Optimal Allocation Research Articles

A multi-objective optimization framework for regional land-use allocation: Fully utilizing terrestrial vegetation to mitigate carbon emissions

Human-induced changes in land use and land cover (LULC) considerably impact the global carbon cycle. Rational LULC optimization with full utilization of terrestrial vegetation facilitates reconciling carbon reduction and socio-economic development. To this end, this study develops a multi-objective framework for low-carbon LULC spatial optimization that incorporates carbon suitability, historical suitability, and socio-economic needs. The framework is applied to Hubei Province, China. The key findings include: (1) Considering spatial variations in vegetation carbon sinks could lead to remarkable carbon reductions of 2.75 × 106 t C by 2030 and 7.68 × 106 t C by 2060. It would also lower carbon emissions per unit of gross domestic product (GDP) and enhance carbon sequestration per unit of vegetation by 2030, signifying enhanced carbon reduction efficiency. (2) Integrating carbon sequestration potential and historical suitability can considerably minimize emissions while sustaining socio-economic growth. Our method, compared with strategies based solely on historical development laws, can reduce carbon emissions by approximately 1.3–1.6 × 106 t C·a−1. (3) We also propose targeted low-carbon development suggestions for different LULCs. This study provides optimal spatial LULC allocations and strategic advice for regional LULC planning aimed at low-carbon development, demonstrating the feasibility of reconciling the tension between carbon reduction and socio-economic growth at the regional scale.

Transmission design for ISAC‐WPT backscatter RFID systems

AbstractIn this letter, a novel integrated sensing and communication (ISAC) scenario with wireless power transfer (ISAC‐WPT) is investigated. In this new setup, an access point simultaneously employs communication and sensing beams to serve a communication user, detect an RFID tag, and power an energy receiver. The objective is to design the sensing and communication beams to minimize the total transmit power while satisfying the requirements of tag detection, communication, and energy harvesting. To address the corresponding non‐convex problem, the constraints are reformulated into convex second‐order cone constraints, thereby obtaining the globally optimal solution. To further reduce computational complexity, zero‐forcing is employed to design the beamforming vectors, followed by solving a convex optimization problem to determine the optimal power allocation between sensing and communication beams. The simulation results demonstrate the effectiveness of the proposed beamforming designs, highlighting the potential of ISAC‐WPT Backscatter RFID systems.

Movable Antenna Empowered Downlink NOMA Systems: Power Allocation and Antenna Position Optimization

Movable Antenna Empowered Downlink NOMA Systems: Power Allocation and Antenna Position Optimization

Dynamic Resource Allocation and Offloading Optimization for Network Slicing in B5G Multi-tier Multi-tenant Systems

For differentiating and customizing different types of flows guaranteeing individual 5QI QoS requirements, 5G and Beyond 5G (B5G) first specify several key technologies, e.g., 1) virtualizing radio resources, network functions and network servers, 2) network slicing, 3) Service Function Chaining (SFC) and flow steering, etc. Furthermore, for reducing E2E delay and path/link traffic congestion for diverse flowing while accessing cloud computing, Multi-access Edge Computing (MEC) is specified in 5G/B5G ETSI. Although several extensively related studies discussed network slicing, SFC and MEC, they seldom consider both resource allocation and traffic offloading in a tenant efficiently, simultaneously. Thus, for efficiently addressing above critical issues, three motivations are proposed, including 1) to dynamically allocate resource for B5G with multi-tier multi-tenant networking, 2) to propose adaptively vertical and horizontal offloading to the computing node for diverse types of flows, and 3) to minimize the blocking rate while guaranteeing the delay constraint. Two novel efficient algorithms are proposed: Fast Latency Decrease Resource Allocation (FLDRA) and Minimum Cost Resource Allocation (MCRA). These two proposed algorithms achieve dynamic E2E resource allocation and optimal vertical and horizontal offloading to the computing node while guaranteeing the E2E latency and 5QI QoS requirements for different types of flows. Numerical results demonstrate that FLDRA minimizes resource allocation while MCRA balances the loading of resource availability. The proposed algorithms of FLDRA and MCRA significantly outperform the compared approaches in blocking rates. Moreover, the proposed MCRA algorithm yields the highest resource utilization, the least network delay, etc.

