthogonal multiple access-passive optical network (NOMA-PON) is facing the dual security threats of primary user interference and unauthorized third-party user eavesdropping, so efficient data security enhancement techniques are crucial. To solve these problems, we propose a fixed-point extended (FE)-logistic chaotic mapping to reduce the computational complexity while employing a two-dimensional (2D) cellular automata (CA) and Turing pattern (TP) cascading scramble (CA-TPCS) encryption algorithm to further improve the sensitivity of the NOMA-PON system. The CA-TPCS consists of 2D-CA dynamic bit encryption and Turing symbol substitution (TSS). By using FE chaos to construct 2D-CA and adopting index sort to extract the TSS matrix, dynamic diffusion of bits and scrambling of a 2D symbol matrix are achieved. To ensure the key privacy, we employ a dual key mechanism, and uplink data is introduced as the private key. To verify the feasibility of the proposed method, a simulation validation is built on a 17.6 Gb/s power division multiplexing-orthogonal frequency division multiplexing (PDM-OFDM) NOMA-PON system transmitted over 25 km standard single mode fiber (SSMF). The results show that the proposed scheme has no effect on the optimal power allocation rate (PAR) values and the values are all 3. Meanwhile, the receiver sensitivity gains of 0.2 and 0.3 dB are obtained for high-power and low-power users after encryption. The ciphertext has good diffusion and statistical properties, and the key space is flexibly controlled by the FE precision f, the length l of the transmitted bit, and the size T of the TP, with the value of 22f+l+T×T. The results show that the proposed scheme is not only very compatible with PDM technology but also can realize the dual defense of internal aggression and external aggression. It has a good application prospect in the future NOMA-PON.

Optimal allocations of wind turbines in power systems via artificial rabbits optimization technique

Optimal allocations of wind turbines in power systems via artificial rabbits optimization technique

Research on intelligent optimal allocation control strategy for stability and braking of heavy tyre blowout vehicle

The traditional control strategy mainly compensates and corrects the driving state of heavy vehicles with a tyre blowout, and rarely considers the need for emergency braking. It is also difficult to effectively coordinate the problems of lateral yaw, slip and tailspin, and longitudinal braking performance degradation. For this, an intelligent optimization allocation control strategy combining stability control and braking control is innovatively designed in this article. The joint simulation model for heavy tyre blowout vehicles is built based on the Dugoff tyre blowout model and the TruckSim vehicle model. A linear quadratic form regulator stable control based on the particle swarm optimization algorithm optimization is used to dynamic equilibrium of the additional yaw moment generated by the vehicle tyre blowout. A logic threshold braking control based on adaptive adjustment is adopted to achieve an ideal braking effect for the vehicle. In order to simultaneously consider the stability and braking performance of vehicles with blowout tyre, a braking force distribution strategy and a fuzzy optimization allocation algorithm are proposed to achieve coordination of lateral and longitudinal control strategies. The results indicate that intelligent optimized allocation control can effectively correct the deviation trajectory of a heavy tyre blowout vehicle, enabling it to achieve rapid braking while maintaining stability.

Reinforcement learning for dynamic pricing and capacity allocation in monetized customer wait-skipping services

ABSTRACT We consider how to facilitate a dynamically-priced premium service option that enables customer parties to shorten their wait in a queue. Offering such a service requires that some of a business’s capacity be reserved continuously and kept ready for premium customers. In tandem with capacity reservation, pricing must be coordinated. Hence, a joint dynamic pricing and capacity allocation problem lies at the heart of this service. We propose a conceptual solution architecture and employ Proximal Policy Optimization (PPO) for dynamic pricing and capacity allocation to maximize total revenue. Simulation experiments over multiple scenarios compared PPO against a human-engineered policy and a baseline policy having no premium option. The human-engineered policy led to significantly greater revenues than the baseline policy in each scenario, illustrating the potential increase in revenues afforded by this concept. The PPO agent substantially improved upon the human-engineered policy advantage, with improvements ranging from 28% to 161%.

