Research on the Status Quo and Countermeasures of Human Resource Management Outsourcing in China’s Pharmaceutical Enterprises

Objective To explore the current situation of human resource management outsourcing in China’s pharmaceutical enterprises,and to put forward some suggestions for enterprises and the government.Methods The current situation of human resource management outsourcing in China’s pharmaceutical enterprises was analyzed through the method of literature research.Results and Conclusion At present,the status of human resource management outsourcing in China’s pharmaceutical companies is that the level of human resource outsourcing companies is not high,and there are no relevant industry norms and laws.The information asymmetry between pharmaceutical enterprises and outsourcing companies results in adverse selection and moral hazard.Besides,the different culture of pharmaceutical enterprises and outsourcing companies leads to inefficient communication between enterprises and employee.To solve these problems,the government should promote and improve industry norms and laws to regulate the market.In addition,enterprises should clarify the motivation for outsourcing and make good decision on the outsourcing content.Meanwhile,enterprises should strengthen communication with employees to eliminate employees’concerns.

Research on Enterprise Human Resource Management Reform Strategy in the Era of Big Data

In the 21st century,with the development of the Internet,mobile devices,and information technology,society has entered a new era:the era of big data.With the help of big data technology,enterprises can obtain massive market and consumer data,realize in-depth analysis of business and market,and enable enterprises to have a deeper understanding of consumer needs,preferences,and behaviors.At the same time,big data technology can also help enterprises carry out human resource management innovation and improve the performance and competitiveness of enterprises.Of course,from another perspective,enterprises in this era are also facing severe challenges.In the face of massive data processing and analysis,it requires superb data processing and analysis capabilities.Secondly,enterprises need to reconstruct their management system to adapt to the changes in the era of big data.Enterprises must treat data as assets and establish a perfect data management system.In addition,enterprises also need to pay attention to protecting customer privacy and data security to avoid data leakage and abuse.In this context,this paper will explore the thinking of enterprise human resource management innovation in the era of big data,and put forward some suggestions on enterprise human resource management innovation.

Exploring the Digital Path of Human Resource Management in Innovation and Entrepreneurship Enterprises

The digitization of human resource management functions can enhance the adaptability of human resource management and promote the comprehensive digital development of innovative and entrepreneurial enterprises.This article first summarizes and evaluates the connotation and research status of the digitalization of enterprise human resource management.Secondly,it analyzes the influencing factors of digitalization of human resource management in innovative and entrepreneurial enterprises from three aspects:employee factors,organizational factors,and technological factors.Finally,it designs a digitalization plan for human resource management in innovative and entrepreneurial enterprises from four aspects:analyzing the current situation,preparing thoroughly,designing plans,and building systems.It emphasizes the value of digitalization of enterprise human resource management for innovative and entrepreneurial enterprises.

Shipbuilding—An Analysis from the Perspective of Enterprise Resources Planning System

Designed as a tool for business intelligence, resource planning is used to manage the essentials of a business by integrating processes into a single system. Enterprise Resource Planning (ERP) solutions ensure all the organizational, operational, and procedural structures interconnection, which can be both an advantage and a disadvantage, knowing that inefficiency within one structure will lead to inefficiency in others. The originality of the approach consists in the unitary analysis of all processes which shipbuilding involves (design;evaluation of ship construction possibilities;contracting of works;contracting of supply of materials and equipment;presentation of inspections for the completed works reception;delivery of the ship into operation). The systemic approach is a notable contribution by highlighting the input and output quantities for the analyzed processes. Also, where it was found relevant, the diagrams highlight the owners of the process and show the interconnectivity of the functional departments within a shipyard. Although it is obviously the expression of in-depth knowledge of the peculiarities of Romanian shipyards, the authors hope that the work can support any shipyard that may be interested in knowing these structures (as a logical scheme mandatory to design a custom ERP software system).

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.

Tasks-Oriented Joint Resource Allocation Scheme for the Internet of Vehicles with Sensing, Communication and Computing Integration

With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a trend.However,the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems.In view of the above challenges,this paper proposes a tasks-oriented joint resource allocation scheme(TOJRAS)in the scenario of Io V.First,this paper proposes a system model with sensing,communication,and computing integration for multiple intelligent tasks with different requirements in the Io V.Secondly,joint resource allocation problems for real-time tasks and delay-tolerant tasks in the Io V are constructed respectively,including communication,computing and caching resources.Thirdly,a distributed deep Q-network(DDQN)based algorithm is proposed to solve the optimization problems,and the convergence and complexity of the algorithm are discussed.Finally,the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme,compared to the existing ones.The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%,and our proposed resource allocation scheme improves the m AP performance by about 0.15 under resource constraints.

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.

