Allocation Approach Research Articles

A Strategic Decision Support System Using Multiplayer Non-Cooperative Games for Resource Allocation After Natural Disasters

A severe natural disaster causes concurrent emergencies in separate geographical locations. However, tackling all the emergencies simultaneously with the limited available resources is challenging. A novel single-stage, non-cooperative multiplayer game-based solution approach for resource allocation is proposed in this paper, where the crisis locations in the demand of resources are treated as the players. The game-based decision support system (DSS) is intended to be implemented by the concerned disaster management authority to obtain a practical strategy for the allocation of indivisible and divisible resources among individual players in a limited resource environment. Any feasible allocation is associated with some cost on the basis of a non-monetary cost function. A discrete strategic game is formulated to tackle indivisible resource allocation and a continuous-kernel game for divisible resources. A mathematical analysis establishes that the game with the proposed cost function possesses at least one pure strategy Nash equilibrium (PSNE) for both types of games. Based on different selection criteria, a particular PSNE is chosen from the collection of multiple PSNEs and used as a desirable resource allocation strategy. A complexity analysis of the proposed algorithm is also carried out. Case studies are given in this paper to demonstrate the results developed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The cost function proposed in this paper to formulate the non-cooperative game-based DSS will be useful to disaster management authorities in resource allocation problems after any natural disaster. While distributing a limited quantity of essential resources, the cost function maintains an overall balance so that none of the disaster locations is deprived or favoured unreasonably. The feasible allocations of the existing resources are determined by the solutions of a multiplayer game. The case study indicates that the disaster management authority can choose different post-disaster ground truths to compute the possible resource demands of the disaster locations. Using the demand vector (a vector comprising the demands of the players as elements) and the number of available resources as the inputs, the game-theoretic model generates the allocation vector, according to which the disaster locations can be allocated resources.

Disaster Vulnerability of Farmers from Flood Damage in District Charsadda

This research examines the association between community organization and disaster vulnerability in three flood-affected Village Councils (VCs) in Charsadda district, namely Nawan Killi, Faqir Abad, and Chak Utmanzaie. Using a proportional allocation approach, 353 households were randomly selected from a total of 4,270 in the sampling frame. Data was collected through interviews, with the study's conceptual framework featuring one independent variable (Community organization) and one dependent variable (Disaster vulnerability), both measured on a three-level Likert Scale. The analysis, employing chi-square tests, revealed significant associations. Disaster vulnerability demonstrated a highly significant relationship with community organization during flood seasons (p=0.000), facilitation of social organization by NGOs, democratic election of disaster management committees, representation of all vulnerable groups in committees, and inclusion of various castes and sects in community organizations. Furthermore, disaster vulnerability exhibited a highly significant correlation with decisions made by disaster management committees, incorporating suggestions from vulnerable groups, prioritizing vulnerability groups, informing community members of committee decisions, and having women represented in committees. The study's findings underscore the importance of establishing and updating community participation guidelines for disaster management, ensuring active involvement of all stakeholders in planning and executing disaster management initiatives, promoting awareness and capacity building in participatory disaster management approaches, and fostering partnerships with NGOs, donors, and government agencies to secure financial and technical support.

Joint time and energy-optimal approach to allocate task to actors in wireless sensor actor networks

In most applications of wireless sensor actor networks (WSANs) in harsh environments, minimizing the make-span and maximizing the residual energy are of paramount importance. The majority of existing task allocation approaches is typically concerned with one of the energy savings or time constraints. These approaches do not consider the types and various features of tasks WSANs may need to perform. Moreover, the limitation on the capacity of task queues has been overlooked by these approaches, and thus may not be applicable to some types of real applications such as search and rescue missions. To this end, a novel queue length aware task allocation (QLATA) approach is proposed that considers the energy consumption as well as the make-span. QLATA is aware of task queues constraint, types of tasks, and the distribution necessities of WSANs with hybrid architecture. QLATA comprises of two protocols, namely a Make-span Calculation Protocol (MsCP) and an Energy Consumption Calculation Protocol (ECCP). Through considering both time and energy, QLATA makes a tradeoff between minimizing make-span and maximizing the residual energies of actors by solving the joint optimization for make-span and energy consumption, simultaneously. A series of extensive simulation results on typical scenarios show shorter make-span and higher remaining energy in comparison to when opportunistic load balancing (OLB), minimum make-span task allocation (MMTa), stochastic task allocation (STA), and task assignment algorithm based on quasi-newton interior point (TA-QNIP) approaches is used. It is also shown that the proposed approach performs significantly better than the four compared algorithms in terms of network lifetime.