A multiobjective optimization model for allocating water quantity and quality in the Yangtze and Yellow River basins

Rapid urbanization and industrial growth have led to water quantity and quality problems. Most current water allocation models focus solely on physical water and neglect the optimization of the water quantity and quality considering the water footprint. Therefore, this study aimed to establish an optimal water resource allocation model for industrial sectors based on the water footprint to identify the most effective strategies for determining the water quantity and quality distribution in river basins. First, we constructed an improved water footprint accounting system using input‒output tables to quantify the total water footprint, grey water footprint, and virtual water trade. Second, we constructed a multiobjective optimal water resource allocation model by considering the water footprint as a decision variable and combining regional economic development and productivity differences. Finally, we compared the water allocation results obtained for the Yellow River Basin, a typical “water-quantity water-scarce region” in China, with those of the Yangtze River Basin, a “water-quality water-scarce region.” The results indicate that the total water footprints of the Yellow and Yangtze River Basins were 211.37 and 317.83 billion m³, respectively, prior to optimization. After optimization, the footprints were 199.54 and 306.03 billion m³, respectively. Water resources have been reallocated through virtual water trade strategies to effectively alleviate both water quantity and quality problems. Virtual water trade strategies have emerged as policy tools capable of mitigating mismatches between industrial systems and regional water resource allocation and providing a solution to physical–virtual water system challenges within river basins.

Forecasts of Video Game Sales based on the ARIMA Model

Abstract. With the rapid development of the digital age, video games have become an important component of the cultural industry, and their sales forecasting has become a focus of industry attention. This article aims to predict the sales of video games based on the ARIMA model and explore effective methods to improve prediction accuracy. Through in-depth research, this article found that the ARIMA model performs well in capturing the time series characteristics of sales data and can accurately predict future sales trends. The ARIMA model provides a systematic and scientific method for predicting the future market performance of video games by comprehensively considering the trends, seasonality, and random changes in historical sales data. This research achievement not only provides strong decision support for game developers, publishers, and platform operators but also promotes the optimization of resource allocation and market risk avoidance in the video games industry, which is of great significance for promoting the healthy and sustainable development of the industry.

Secrecy performance of D2D assisted cooperative uplink NOMA system with optimal power allocation strategy

This paper investigates the physical-layer security (PLS) aspects of the uplink cooperative non-orthogonal multiple access (NOMA) system in the context of two user scenario. More specifically, this work employs device-to-device (D2D) pair users (i.e., D1, and D2) to further improve the spectral efficiency (SE) of the proposed cooperative uplink NOMA (CU-NOMA) system. One D2D user acts as a decode-and-forward relay, improving the performance of both the cell-edge user (CU) and another D2D user i.e., (D2). We analyze the secrecy performance of CU and D2 under perfect and imperfect successive interference cancellation (SIC), and optimizes power allocation (PA) to boost the performance. The proposed CU-NOMA system is evaluated in terms of its performance metrics like ergodic secrecy capacity (ESC), ergodic secrecy sum capacity (ESSC), non-zero secrecy capacity (NSC), effective secrecy throughput (EST), and secrecy outage probability (SOP) under the presence of an external eavesdropper (Eav). In addition, this work derives the closed-form analytical expressions of ESC, NSC, EST, and SOP metrics for both CU and D2 under perfect SIC (pSIC) and imperfect (ipSIC) cases in order to characterize the secrecy performance of the proposed secure CU-NOMA network. Further, the outcomes of the simulations are shown as evidence for both the validation of the mathematical analysis and the performance of the method being suggested. The simulation result analysis of this work infers that the secrecy performance of CU-NOMA system with optimal PA exhibits superior performance than that of the fixed PA scheme.

Multiple Data Imputation Methods Advance Risk Analysis and Treatability of Co-occurring Inorganic Chemicals in Groundwater.