Stochastic optimal allocation of grid-side independent energy storage considering energy storage participating in multi-market trading operation

The integration of large-scale intermittent renewable energy generation into the power grid imposes challenges to the secure and economic operation of the system, and energy storage (ES) can effectively mitigate this problem as a flexible resource. However, the conventional ES allocation is mostly planned to meet the regulation demands of individual entities, which is likely to result in low utilization of ES and difficult to recover the investment cost. Therefore, a two-stage stochastic optimal allocation model for grid-side independent ES (IES) considering ES participating in the operation of multi-market trading, such as peak-valley arbitrage, frequency regulation, and leasing, is proposed in this paper to improve the comprehensive benefits and utilization rate of ES. The first stage aims to allocate IES and develop a systematic scheduling plan based on the forecast of wind power output and load demand, while the second stage responds to the uncertainty of wind power output by re-dispatching generating units and invoking ES power leased by wind farms. Then, a two-layer loop iterative solution algorithm based on the Benders decomposition is formed to effectively solve the proposed model. Finally, the approach developed in this paper is applied to a modified IEEE RTS-79 test system, and the results verify that it is both feasible and effective.

Adaptive Neurodynamic Approach to Multiple Constrained Distributed Resource Allocation.

In this article, an adaptive neurodynamic approach over multiagent systems is designed to solve nonsmooth distributed resource allocation problems (DRAPs) with affine-coupled equality constraints, coupled inequality constraints, and private set constraints. It is to say, agents focus on tracking the optimal allocation to minimize the team cost under more general constraints. Among the considered constraints, multiple coupled constraints are dealt with by introducing auxiliary variables to make Lagrange multipliers reach consensus. Furthermore, aiming to address private set constraints, an adaptive controller is proposed with the aid of the penalty method, thus avoiding the disclosure of global information. Through using the Lyapunov stability theory, the convergence of this neurodynamic approach is analyzed. In addition, to reduce the communication burden of systems, the proposed neurodynamic approach is improved by introducing an event-triggered mechanism. In this case, the convergence property is also explored, and the Zeno phenomenon is excluded. Finally, a numerical example and a simplified problem on a virtual 5G system are implemented to demonstrate the effectiveness of the proposed neurodynamic approaches.

Towards data auctions with externalities

The design of data markets has gained importance as firms increasingly use machine learning models fueled by externally acquired training data. A key consideration is the externalities firms face when data, though inherently freely replicable, is allocated to competing firms. In this setting, we demonstrate that a data seller's optimal revenue increases as firms can pay to prevent allocations to others. To do so, we first reduce the combinatorial problem of allocating and pricing multiple datasets to the auction of a single digital good by modeling utility for data through the increase in prediction accuracy it provides. We then derive welfare and revenue maximizing mechanisms, highlighting how the form of firms' private information – whether the externalities one exerts on others is known, or vice-versa – affects the resulting structures. In all cases, under appropriate assumptions, the optimal allocation rule is a single threshold per firm, where either all data is allocated or none is.

Wavelet quantile correlation between DeFi assets and banking stocks

We examine time frequency quantile correlation and portfolio benefits between decentralized finance (DeFi) assets and G7 banking stocks We employ wavelet quantile correlation to examine the correlation between DeFi and bank stocks. Notably, results reveal diversification benefits in the short to medium term and highlight DeFi assets' hedging and safe-haven properties, particularly against Japanese banking stocks. Further, bivariate portfolio analysis provides optimal allocation strategies. Our research offers new guidance for investors, portfolio managers, and regulators regarding the intersection of DeFi and traditional banking stocks.