The role of multi-family properties in hedging pension liability risk: long-run evidence

PurposeUsing data spanning 145 years for Sweden, the authors investigate the benefits of holding multi-family properties for investors who aim to hedge wage growth.Design/methodology/approachThe authors assess the risk-adjusted excess return that results from adding multi-family properties to a mixed-asset portfolio that aims to track wage growth. The authors also analyse the macroeconomic determinants of asset returns. Finally, the authors test whether a causal relationship exists between the growth rate of real wages and that of real net operating income.FindingsThe benefits from holding multi-family properties are the greatest for low-risk allocation approaches. For more risky strategies, the role of real estate is more muted, and it varies greatly over time. Holding real estate was most beneficial during the first two decades of the 21st century. Multi-family properties are found to be the only asset class to be positively related to wage growth. The authors show that the net operating income acts as the transmission channel between wages and property returns.Practical implicationsThe paper assesses whether the growing interest of pension funds for multi-family properties is warranted in the context of a portfolio that aims to track wage growth.Originality/valueUsing long term data makes it possible to use a rolling windows approach and hence to consider multiple outcomes for an allocation strategy over a typical investment horizon. This permits to assess the dispersion of performance across several periods rather than just one as is commonly done in the literature. The results show that the conclusions that would be drawn from looking at the past two or three decades of data differ substantially from those for earlier time periods.

Application of ARIMA Model in Portfolio Optimization

This paper investigates the portfolio allocation strategy using the ARIMA time series model with eight companies in four industries: Technology, Healthcare, Financial Services, and New Energy. This research aims to identify the best portfolio allocation approach that maximizes the returns for investors in each industry. This paper collected daily stock prices of Apple and Microsoft in the technology industry, Johnson &amp; Johnson and Pfizer in the healthcare industry, JPMorgan Chase and Goldman Sachs in the financial services industry, and Tesla and NextEra Energy in the new energy industry from January 2016 to December 2019. The logarithmic returns first were calculated for the first three years as the training data. Then, an appropriate ARIMA time series model would be fitted to forecast the stock return for the last year and perform the portfolio optimization based on the predicted return for 2019 following a result of weights for each asset. By comparison, the paper also applied portfolio optimization to the raw logarithmic daily return from 2016 to 2018 without the time series model fitted and got another set of weights for each asset. Finally, both cumulative returns for these two portfolios computed and compared with visualizations. There is a finding that the modified strategy of portfolio with ARIMA model forecasted data could provide a more obvious trade-off relationship between volatility and sharpe-ratio for returns than the traditional one performed with the raw data. The result of this paper provides a potential scope of an application of portfolio optimization worked with time series modeling for investors to make investment decisions that possibly yield a higher return.

A deep reinforcement learning assisted task offloading and resource allocation approach towards self-driving object detection

With the development of communication technology and mobile edge computing (MEC), self-driving has received more and more research interests. However, most object detection tasks for self-driving vehicles are still performed at vehicle terminals, which often requires a trade-off between detection accuracy and speed. To achieve efficient object detection without sacrificing accuracy, we propose an end–edge collaboration object detection approach based on Deep Reinforcement Learning (DRL) with a task prioritization mechanism. We use a time utility function to measure the efficiency of object detection task and aim to provide an online approach to maximize the average sum of the time utilities in all slots. Since this is an NP-hard mixed-integer nonlinear programming (MINLP) problem, we propose an online approach for task offloading and resource allocation based on Deep Reinforcement learning and Piecewise Linearization (DRPL). A deep neural network (DNN) is implemented as a flexible solution for learning offloading strategies based on road traffic conditions and wireless network environment, which can significantly reduce computational complexity. In addition, to accelerate DRPL network convergence, DNN outputs are grouped by in-vehicle cameras to form offloading strategies via permutation. Numerical results show that the DRPL scheme is at least 10% more effective and superior in terms of time utility compared to several representative offloading schemes for various vehicle local computing resource scenarios.