Accurately assessing and managing risks associated with inorganic pollutants in groundwater is imperative. Historic water quality databases are often sparse due to rationale or financial budgets for sample collection and analysis, posing challenges in evaluating exposure or water treatment effectiveness. We utilized and compared two advanced multiple data imputation techniques, AMELIA and MICE algorithms, to fill gaps in sparse groundwater quality data sets. AMELIA outperformed MICE in handling missing values, as MICE tended to overestimate certain values, resulting in more outliers. Field data sets revealed that 75% to 80% of samples exhibited no co-occurring regulated pollutants surpassing MCL values, whereas imputed values showed only 15% to 55% of the samples posed no health risks. Imputed data unveiled a significant increase, ranging from 2 to 5 times, in the number of sampling locations predicted to potentially exceed health-based limits and identified samples where 2 to 6 co-occurring chemicals may occur and surpass health-based levels. Linking imputed data to sampling locations can pinpoint potential hotspots of elevated chemical levels and guide optimal resource allocation for additional field sampling and chemical analysis. With this approach, further analysis of complete data sets allows state agencies authorized to conduct groundwater monitoring, often with limited financial resources, to prioritize sampling locations and chemicals to be tested. Given existing data and time constraints, it is crucial to identify the most strategic use of the available resources to address data gaps effectively. This work establishes a framework to enhance the beneficial impact of funding groundwater data collection by reducing uncertainty in prioritizing future sampling locations and chemical analyses.

US land sector mitigation investments and emissions implications

The land sector is anticipated to play an important role in achieving U.S. GHG emissions targets by reducing emissions and increasing sequestration from the atmosphere. This study assesses how much different levels of investment could stimulate land-based mitigation activities in the U.S. By applying a dynamic economic model of the land use sectors, with representation of 26 forestry and agricultural mitigation strategies across 11 U.S. regions, the study shows that annual investments of $2.4 billion could deliver abatement of around 80 MtCO2e annually. Under an optimal allocation of investments, the forestry sector and the Corn Belt are projected to receive the largest share of funds. Restricting land-based activities eligible for funds significantly reduces overall potential mitigation. For instance, if $24 billion investments are allocated only to agricultural activities, mitigation declines by 48% to 54 MtCO2e/yr over the next ten years. Finally, the level of abatement from each policy depends on the timing of implementation as the lowest cost mitigation actions are generally taken by the policy implemented first.

Research on Resource Allocation Algorithm for Non-Orthogonal Multiple Access Visible Light Communication

In order to satisfy the large-scale access of visible light communication (VLC) users, as well as the demand for user request rate, the resource allocation problem of visible light communication with drone-assisted non-orthogonal multiple access (NOMA) technique is investigated. An efficient scheme for joint optimization of power allocation and access point (AP) location is proposed. According to the state of user channel information, a user pairing strategy with uniform channel gain difference for any number of users is designed, and an objective function of maximizing the average user data rate with constraints is constructed. For this non-convex NP-hard problem, the optimization problem with constraints is transformed into an optimization problem without constraints by introducing the idea of a penalty function and then solved by the Harris Hawk Optimization (HHO) algorithm based on the nonlinear energy convergence factor, and ultimately, the optimal user power allocation factor, as well as the location of the AP, are found. The simulation results show that the scheme in this paper can improve the average user data rate better compared to other classical schemes. The system performance of the HHO algorithm is improved by about 20.31% compared to the Particle Swarm Optimization (PSO) algorithm. The HHO algorithm based on the nonlinear energy convergence factor improves the convergence speed by about 50% compared to the classical HHO algorithm.