Content adaptive JND profile by leveraging HVS inspired channel modeling and perception oriented energy allocation optimization

The existing just noticeable difference (JND) models consider the effects of various covariates, however, they rarely account for the fusion relationship between the covariates, i.e., they lack a holistic understanding of the mechanisms of visual perception and disregarding the significant impact of energy consumption on visual perception. In fact, visual perception is no exception to the rule that nerve activities and energy supply are inextricably linked. Based on this insight, this paper proposes a novel JND estimation model employing content-adaptive energy allocation. Primarily, the information theory is applied to the visual perception system by conceptualizing human visual system (HVS) as an information communication framework. Then, leveraging the relationship between energy consumption and information perception, this paper quantitatively measures the HVS energy consumption as uniform metric to describe the complicated and heterogeneous HVS perception process, and then construct JND model by fusing low-level and Semantic-level features. Numerous simulation results verify that the proposed JND model is significantly competitive with other frontier models and highly compatible with HVS.

Uplink Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks

Uplink Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks

Optimal resource allocation in micro-organisms under periodic nutrient fluctuations

Although microorganisms often live in dynamic environments, most studies, both experimental and theoretical, are carried out under static conditions. In this work, we investigate the issue of optimal resource allocation in bacteria growing in periodic environments. We consider a dynamic model describing the microbial metabolism under varying conditions, involving a control variable quantifying the protein precursors allocation. Our objective is to determine the optimal strategies maximizing the long-term growth of cells under a piecewise-constant periodic environment. Firstly, we perform a theoretical analysis of the resulting optimal control problem (OCP), based on the application the Pontryagin’s Maximum Principle (PMP). We determine that the structure of the optimal control must be bang–bang, with possibly some singular arcs corresponding to optimal equilibria of the system. If the control presents singular arcs, then these can only be reached and left through chattering arcs. We also use a direct optimization method, implemented in the BOCOP software, to solve the studied OCP. Our study reveals that the optimal solution over a large time horizon is related to the one over a single period of the varying environment with periodic constraints. Moreover, we observe that the maximal average growth rate attainable under periodic conditions can be higher than the one under a constant environment. We further extend our analysis to conduct a qualitative comparison between the predictions from our model and some recent biological experiments on E. coli. This analysis particularly highlights the mechanisms of action of the ppGpp signaling molecule, thus providing relevant explanations of the experimental observations. In conclusion, our study corroborates previous research indicating that this molecule plays a crucial role in the regulation of resource allocation of protein precursors in E. coli.

Logistics supply of the eco-industrial parks’ residents

This paper provides a comprehensive analysis of the logistics supply for residents of eco-industrial parks and examines its influence on the competitiveness and sustainability of these parks. Eco-industrial parks, as integral components of circular economy systems, facilitate the efficient use of resources, reduction of environmental impact, and optimization of supply chain operations. The purpose of the paper is to assess the logistics systems within eco-industrial parks, identify the challenges and opportunities for enhancing operational efficiency, and explore how these strategies can be adapted to the Ukrainian context, particularly during the ongoing war. The research draws upon international case studies, focusing on the integration of advanced technologies and green logistics practices in China, Europe, and other regions. The methodology used in this paper combines inductive analysis of literature, comparative analysis, and empirical research based on existing models of logistics management within EIPs. A key part of the analysis involves the evaluation of institutional pressures and their role in promoting sustainable supply chain management. The paper examines how these practices, when applied in a coordinated and efficient manner, help to reduce costs and carbon emissions, while also improving resource-sharing between companies located within eco-industrial parks. The results show that implementing circular supply chain management significantly enhances the performance of companies operating within EIPs. Firms that adopt renewable energy sources and optimize their transportation systems using electric vehicles report a 13-14% reduction in operational costs and carbon emissions. The integration of technologies such as Geographic Information Systems and Multi-Criteria Decision-Making tools has also proven beneficial in the site selection and planning of eco-industrial parks, allowing for the optimization of logistics and resource allocation. In Ukraine, which is currently facing critical challenges due to war, adopting these strategies can strengthen industrial clusters, reduce dependence on external resources, and enhance resilience against economic shocks. The scientific novelty of this research lies in the proposed structure of the logistics supply for eco-industrial parks’ residents. The practical implications of this paper include the application of green logistics technologies, the development of self-sustaining energy networks, and the implementation of closed-loop supply chains that promote resource efficiency. Future research directions should focus on the development of logistics optimization models for Ukrainian eco-industrial parks, particularly in war-torn areas, and the assessment of their long-term economic and environmental impact. Additionally, the paper advocates for the expanded use of renewable energy sources and Geographic Information Systems-based planning tools to further improve logistics processes and reduce the ecological footprint of industrial activities. The results of this paper underscore the importance of inter-firm cooperation and the strategic use of logistics to foster both economic and environmental resilience in eco-industrial settings.