A posteriori multiobjective approach for techno-economic allocation of PV and BES units in a distribution system hosting PHEVs

This paper proposes a posteriori multiobjective approach to effectively integrate photovoltaic (PV) units into the distribution network. PV units are inherently non-dispatchable sources due to the intermittent nature of solar irradiance. To address this limitation, battery energy storage (BES) units are employed to support the PV units, thereby transforming them into dispatchable sources. The objective of this study is to optimally allocate dispatchable PV-BES units within a distribution network that accommodates plug-in hybrid electric vehicles (PHEVs). The PHEVs are modelled considering stochastic parameters at three different demand response levels. A multiobjective optimization problem is formulated to address the conflicting technical and economic objectives associated with the allocation of PV-BES units. The multiobjective manta ray foraging optimizer algorithm is utilized for solving the multiobjective optimization problem. Additionally, the technique for order of preference by similarity to ideal solution is used to identify the best trade-off solution, and multiple solutions are presented based on the decision-makers preferences regarding the conflicting objectives under consideration. The proposed approach is validated on 33-bus and 69-bus distribution networks, and extensive case studies are conducted to showcase the advantages of the proposed methodology.

Sparse and stable international portfolio optimization and currency risk management

This paper introduces a sparse and stable optimization approach for multi-currency asset allocation, aiming to improve portfolio performance and currency risk management. We demonstrate that the widespread industry practice of employing currency overlay strategies is suboptimal. In contrast, our proposed regularized joint optimization approach, which integrates assets and currencies, consistently outperforms currency overlay strategies as well as equally-weighted and non-regularized global portfolio benchmarks net of transaction costs. On average, the joint optimization approaches achieve 23.3% higher out-of-sample Sharpe ratios compared to their currency overlay counterparts. By addressing parameter uncertainty and inducing sparsity and stability, our method enhances the mean-variance framework, resulting in improved out-of-sample portfolio performance. These findings challenge the prevailing practice of employing currency overlay strategies and highlight the potential for additional gains in risk-adjusted returns through the joint optimization of assets and currencies.

An approach for water allocation with a couple surface and groundwater model

The integrative analysis considering the spatial and temporal representation of the hydrological and hydrogeological process is a challenge for the water resources management process and the water allocation policy. The join of distributed hydrological, hydrogeological and optimization models is an alternative to achieve the effective water integrated management. We present a framework for the conjunctive use of the RUBEM, MODFLOW and an extended version of PYWR models that return monthly results of the surface-groundwater processes and the optimization of the runoff. An irrigation node was created with maximum flow restriction to save storage in the basin. The framework was applied in a basin of Sao Paulo state, that suffers with water scarcity, showing good adherence with the water balance patterns and the differences in demand attendance when irrigation nodes are active.

Enhancing Load Balancing in Heterogeneous High Performance Cloud Computing with MMWRR+: A Novel Task Allocation Approach

Enhancing Load Balancing in Heterogeneous High Performance Cloud Computing with MMWRR+: A Novel Task Allocation Approach

RSMA for Hybrid RIS-UAV-Aided Full-Duplex Communications With Finite Blocklength Codes Under Imperfect SIC

