Human resources allocation for aircraft maintenance with predefined sequence

There are always large-scale items in the maintenances schedule of aircraft system, many of which have been fixed to be done in predefined sequences, which leads the workflow to be sys-tematically complex and makes this kind of problem quite different from all sorts of existing job-selection modes. On the other hand, the human resources are always limited and men have different working capabilities on different items, which make the allocation operation of human resources be much roomy. However, the final total time span of maintenance is often required to be as short as possible in many practices, in order to suffer only the lowest cost of loss while the system is stopping. A new model for op-timizing the allocation if aircraft maintenance human resources with the constraint of predefined sequence is presented. The ge-netic algorithm is employed to find the optimal solution that holds the shortest total time span of maintenance. To generate the ul-timate maintenance work items and the human resource array, the sequences among all maintenance work items are considered firstly, the work item array is then generated through traversal with the constraint of maintenance sequence matrix, and the human resources are finally allocated according to the work item array with the constraint of the maintenance capability. An example is demonstrated to show that the model and algorithm behave a satisfying performance on finding the optimal solution as expected.

Joint Optimization of Communications and Computing Resources Allocation for Deterministic Transmission in Wireless Edge Networks

Motivated by 5th generation wireless systems(5G),a large number of emerging applications appear,which put forward higher requirements for the task’s transmission determinacy,which refers to the delay and jitter.To satisfy the deterministic requirement,mobile edge computing(MEC)is envisioned as a promising technique for reducing the end-to-end delay significantly.In this paper,we consider delaysensitive task and jitter-sensitive task,and then formulate the joint communications and computing optimization problem under the latency,the total bandwidth,the total transmission power of base station(BS)and the computing ability of the MEC server constraints to minimize the delay and jitter in a multiuser MEC system.Because of the problems are nonconvex,we decouple them into some subproblems and propose the corresponding algorithms to obtain a suboptimal solution.Finally,numerical results show that the proposed algorithms have a significant performance gain over the traditional solution in terms of the delay and the jitter.

Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System

The high renewable penetrated power system has severe frequency regulation problems.Distributed resources can provide frequency regulation services but are limited by com-munication time delay.This paper proposes a communication resources allocation model to reduce communication time delay in frequency regulation service.Communication device resources and wireless spectrum resources are allocated to distributed resources when they participate in frequency regulation.We reveal impact of communication resources allocation on time delay reduction and frequency regulation performance.Besides,we study communication resources allocation solution in high renewable energy penetrated power systems.We provide a case study based on the HRP-38 system.Results show communication time delay decreases distributed resources'ability to provide frequency regulation service.On the other hand,allocating more communication resources to distributed resources'communica-tion services improves their frequency regulation performance.For power systems with renewable energy penetration above 70%,required communications resources are about five times as many as 30%renewable energy penetrated power systems to keep frequency performance the same.Index Terms-Communication resources allocation,commun-ication time delay,distributed resource,frequency regulation,high renewable energy penetrated power system.

Assessment of radiotherapy and diagnosis resources allocation in Shanghai,China

Objective:To evaluate the equity of medical resources in radiotherapy and diagnosis in Shanghai,China,based on population,geography,and economic factors.Methods:Data on medical resources including institutions,equipment,and staff in radiotherapy/diagnosis were collected from all 16 districts of Shanghai,China in 2022.Separate data were collected for institutions and devices in CT.The Gini coefficient(G)and Lorenz curves were used to assess fairness based on population and geography,while the Theil index(T)was employed to evaluate health equity based on economic factors.Health resource agglomeration degree(HRAD)and population agglomeration degree(PAD)were utilized to analyze the equity and accessibility of medical resources considering both population and geography.Results:In 2022,Shanghai had a total of 992 institutions,4,925 devices,and 10,282 personnel in radiotherapy and diagnosis.Additionally,there were 381 institutions conducting CT examinations and 776 CT machines in Shanghai.The Gini coefficients for institutions,devices,and personnel in radiotherapy and diagnosis based on population ranged from 0.2 to 0.4,while for CT,the Gini coefficients for institutions and devices ranged from 0.2 to 0.4.When considering geography,all Gini coefficients were greater than 0.5.The results of the Theil index indicated that inequities in distribution may be influenced by economic factors.The HRAD and PAD revealed disparities in the accessibility of institutions,devices,and personnel in radiotherapy/diagnosis and CT in Shanghai.Conclusions:Inequities in the distribution of institutions,equipment,and personnel for radiotherapy/diagnosis and CT were observed in Shanghai in 2022,both geographically and economically.There is a critical need to enhance the allocation of resources for radiological equipment and personnel and to establish a scientifically robust urban resource planning framework.

