Computational Intelligence and Neuroscience

Human Behavior Modelling in Engineering Management under Industry 4.0


Publishing date
01 Aug 2022
Status
Published
Submission deadline
08 Apr 2022

Lead Editor

1Central South University, Changsha, China

2City University of Hong Kong, Hong Kong

3Xi'an University of Architecture and Technology, Xian, China

4China University of Mining and Technology, Xuzhou, China

5London South Bank University, London, UK


Human Behavior Modelling in Engineering Management under Industry 4.0

Description

Human society is entering the era of Industry 4.0. Automatic production and intelligent manufacturing are proliferating rapidly across industries, freeing the workforce from simple but repetitive work. The roles of humans in engineering systems are continuously changing. In addition, engineering systems are becoming more intelligent and complex, which further increases the difficulties in engineering management. Engineering management is facing unprecedented opportunities and challenges. As humans are the most important and determining factor of management, human behavior can significantly affect the efficiency of engineering management.

However, it is usually not easy to understand the mechanism of decision-making process and human behavior in engineering management. This often requires interdisciplinary knowledge in engineering, management, neural engineering, mathematics, and psychology. Efficient tools for human behavior modelling and simulation are urgently needed. With the development of mathematical algorithms and computational technologies, intelligent modelling and simulation bring innovation to research on human behavior in engineering management. Specifically, advanced modelling and simulation methods, such as machine learning, deep learning, big data, system dynamics, and agent-based modelling, enable revealing the mechanism of decision-making process and predicting human behavior under various circumstances. Intelligent modelling and simulation provide valuable information and databases for managers to improve the efficiency of engineering management. There should be continuous research exploring applying intelligent techniques to model and simulate human behavior in engineering management.

The aim of this Special Issue is to solicit research and offer a timely opportunity to scholars and industry practitioners to discuss, share, and disseminate current innovations in intelligent modelling and simulation techniques for human behavior in engineering management under Industry 4.0. Authors are invited to present original research and review articles that will stimulate continuing efforts in this field. We hope that this Special Issue helps solve practical engineering problems and bring theoretical contributions.

Potential topics include but are not limited to the following:

  • Human behavior changes in engineering management under Industry 4.0
  • Intelligent modelling and simulation methods for human behavior in engineering management
  • Dynamic relationships between human behavior and the efficiency of engineering management under Industry 4.0
  • Machine learning and deep learning for human behavior in engineering management
  • Data mining for human behavior in engineering management
  • System dynamics for human behavior in engineering management
  • Agent-based modelling for human behaviour in engineering management
  • Complex employee relationship network modelling in engineering management under Industry 4.0
  • Intelligent prediction, monitoring, assessment, and management of human behavior in engineering management
  • Data-driven and intelligent decision support systems in engineering management under Industry 4.0

Articles

  • Special Issue
  • - Volume 2022
  • - Article ID 6092802
  • - Research Article

Four-Way Evolutionary Game Analysis of Government Project Bidding Collusion in a State of Limited Rationality Based on Prospect Theory

Chongsen Ma | Yun Chen | Sirui Nie
  • Special Issue
  • - Volume 2022
  • - Article ID 3376296
  • - Research Article

Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence

Liangliang Wang
  • Special Issue
  • - Volume 2022
  • - Article ID 8589396
  • - Research Article

Coordinated Development of Population, Resources, Environment, Economy, and Society under Engineering Management Combined with Bilevel Optimization Model

Kai Chen | Yilin Chen
  • Special Issue
  • - Volume 2022
  • - Article ID 4064747
  • - Research Article

Quality Evaluation Model for Smart City Social Sports Information Cloud Service

Lan Zhang
  • Special Issue
  • - Volume 2022
  • - Article ID 6761857
  • - Research Article

Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

Ruan Hui
  • Special Issue
  • - Volume 2022
  • - Article ID 9932603
  • - Research Article

Cost Control of Mining Personnel Based on Wireless Communication Network from the Perspective of Operations Research

Hongyi Wang | Meichang Zhang
  • Special Issue
  • - Volume 2022
  • - Article ID 2586307
  • - Research Article

[Retracted] Value Proposition for Enabling Construction Project Innovation by Applying Building Information Modeling

Hui Liu | Qianqian Ju | ... | Miroslaw J. Skibniewski
  • Special Issue
  • - Volume 2022
  • - Article ID 8522751
  • - Research Article

Smart Garden Planning and Design Based on the Agricultural Internet of Things

Yi Xun | Guangpei Ren
  • Special Issue
  • - Volume 2022
  • - Article ID 9965427
  • - Research Article

Research on Artificial Intelligence Classification and Statistical Methods of Financial Data in Smart Cities

Xuezhong Fu
  • Special Issue
  • - Volume 2022
  • - Article ID 4851615
  • - Research Article

Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification

Shu Chen | Junbo Xi | ... | Jinfan Zhao

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