Computation of PoA for Selfish Node Detection and Resource Allocation Using Game Theory

The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks(DCN).As the network has the characteristics like decentralized and dynamic,few nodes in the network may not associate with other nodes.These uncooperative nodes also known as selfish nodes corrupt the performance of the cooperative nodes.Namely,the nodes cause congestion,high delay,security concerns,and resource depletion.This study presents an effective selfish node detection method to address these problems.The Price of Anarchy(PoA)and the Price of Stability(PoS)in Game Theory with the Presence of Nash Equilibrium(NE)are discussed for the Selfish Node Detection.This is a novel experiment to detect selfish nodes in a network using PoA.Moreover,the least response dynamic-based Capacitated Selfish Resource Allocation(CSRA)game is introduced to improve resource usage among the nodes.The suggested strategy is simulated using the Solar Winds simulator,and the simulation results show that,when compared to earlier methods,the new scheme offers promising performance in terms of delivery rate,delay,and throughput.

An Empirical Study of the Allocation Efficiency of Rural Financial Resources in Hubei Province from the Perspective of Agricultural Loans

The incompatibility of China's economy and finance has to some extent inhibited the development of rural economy. Taking Hubei Province for example,we measure the allocation efficiency of rural financial resources from the perspective of agricultural input and output,and use the modern rural financial development theory to set forth some policy recommendations on how to build a new rural financial resource allocation system. Studies have shown that the allocation efficiency of rural financial resources is low in China,and improving the allocation efficiency of rural financial resources is the key to perfecting rural financial environment while increasing financial support for agriculture.

Fundamental issues and solutions in the design of China's ETS pilots:Allowance allocation,price mechanism and state-owned key enterprises

This paper analyzes the three main fundamental issues in the design of China's ETS pilots,including allowance allocation,price mechanism and state-owned key enterprises,and proposed suggested solutions.For the issue of allowance allocation,we suggest that the gradual hybrid mode could be applied at the beginning,which starts with mainly free allocation and then increases auction ratio gradually.And grandfathering is a suitable method of free allocation.For the issue of price mechanism,we suggest a price floating zone with open market operation to reduce the uncertainty of prices.For the issue of state-owned key enterprises,we suggest a good coordination with SASAC,defining the state-owned property right and supervision right when state-owned key enterprises are involved into the carbon market,and the local government can set rules of allocation and transaction to limit their potential market power.

The Optimization of Manufacturing Resources Allocation Considering the Geographical Distribution

From the perspective of the geographical distribution, considering production fare, supply chain information and quality rating of the manufacturing resource(MR), a manufacturing resource allocation(MRA) model considering the geographical distribution in cloud manufacturing(CM) environment is built. The model includes two stages, preliminary selection stage and optimal selection stage. The membership function is used to select MRs from cloud resource pool(CRP) in the first stage, and then the candidate resource pool is built. In the optimal selection stage, a multi-objective optimization algorithm, particle swarm optimization(PSO) based on the method of relative entropy of fuzzy sets(REFS_PSO), is used to select optimal MRs from the candidate resource pool, and an optimal manufacturing resource supply chain is obtained at last. To verify the performance of REFS_PSO, NSGA-Ⅱ and PSO based on random weighting(RW_PSO) are selected as the comparison algorithms. They all are used to select optimal MRs at the second stage. The experimental results show solution obtained by REFS_PSO is the best. The model and the method proposed are appropriate for MRA in CM.

Coordinated optimal allocation of water resources and industrial structure in the Beijing-Tianjin-Hebei regions of China

Optimizing the allocation of water resources is critical for promoting the optimization and upgrading of industrial structure and coordinated development in the Beijing-Tianjin-Hebei regions of China.Based on specific regional and water conditions,to strengthen the constraints on water resources,the“three-step”adaptive management approach of“scheme design-scheme diagnosis-scheme optimization”of water resource allocation are adopted to facilitate the coordinated optimal allocation of water resources and industrial structure in the Beijing-Tianjin-Hebei regions.First,from the level of overall industry,a water resource allocation scheme for the regions is designed by applying the master-slave hierarchical mode and a bi-level optimal model to determine the ideal amount of water resource allocation for the regions and respective industries.Second,the diagnostic criteria of spatial balance,structural matching,and coordinated development are constructed to determine the rationality of the water resource allocation scheme.Then a benefit compensation function with water market transactions is developed,to adaptively adjust the water resource allocation scheme.Finally,the optimization and upgrading of industrial structure are promoted to improve water consumption efficiency and the coordinated development of the Beijing-Tianjin-Hebei regions.The study can provide reference for the Beijing-Tianjin-Hebei regions to realize the comprehensive optimal allocation of water resources in the regions and improve the adaptability of water resources and industrial structure optimization.

Optimal allocation of land resources in the process of economic development

In this paper,the writer uses a mathematical model to analyze:a theoretical model of land resources optimal allocation with the constraint of sustainable development;equilibrium and defects of land resources allocation in a competitive market;and how effective governmental supervision can change the equilibrium in the market and promote the optimization of land resources allocation.The main points of this paper are:continuous and excessive conversions that change land resources from agricultural use to non-agricultural use in the process of economic development are economic rules;a competitive market is an important way to improve the efficiency of land resources allocation;effective governmental supervision can cover the shortage of market and promote the optimization of land resources allocation;a reasonable arrangement of land property rights can reduce the transaction costs of government management in optimizing land resources allocation;and,the targets of land resources optimal allocation are developing along with economic development.

An Adaptive Hybrid Optimization Strategy for Resource Allocation in Network Function Virtualization

With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation.

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

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.