Joint Allocation of Computing and Connectivity Resources in Survivable Inter-Datacenter Elastic Optical Networks

Inter-datacenter elastic optical networks(EON)need to provide the service for the requests of cloud computing that require not only connectivity and computing resources but also network survivability.In this paper,to realize joint allocation of computing and connectivity resources in survivable inter-datacenter EONs,a survivable routing,modulation level,spectrum,and computing resource allocation algorithm(SRMLSCRA)algorithm and three datacenter selection strategies,i.e.Computing Resource First(CRF),Shortest Path First(SPF)and Random Destination(RD),are proposed for different scenarios.Unicast and manycast are applied to the communication of computing requests,and the routing strategies are calculated respectively.Simulation results show that SRMLCRA-CRF can serve the largest amount of protected computing tasks,and the requested calculation blocking probability is reduced by 29.2%,28.3%and 30.5%compared with SRMLSCRA-SPF,SRMLSCRA-RD and the benchmark EPS-RMSA algorithms respectively.Therefore,it is more applicable to the networks with huge calculations.Besides,SRMLSCRA-SPF consumes the least spectrum,thereby exhibiting its suitability for scenarios where the amount of calculation is small and communication resources are scarce.The results demonstrate that the proposed methods realize the joint allocation of computing and connectivity resources,and could provide efficient protection for services under single-link failure and occupy less spectrum.

Resources Allocation Schemas for Web Information Monitoring

The web is an extremely dynamic world where information is updated even every second. A web information monitoring system fetches information from the web continuously and finds changes by compar- ing two versions of the same page. The updating of a specific web page is modeled as a Poisson process with parameter to indicate the change frequency. As the amount of computing resources is limited, it is nec- essary to find some policies for reducing the overall change-detection time. Different allocation schemas are evaluated experimentally to find out which one is the most suitable for the web information monitoring prob- lem. The experimental data shows the runtime characteristics of the overall system performance and the re- lationship to the total amount of resources.

Theory and model of water resources complex adaptive allocation system

Complex adaptive sys tem theory is a new and important embranchment of system science, which prov ides a new thought to research water resources allocation system. Based on the a nalysis of complexity and complex adaptive mechanism of water resources allocat ion system, a fire-new analysis model is presented in this paper. With t he description of dynamical mechanism of system, behavior characters of agents and the evaluation method of system status, an integrity research system is built to analyse the evolvement rule of water resources allocation system. A nd a brief research for the impact of water resources allocation in benefi cial regions of the Water Transfer from South to North China Project is conducted.

Prioritization methodology for roadside and guardrail improvement:Quantitative calculation of safety level and optimization of resources allocation

The attention to road safety-related issues has grown fast in recent decades. The experi- ence gained with these themes reveals the importance of considering these aspects in the resource allocation process for roadside and guardrail improvement, which is a complex process often involves conflicting objectives. This work consists on defining an innovative methodology, with the objective of calculating and analysing a numerical risk factor of a road. The method considers geometry, accident rate, traffic of the examined road and four categories of elements/defects where the resources can be allocated to improve the road safety （safety barriers, discrete obstacles, continuous obstacles, and water drainage）. The analysis allows the assessment of the hazard index, which could be used in decision- making processes. A case study is presented to analyse roadsides of a 995 km long road network, using the cost-benefit analysis, and to prioritize possible rehabilitation work. The results highlighted that it is suitable to intervene on roads belonging to higher classes of risk, where it is possible to maximize the benefit in terms of safety as consequence of rehabilitation works （i.e., new barrier installation, removal and new barrier installation, and new terminal installation）. The proposed method is quantitative; therefore, it avoids providing weak and far from reliable results; moreover, it guarantees a broad vision for the problem, giving a useful tool for road management body.