In this work, we consider a hybrid aerial full-duplex (FD) relaying consisting of a reconfigurable intelligent surface (RIS) mounted over an FD unmanned aerial vehicle (UAV) relay operating in decode and forward mode to assist the information transfer between the base station and multiple users. For better spectral efficiency, we investigate the use of rate splitting multiple access (RSMA) in such networks and focus on joint optimization of RSMA parameters, 3D-coordinates of the UAV/RIS, and phase shift matrix at the RIS along with analyzing the outage probability, block error rate (BLER) and achievable weighted sum rate for finite blocklength (FBL) and infinite blocklength (IBL) codes under imperfect successive interference cancellation (SIC) at each user and residual-self interference (RSI) at the UAV. We first formulate the weighted sum rate maximization problem and adopt the block coordinate descent (BCD) method to deal with the non-convex nature of the problem. Thereafter, we propose a BCD-based algorithm that jointly optimizes these parameters using a heuristic approach for optimum power allocation, a Riemannian conjugate gradient-based algorithm to get the optimal phase shift at the RIS, and an iterative algorithm to obtain the optimal UAV/RIS position. It also distributes the common rate among the users optimally. Next, with obtained optimal parameters, we further analyze the performance of the network and derive the closed-form expressions of BLER, outage probability, and average weighted sum rate. We present Monte Carlo simulation-based results to validate the accuracy of the proposed algorithms and derived expressions, and demonstrate the superiority of RSMA over non-orthogonal multiple access (NOMA) and conventional orthogonal multiple access (OMA) schemes.

Multi-criterion multi-UAV task allocation under dynamic conditions

Multi-UAV systems are essential in accomplishing specific tasks across various research applications. UAVs can be used in search and rescue (SR), which requires them to explore disaster areas and rescue survivors. A significant challenge in such a scenario is allocating tasks to UAVs to support as many survivors as possible. The allocation of tasks to a multi-UAV system is a highly intricate issue that falls under the NP-hard classification. Dealing with multiple attributes and constraints for tasks and UAVs can complicate the problem. Based on several limiting factors and distinctive characteristics, this paper proposes a compromised task allocation (TA) approach for multi-UAVs operating in SR scenarios. The proposed algorithm enhances the performance impact (PI) algorithm by incorporating compromised PI (CPI) with specific constraints. Additionally, CPI is extended to compromised dynamic PI (CDPI) to handle any new tasks that may arise during task execution to accommodate dynamic events. Simulation results demonstrate a performance improvement of 22% in task allocation and 28.57% for task prioritization as compared with the PI algorithm.

Energy Efficiency Resource Management for D2D-NOMA Enabled Network: A Dinkelbach Combined Twin Delayed Deterministic Policy Gradient Approach

In this paper, a joint cluster association and power allocation resource management scheme is proposed in a D2D-NOMA-enabled heterogeneous network. The target is to maximize users' long-term cumulative energy efficiency. A novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -times iteration method based on matching theory is constructed to handle the NOMA cluster association, which is formulated as the perfect matching of a weighted bipartite graph. Considering it is difficult to obtain the complete instantaneous channel state information, we offer a deep reinforcement learning (DRL) framework, which guarantees the feasibility of DRL methods for the NP-hard power allocation. Meanwhile, an optimal reward function is designed to enhance the training and interacting efficiency. Based on the proposed DRL framework, the Dinkelbach-TD3 power allocation approach is proposed to shrink the action range of twin delayed deep deterministic policy gradient (TD3) with the fractional programming method Dinkelbach, which elaborately increases the training efficiency and the precision of the DRL methods. Simulation results demonstrate that the proposed DRL framework has significant energy efficiency performance improvement.

Federated Reinforcement Learning-Based Resource Allocation in D2D-Enabled 6G

The current 5G and conceived 6G era with ultrahigh density, ultra-high frequency bandwidth, and ultra-low latency can support emerging applications like Extended Reality (XR), Vehicle to Everything (V2X), and massive Internet of Things (IoT). With the rapid growth of transmission rate requirements and link numbers in the wireless communication network, how to allocate resources reasonably and further improve spectrum utilization challenges the traditional approaches. To address these problems, technologies such as device-to-device (D2D) communication and machine learning (ML) are introduced to the traditional cellular communication network to improve network performance. However, due to the interference caused by spectrum reusing, efficient resource allocation for both cellular users and D2D users is necessary. In this article, we consider underlay mode D2D-enabled wireless network to improve the spectrum utilization, and deep reinforcement learning (DRL)- based federated learning (FL)-aided decentralized resource allocation approach to maximize the sum capacity and minimize the overall power consumption while guaranteeing the quality of service (QoS) requirement of both cellular users and D2D users. The performance of the proposed schemes is evaluated through simulations under 5G millimeter-wave (mm-wave) and 6G terahertz (THz) scenarios separately. The simulation results show that the proposal achieves significant network performance compared with the baseline algorithms.

