Optimization of Digital Management Path for Human Resource Performance Evaluation Based on Multiobjective Decision-Making Mathematical Model

Liu, Li; Yong, Seechen

doi:10.1155/2022/2604761

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Then through 5G communication, cloud computing, big data, neural networks, user portraits, and other smart digital means, the author designed a few strategies for the digital transformation of HRM and proposed targeted measures for raising awareness of HRM and creating a culture of HRM. Liu and Yong [20] analyzed the actual requirements, business procedures, and existing problems of the performance management in modern enterprises and used a multi-objective decision-making model to calculate the optimal solution for the performance management of HRM.The paper also designed a digital management system for corporate performance assessment.…”

Section: Introductionmentioning

confidence: 99%

Risk Prediction of Digital Human Resource Management Based on Artificial Intelligence

2023

CIT. J. comput. inf. technol

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The latest information technologies have greatly accelerated the digitalization progress of Human Resource Management (HRM) and many useful techniques and tools have been developed for that purpose. However, in terms of risk management, effective enough tools and methods are still insufficient. Existing studies generally fail to give a turnkey solution to the operational risks in digital HRM system, and the macro measurement models are not suitable for dealing with the risks in the digital HRM system of each single enterprise. In view of these defects, this paper studied the prediction of risks in digital HRM systems based on Artificial Intelligence (AI). Firstly, the paper outlined the functions of a digital HRM system, defined the risk management mechanism of a HRM system, and built a conceptual model for it. Then, this paper proposed a novel method for predicting the risks in the digital HRM system, which innovatively integrates the digital HRM risk event chains with the risk event graph. After that, the paper elaborated on the structures and building principles of the risk event representation layer, risk event chain module, risk event graph module, and attention fusion module. At last, experimental results verified that the proposed model has obvious advantages in digital HRM risk prediction in terms of both stability and accuracy.

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Section: Introductionmentioning

confidence: 99%

Risk Prediction of Digital Human Resource Management Based on Artificial Intelligence

2023

CIT. J. comput. inf. technol

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A new heuristic method for solving unbalanced multi-objective assignment problem

Fouad,

Kassam,

Al-Zubaidi

2024

Eng. Res. Express

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In the era of globalization, institutions face increasing challenges in effectively managing their resources to achieve diverse and differentiated objectives. The unbalanced multi-objective assignment problem reflects these challenges, as the distribution of limited resources requires achieving a balance between objectives such as minimizing time, cost, etc. In this study, an application case in the industrial field of rolling process was investigated, where the objective of the assignment problem is to minimize total assignment. In order to tackle this problem, the multi-objective problem was addressed by converting it into a single-objective problem through the weighted sum method by assigning a weight to each objective. A new heuristic method was then applied to solve the unbalanced single-objective assignment problem where the machines were loaded with more than one job. The Hungarian method was also used to solve the problem in order to assess the proposed method, and the outcomes were then compared. The proposed method proved efficient as it reduced idle time by 28%, increased machine efficiency by 5% and reduced costs by an average of 15%.

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Optimizing Labor Allocation based on Multiobjective Decision Making Using Improved Hungarian Algorithm

Tran,

Nguyen

2023

JES

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<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: arial, helvetica, sans-serif;">The aim of this study is to solve the problem of labor allocation in cases where the company has fewer employees than the number of existing jobs based on the evaluation of work quality according to multiple objectives. Optimizing labor allocation not only benefits the company by maximizing the use of human resources, but also saves employees' energy. In addition, employees are assigned to tasks that match their skills and qualifications, maximizing their productivity. The research results show that the multi-objective decision-making algorithm based on the Hungarian algorithm is a suitable method to help leaders of companies solve the aforementioned problem text.</span></p>

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Optimization of Digital Management Path for Human Resource Performance Evaluation Based on Multiobjective Decision-Making Mathematical Model

Cited by 3 publications

References 21 publications

Risk Prediction of Digital Human Resource Management Based on Artificial Intelligence

Risk Prediction of Digital Human Resource Management Based on Artificial Intelligence

A new heuristic method for solving unbalanced multi-objective assignment problem

Optimizing Labor Allocation based on Multiobjective Decision Making Using Improved Hungarian Algorithm

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