Optimizing Higher Education for Entrepreneurship Digital economy and Innovation: A Novel Spatial Analysis Approach

The paper extensively examined the intricate components underpinning innovation ability, culminating in the construction of a linear spatial model delineating innovation and entrepreneurship prowess. This paper analyzed the components of the connotation of innovation ability, then constructs a linear spatial model of innovation and entrepreneurship ability, proposes a multi-objective function model of the utilization efficiency and allocation efficiency of education resources, and uses the grey correlation algorithm The experimental simulation and model solution are carried out. The simulation results show that, through the optimization, the utilization efficiency and allocation efficiency of the educational resources for innovation and entrepreneurship for all are increased by 18.72\% and 20.98\% respectively, and tend to be in equilibrium, which can achieve the optimization of educational resources allocation. Among all people, the correlation value with ideal entrepreneurship is 0.3177, achieving the most excellent innovation and entrepreneurship education.

Evaluation and Comparison of the Performance of Different Hypervisors under Various Workloads of GIADEC

This research study evaluates and compares the performance of Hyper-V, Citrix Xen, and VMware ESXi to aid Ghana Integrated Aluminium Development Corporation (GIADEC) in informed hypervisor selection. The study focuses on identifying the optimal hypervisor for GIADEC, considering factors like effectiveness and efficient resource utilization. It aims to improve resource allocation, hypervisor selection, and performance optimization within GIADEC's IT infrastructure. Using a qualitative approach and extensive literature review, the study supports informed decision-making for hypervisor selection. Through benchmarking with Passmark Performance Test Suite and BootRacer application, each hypervisor underwent rigorous testing across various performance metrics. The study assesses performance aspects including installation and booting times, CPU, memory, disk, and network performance. Results highlight distinct advantages of each hypervisor: Hyper-V for quick virtualization deployment, VMware ESXi for superior CPU, memory, and network performance. Hyper-V excels in memory write and disk sequential read/write speeds, while VMware ESXi demonstrates low memory latency and superior network bandwidth. It is recommended that GIADEC should consider VMware ESXi due to its outstanding overall performance and suitability for diverse workload requirements. This study adds to the knowledge base on hypervisor performance evaluation and supports GIADEC's decision-making process for optimal hypervisor selection.

End-to-End Resource Allocation Management Model in Next-Generation Network: Survey

Network communication has grown rapidly with massive demands of services. Moreover, resource allocation in networking is a fundamental and crucial issue that cannot ensure the network's stability and efficiency with the myriad requirements of different services. Various vertical businesses may seek varied network services, particularly in the Fifth-Generation networks and Beyond (5G+). The pros of Fifth-Generation communication networks are to outperform 4G in performance by having higher bandwidth, minimum latency, more capacity, and QoS (Quality of Service). Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two technologies that are combined in the next generation cellular network to provide improved network management. The primary idea behind resource allocation (RA) in the next generation network is the concept of network slicing where the network resources are virtually partitioning into many separate networks. Each separated network must satisfy the unique needs of the required service to achieve the required QoS. In this survey, we focus on resource management issues related to network slicing and tackling the biggest obstacles in this field while offering a thorough and up-to-date overview of this field. Thus, thorough analysis of the allocation of resources on the access side and core side of the network communication was sought. Also, demonstrates how revolutionary techniques that are used to support the management of sliced networks which are based on Machine Learning (ML) and Artificial Intelligence (AI). Importantly, use appropriate ML techniques such as deep learning for predicting the network condition and Reinforcement learning to learn optimal allocation policy without depending on prior knowledge and other techniques such as classification and clustering to aggregate the similar needs of users into separate slices. This could help to enhance resource utilization by allocating a sufficient amount of resource as needed based on ML algorithms and optimal utilization of resources and reducing operational costs by real-time adjustment of it based on user demands and network conditions