Simulation Optimization for Inpatient Bed Allocation with Sharing

Simulation Optimization for Inpatient Bed Allocation with Sharing

Outpatient scheduling problem in smart hospital with two-agent deep reinforcement learning algorithm

As the internet and digital technologies revolutionize the healthcare sector, the development of smart hospitals is pivotal for improving medical service efficiency. This research presents a mathematical model for the outpatient scheduling problem and introduces the Graph-based Two-Agent Deep Reinforcement Learning algorithm (GTARS-DRL) to address the escalating demand for medical services and the scarcity of resources. The GTARS-DRL leverages graph neural networks for feature extraction of patient medical activities and applies the PDQPN algorithm to train agents for optimizing patient sequencing and service desk allocation, effectively minimizing wait times. Our experiments indicate that GTARS-DRL outperforms traditional scheduling rules in solution quality and matches the performance of Genetic Algorithms (GA), with a notable advantage in computational efficiency, boasting an ACPU of 0.762 s and an ARPT of 0.327 s. The algorithm’s efficacy is further supported by its successful application to larger-scale instances, showcasing its potential for real-world use. This study contributes a novel approach to outpatient scheduling in smart hospitals, providing theoretical backing and practical direction for the optimization of medical resource allocation, and is poised to significantly enhance service efficiency and patient satisfaction.

A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing

With advancements in integrated space–air–ground global observation capabilities, the volume of remote sensing data is experiencing exponential growth. Traditional computing models can no longer meet the task processing demands brought about by the vast amounts of remote sensing data. As an important means of processing remote sensing data, distributed cluster computing’s task scheduling directly impacts the completion time and the efficiency of computing resource utilization. To enhance task processing efficiency and optimize the allocation of computing resources, this study proposes a Multi-Strategy Improved Siberian Tiger Optimization (MSSTO) algorithm based on the original Siberian Tiger Optimization (STO) algorithm. The MSSTO algorithm integrates the Tent chaotic map, the Lévy flight strategy, Cauchy mutation, and a learning strategy, showing significant advantages in convergence speed and global optimal solution search compared to the STO algorithm. By combining stochastic key encoding schemes and uniform allocation encoding schemes, taking the task scheduling of aerosol optical depth retrieval as a case study, the research results show that the MSSTO algorithm significantly shortens the completion time (21% shorter compared to the original STO algorithm and an average of 15% shorter compared to nine advanced algorithms, such as a particle swarm algorithm and a gray wolf algorithm). It demonstrates superior solution accuracy and convergence speed over various competing algorithms, achieving the optimal execution sequence and machine allocation scheme for task scheduling.

Improvement of Optimal Human Resource Allocation Algorithm Based on Deep Latent Semantic Model

With the advances of technology, the banking industry faces the challenge of processing large-scale, heterogeneous human resource data. Traditional methods have difficulties in providing efficient and accurate analysis and prediction. This paper proposes an optimized human resource allocation algorithm based on deep latent Semantic Model (DLSM), which improves the accuracy and efficiency of data analysis by integrating deep neural network (DNN) and transfer learning technology. The experimental results show that the DLSM model performs well in text classification and information retrieval tasks, with the accuracy, accuracy, recall and F1 scores improving by 7.5%, 5.5%, 6.0% and 5.8%, respectively. This study confirms the excellent performance of DLSM model in HR data processing, provides an efficient and scientifically based solution for recruitment optimization in the banking industry, and enhances the competitiveness of enterprises.

Machine Learning-Based Resource Allocation Algorithm to Mitigate Interference in D2D-Enabled Cellular Networks