Experience Overview of Foreign Teachers in the Allocation of Resources of Compulsory Education Stage

This article briefly introduces the successful experience of American ＂Compensatory Education Program＂, British ＂Education Action Zone＂, French＂Priority Education Zone Policy＂ and Japanese ＂Regular Flow of TeachersSystem＂, which are about the optimal allocation of compulsory education teacher resources allocation. Corresponding enlightenment has been gotten based on it. In addition, this article holds the opinion that we can focus on the reference and practice of Japan, and implement the regular flow of teachers. The main reason is that China has possessed the key condition of carrying out this system.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Study on functions and rational allocation of Shule River Basin groundwater resources

Based on Investigation and Assessment on Rational Exploitation and Utilization of Groundwater Resources in Typical Areas of the Hexi Corridor, the thesis studies on groundwater and environmental problems arising from the large-scale agricultural development projects in Shule River Basin. The thesis analyzes problems in exploiting and utilizing water resources, defines the function zoning of groundwater resources in key areas and evaluates them. Finally, the thesis uses three-dimensional unsteady flow simulation and regional social and economic development plan to study on the allocation of groundwater in Shule River Basin. A proposal for rational allocation of Shule River Basin water resources has been put forward.

A SOLVABLE ONE-DIMENSIONAL ALLOCATION DECISION MODEL OF RENEWABLE RESOURCES

In the paper, the determinate atlecation decision model and the probabilistic allocation decision model of a kind of renewable resource are separatly studied by means of dynamic programming, and the optimal allocation policy is given under some special conditions.

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

The current resource allocation in 5G vehicular networks for mobile cloud communication faces several challenges,such as low user utilization,unbalanced resource allocation,and extended adaptive allocation time.We propose an adaptive allocation algorithm for mobile cloud communication resources in 5G vehicular networks to address these issues.This study analyzes the components of the 5G vehicular network architecture to determine the performance of different components.It is ascertained that the communication modes in 5G vehicular networks for mobile cloud communication include in-band and out-of-band modes.Furthermore,this study analyzes the single-hop and multi-hop modes in mobile cloud communication and calculates the resource transmission rate and bandwidth in different communication modes.The study also determines the scenario of one-way and two-way vehicle lane cloud communication network connectivity,calculates the probability of vehicle network connectivity under different mobile cloud communication radii,and determines the amount of cloud communication resources required by vehicles in different lane scenarios.Based on the communication status of users in 5G vehicular networks,this study calculates the bandwidth and transmission rate of the allocated channels using Shannon’s formula.It determines the adaptive allocation of cloud communication resources,introduces an objective function to obtain the optimal solution after allocation,and completes the adaptive allocation process.The experimental results demonstrate that,with the application of the proposed method,the maximum utilization of user communication resources reaches approximately 99%.The balance coefficient curve approaches 1,and the allocation time remains under 2 s.This indicates that the proposed method has higher adaptive allocation efficiency.

Resource Allocation for Cognitive Network Slicing in PD-SCMA System Based on Two-Way Deep Reinforcement Learning

In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.

Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions.To this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground users.The goal was to mitigate co-channel interference while maximizing long-term system utility.The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this problem.Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

A new quantum key distribution resource allocation and routing optimization scheme

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

Optimization of resource allocation in FDD massive MIMO systems

The performance of massive MIMO systems relies heavily on the availability of Channel State Information at the Transmitter(CSIT).A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CsI feedback stage.In fact,the downlink communication generally includes three stages,i.e.,pilot training,CsI feedback,and data transmission.These three stages are mutually related and jointly determine the overall system performance.Unfortunately,there exist few studies on the reduction of csIT acquisition overhead from the global point of view.In this paper,we integrate the Minimum Mean Square Error(MMSE)channel estimation,Random Vector Quantization(RVQ)based limited feedback and Maximal Ratio Combining(MRC)precoding into a unified framework for investigating the resource allocation problem.In particular,we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio(SNR)based on the deterministic equivalence theory.Then the three performance metrics(the spectral efficiency,energy efficiency,and total energy consumption)oriented problems are formulated analytically.With practical system requirements,these three metrics can be collaboratively optimized.Finally,we propose an optimization solver to derive the optimal partition of channel coherence time.Experiment results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.

Distributed Resource Allocation in Dispersed Computing Environment Based on UAV Track Inspection in Urban Rail Transit

With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety accidents.At the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices.As a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service quality.This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks.Furthermore,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints.The Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment.Subsequently,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal policy.Simultaneously,an experience replay mechanism is implemented to tackle the issue of increasing dimensionality.The experimental simulations validate the efficacy of the method across various scenarios.

Resource Allocation for IRS Assistedmm Wave Wireless Powered Sensor Networks with User Cooperation

In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.

Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.