Hierarchical Vaccine Allocation Based on Epidemiological and Behavioral Considerations.

Vaccines have proven useful in curbing contagion from new strains of the SARS-CoV-2 virus. However, equitable vaccine allocation continues to be a significant challenge worldwide, necessitating a comprehensive allocation strategy incorporating heterogeneity in epidemiological and behavioral considerations. In this paper, we present a hierarchical allocation strategy that assigns vaccines to zones and their constituent neighborhoods cost-effectively, based on their population density, susceptibility, infected count, and attitude towards vaccinations. Moreover, it includes a module that tackles vaccine shortages in certain zones by locally transferring vaccines from zones with surplus vaccines. We leverage the epidemiological, socio-demographic, and social media datasets from Chicago and Greece and their constituent community areas to show that the proposed allocation approach assigns vaccines based on the chosen criteria and captures the effects of disparate vaccine adoption rates. We conclude the paper with a lowdown on future efforts to extend this study to design models for effective public policies and vaccination strategies that curtail vaccine purchase costs.

Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach

Chatbots are AI-powered programs designed to replicate human conversation. They are capable of performing a wide range of tasks, including answering questions, offering directions, controlling smart home thermostats, and playing music, among other functions. ChatGPT is a popular AI-based chatbot that generates meaningful responses to queries, aiding people in learning. While some individuals support ChatGPT, others view it as a disruptive tool in the field of education. Discussions about this tool can be found across different social media platforms. Analyzing the sentiment of such social media data, which comprises people’s opinions, is crucial for assessing public sentiment regarding the success and shortcomings of such tools. This study performs a sentiment analysis and topic modeling on ChatGPT-based tweets. ChatGPT-based tweets are the author’s extracted tweets from Twitter using ChatGPT hashtags, where users share their reviews and opinions about ChatGPT, providing a reference to the thoughts expressed by users in their tweets. The Latent Dirichlet Allocation (LDA) approach is employed to identify the most frequently discussed topics in relation to ChatGPT tweets. For the sentiment analysis, a deep transformer-based Bidirectional Encoder Representations from Transformers (BERT) model with three dense layers of neural networks is proposed. Additionally, machine and deep learning models with fine-tuned parameters are utilized for a comparative analysis. Experimental results demonstrate the superior performance of the proposed BERT model, achieving an accuracy of 96.49%.

A Survey of Energy Optimization Approaches for Computational Task Offloading and Resource Allocation in MEC Networks

With the increased penetration of cloud computing and virtualization, a plethora of internet of things devices have been deployed globally. As a result, computationally intensive tasks are transmitted from the edge towards the centralized cloud for processing that leads to increased energy utilization in the cloud data centers while at the same increasing significant latency for critical applications. Recent years have witnessed a paradigm shift from centralized cloud computing towards mobile edge computing (MEC), where computational tasks are offloaded at the edge servers near user equipment (UE). This paradigm leads to lowering the energy utilization in the cloud data centers, along with low latency for UE and efficient resource utilization at the edge. In this context, the scale and complexity of the MEC networks is drastically increasing and, consequently, finding effective energy-efficient solutions for computational task offloading and resource allocation in MEC networks has become an ambitious task. To address the aforementioned challenges, this work surveys the state of the art in different categorizations of algorithm-based computational task offloading and resource allocation strategies focusing on energy utilization. It also provides a detailed cross-comparison of existing strategies in terms of their implementation specifications. Additionally, this paper also highlights open challenges and potential future research directions to facilitate efficient task offloading and resource allocation at the edge with reduced energy consumption at the centralized data centers. Our work also paves the way for the deployment of critical applications at the edge that require low latency and high service quality guarantees.