Cost - benefit analysis of DISCOs by Optimal allocation of DGs and DSTATCOM using GTO algorithm

Electric power Distribution Companies (DISCOs) provide low-cost, reliable, and consistent voltage power from distribution substations. DISCOs have radial transmission lines with all buses as load buses and no generating buses. The voltage at each bus decreases, resulting in larger network losses and voltage variations, which lowers DISCO and customer costs and benefits. This study strategically uses Distributed Generation (DGs) and DSTATCOM to optimize cost-benefit analysis and voltage stability for Distribution Companies (DISCOs). We planned DG unit and DSTATCOM deployment using a new and comprehensive Group Teaching Optimization (GTO) method in this study. The algorithm considers Distribution Company and DG Owner profitability simultaneously. Group Teaching Optimization (GTO) is used on a 33-node test system. MATLAB simulations show the GTO's usefulness in Distribution System Operators.

Стратегирование развития малого бизнеса в Российской Федерации

At the present stage of development of the Russian economy, under conditions of increasing external and internal challenges, such as sanctions and limited access to foreign technologies, there is an increasing need for the development and implementation of strategies that promote the sustainable development of small businesses. These circumstances are of particular relevance in the context of the desire to ensure economic independence and increase the competitiveness of the national economy. In this regard, this study is aimed at identifying and systematizing the basic principles of strategic management, adapted to the operating conditions of small businesses in Russia. The authors of the article focus on the theoretical foundations of strategic management, including SWOT analysis, macro-environmental analysis and strategic planning, and their applicability to small businesses. Based on an analysis of scientific literature, monographs and practical studies, the authors identified key aspects that should be taken into account when formulating development strategies for small enterprises. The article highlights and details the main practical recommendations for increasing the sustainability and competitiveness of small businesses, including adaptation to changing external and internal conditions, as well as optimal allocation of resources and innovative development. In conclusion, it is concluded that strategic planning and management, based on in-depth and comprehensive analysis, play a key role in ensuring the long-term development of small businesses in Russia.

English

English

Multi-objective energy management strategy for a dual-stack fuel cell heavy-duty truck via deep reinforcement learning

The Proton Exchange Membrane Fuel Cell (PEMFC) system, as an efficient, zero-pollution emission, and high-efficiency energy conversion device, is particularly suitable for heavy-duty transportation. Considering the limited power of the current PEMFC system, this study focuses on a hybrid fuel cell heavy-duty truck with dual stacks. Considering the optimal operating range of the PEMFC system and the health status of the lithium-ion battery pack, an energy management strategy (EMS) based on the Deep Deterministic Policy Gradient (DDPG) algorithm is established. This strategy optimizes the hydrogen consumption cost, degradation costs of lithium-ion battery and fuel cells. Additionally, a dynamic programming (DP) and an equivalent consumption minimization strategy (ECMS) based EMSs are established. The power allocation and overall transportation costs under the CSC driving cycle are compared and verified for the three EMSs. The results show that the DDPG algorithm provides a more optimal power allocation for the dual-stack fuel cells, improving the overall system efficiency, enhancing the lifecycle of heavy-duty trucks, and significantly reducing overall travel costs.

Interaction between product strategy selection and the carbon quota allocation policy

The interaction between the government’s carbon reduction policy and a firm’s product strategy has not been well studied in the literature. This paper considers the government’s two different carbon quota allocation policies for the cap-and-trade scheme and the firm’s two product strategies and investigates the interaction between them by establishing a theoretical model and deriving the optimal decisions. This paper first examines the firms’ selection of low-carbon products or ordinary product strategies under the government’s two carbon quota allocation policies and then studies the government’s optimal carbon quota allocation policy for overall social welfare, which is based on the firm’s two product strategies. Our analysis reveals that (i) when the government allocates carbon quotas aimed at reducing the firm’s total carbon emissions, the firm will choose the low-carbon product strategy. When the government allocates a carbon quota aimed at optimal total social welfare, the firm’s decision depends on the impact of total carbon emissions. (ii) To achieve optimal social welfare, the government will formulate different carbon quota allocation policies on the basis of firms’ different product strategies.