Mobile communications have experienced exponential growth both in connectivity and multimedia traffic in recent years. To support this tremendous growth, device-to-device (D2D) communications play a significant role in 5G and beyond 5G networks. However, enabling D2D communications in an underlay, heterogeneous cellular network poses two major challenges. First, interference management between D2D and cellular users directly affects a system’s performance. Second, achieving an acceptable level of link quality for both D2D and cellular networks is necessary. An optimum resource allocation is required to mitigate the interference and improve a system’s performance. In this paper, we provide a solution to interference management with an acceptable quality of services (QoS). To this end, we propose a machine learning-based resource allocation method to maximize throughput and achieve minimum QoS requirements for all active D2D pairs and cellular users. We first solve a resource optimization problem by allocating spectrum resources and controlling power transmission on demand. As resource optimization is an integer nonlinear programming problem, we address this problem by proposing a deep Q-network-based reinforcement learning algorithm (DRL) to optimize the resource allocation issue. The proposed DRL algorithm is trained with a decision-making policy to obtain the best solution in terms of spectrum efficiency, computational time, and throughput. The system performance is validated by simulation. The results show that the proposed method outperforms the existing ones.

Long-Term Fine-Grained Forecasts of Emergency Demand Using EMS Big Data and Population Estimates

In recent years, Japan has been facing a super-aging society, impacting various sectors. The issues of overcrowded emergency medical services and the increase in emergency transport cases are particularly severe, necessitating urgent responses. The research group, in collaboration with the Kobe City Fire Department, is conducting a study focusing on long-term fine-grained forecasts of emergency demand. This study aims to provide indicators for the strategic deployment of emergency teams and the scaling of medical facilities. By utilizing regional mesh data, it can capture local characteristics far more precisely than previous city or district-level forecasts, offering significant advantages for local government policy implementation. In the prediction process, there are two stages to leverage national land numerical information and improve the accuracy of the forecast model. The results reveal regional disparities in future emergency demand, enabling optimal allocation of emergency resources and strategic planning for emergency services.

Robustness meets co-jumps: optimal consumption and portfolio choice with derivatives

In this paper, we study a robust, dynamic, continuous-time optimal consumption and portfolio allocation problem for investors with recursive preferences who have access to both stock and derivatives markets. We assume the stock price process follows a stochastic volatility model, with instantaneous precision as the unique state variable, allowing for discontinuities in all the dynamics. We obtain a closed-form approximate solution up to a system of ODEs to the optimization problem for a non-unitary value of the elasticity of intertemporal substitution of consumption, being able to derive an exact solution as a particular case. Our theoretical findings show that the optimal policies are remarkably affected by the ambiguity-aversion parameters to diffusive and jump risks. A detailed numerical analysis confirms the effectiveness of our theoretical results on real data. Finally, we prove that investors who do not believe in ambiguity may suffer considerable wealth losses.

Predicting Intensive Care Admission in Children with Acute Asthma: A Meta-Analysis of Predictive Models

Background: Acute asthma is a common cause of pediatric emergency department visits and hospitalizations. Early identification of children at high risk of requiring intensive care unit (ICU) admission is crucial for optimal management and resource allocation. This meta-analysis aimed to evaluate the performance of predictive models for ICU admission in children presenting with acute asthma. Methods: A systematic search of PubMed, Embase, and Cochrane Library was conducted for studies published between 2013 and 2024 that developed or validated predictive models for ICU admission in children with acute asthma. Studies reporting sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were included. Methodological quality was assessed using the QUADAS-2 tool. Pooled estimates of diagnostic accuracy were calculated using a random-effects model. Results: Six studies (n = 2,850 children) met the inclusion criteria. The predictive models included clinical features (respiratory rate, oxygen saturation, accessory muscle use), lung function measures (peak expiratory flow rate), and blood gas analysis. Pooled sensitivity ranged from 0.71 (95% CI 0.59-0.82) to 0.78 (95% CI 0.72-0.83), specificity from 0.79 (95% CI 0.75-0.83) to 0.86 (95% CI 0.78-0.91), and AUROC from 0.79 (95% CI 0.72-0.86) to 0.88 (95% CI 0.84-0.92). Conclusion: Several predictive models demonstrate moderate to high accuracy in identifying children with acute asthma at risk of ICU admission. However, heterogeneity in model performance highlights the need for further research to validate existing models in diverse populations and develop more robust tools to guide clinical decision-making.