The AMG model coupled with Rock-Eval® analysis accurately predicts cropland soil organic carbon dynamics in the Tuojiang River Basin, Southwest China

Accurate soil organic carbon models are key to understand the mechanisms governing carbon sequestration in soil and to help develop targeted management strategies to carbon budget. The accuracy and reliability of soil organic carbon (SOC) models remains strongly limited by incorrect initialization of the conceptual kinetic pools and lack of stringent model evaluation using time-series datasets. Notably, due to legacy effects of management and land use change, the traditional spin-up approach for initial allocation of SOC among kinetic pools can bring substantial uncertainties in predicting the evolution of SOC stocks. The AMG model can fulfill these conditions as it is a parsimonious yet accurate SOC model using widely-available input data. In this study, we first evaluated the performance of AMGv2 before and after optimizing the potential mineralization rate (k0) of SOC stock following a leave-one-site-out cross-validation based on 24 long-term field experiments (LTEs) in the Southwest of China. Then, we used Rock-Eval® thermal analysis results as input variables in the PARTYSOC machine learning model to estimate the initial stable SOC fraction (CS/C0) for the 14 LTEs where soil samples were available. The results showed that initializing the CS/C0 ratio using PARTYSOC combined with the optimized k0 further improved the accuracy of model simulations (R2 = 0.87, RMSE = 0.25, d = 0.90). Combining average measured CS/C0 and k0 optimization across all 24 LTEs also improved the model predictive capability by 25% compared to using default parameterization, thus suggesting promising avenue for upscaling model applications at the regional level where only a few measurement data on SOC stability can be available. In conclusion, the new version of the AMG model developed in the Tuojiang River Basin context exhibits excellent performance. This result paves the way for further calibration and validation of the AMG model in a wider set of contexts, with the potential to significantly improve confidence in SOC predictions in croplands over regional scales.

Analysis of feasible region in network slicing: Markov chain approach

Network slicing is a key feature introduced for 5G and Beyond 5G (B5G) to provide complete connectivity between people and things by overcoming limited resources. This study considers a theoretical approach for resource allocation when network slicing is employed. Specifically, an algorithm finding a feasible region of users for a given environment is proposed, and a continuous time Markov chain is adopted for its analysis. Some performance measures, such as average throughput and expected total revenue of the infrastructure provider, are expressed by the stationary probabilities of the Markov chain. A simple example and three different environments are presented in the experiment to illustrate the analysis procedure and investigate the effects of environments on performance, respectively. This analytical approach can simplify and speed up the evaluation of the performance of the network operator's strategy and increases the reliability of the evaluation, which is beneficial in practical applications. Moreover, this can provide theory based-guidance for infrastructure providers in determining the consistent network slice allocation policies, considering the users' needs and the network operator's decision strategy.

Unveiling Vaccine Hesitancy on Twitter: Analyzing Trends and Reasons during the Emergence of COVID-19 Delta and Omicron Variants.

Given the high amount of information available on social media, the paper explores the degree of vaccine hesitancy expressed in English tweets posted worldwide during two different one-month periods of time following the announcement regarding the discovery of new and highly contagious variants of COVID-19-Delta and Omicron. A total of 5,305,802 COVID-19 vaccine-related tweets have been extracted and analyzed using a transformer-based language model in order to detect tweets expressing vaccine hesitancy. The reasons behind vaccine hesitancy have been analyzed using a Latent Dirichlet Allocation approach. A comparison in terms of number of tweets and discussion topics is provided between the considered periods with the purpose of observing the differences both in quantity of tweets and the discussed discussion topics. Based on the extracted data, an increase in the proportion of hesitant tweets has been observed, from 4.31% during the period in which the Delta variant occurred to 11.22% in the Omicron case, accompanied by a diminishing in the number of reasons for not taking the vaccine, which calls into question the efficiency of the vaccination information campaigns. Considering the proposed approach, proper real-time monitoring can be conducted to better observe the evolution of the hesitant tweets and the COVID-19 vaccine hesitation reasons, allowing the decision-makers to conduct more appropriate information campaigns that better address the COVID-19 vaccine hesitancy.