Intelligent Manufacturing Process Optimization Based on Multi-Department Collaborative Coupling Model

This study aims to explore the effective method of optimizing the whole process of intelligent manufacturing, and to model and optimize the intelligent manufacturing process of complex equipment in detail based on Anylogic simulation platform. Firstly, a simulation model covering the whole process of “design-development-test” is designed in this paper, and the operational efficiency and synergistic effectiveness of each stage are comprehensively evaluated by dynamically simulating the information flow, task flow and resource flow of each production link. Secondly, combined with the TOPSIS hierarchical analysis method, a multi-scenario optimization design is carried out on the simulation results, and the information flow and resource allocation are optimized through the weighting analysis of the key indexes, which improves the operational efficiency of the whole manufacturing process. In addition, the study introduces the theory of complex network nodes, which simplifies the key links in the simulation process and improves the operability and practical application value of the model. Finally, based on the simulation results, a specific implementation scheme to improve the resilience of the manufacturing chain is proposed, which can effectively monitor the production process, quickly identify the sources of failure and optimize the production decision.

Authors

Institutions

Publication Details

Published
2025-06-28
DOI
https://doi.org/10.1109/icipca65645.2025.11138605
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intelligent Manufacturing Process Optimization Based on Multi-Department Collaborative Coupling Model

Jingyao Xia, Shuyu Xie, Minghao Li, Jing Wang et al.
Manufacturing Process and Optimization
article

Intelligent Manufacturing Process Optimization Based on Multi-Department Collaborative Coupling Model

Jingyao Xia, Shuyu Xie, Minghao Li, Jing Wang, Zhan Shi, Tao Xia
article en

Abstract

This study aims to explore the effective method of optimizing the whole process of intelligent manufacturing, and to model and optimize the intelligent manufacturing process of complex equipment in detail based on Anylogic simulation platform. Firstly, a simulation model covering the whole process of “design-development-test” is designed in this paper, and the operational efficiency and synergistic effectiveness of each stage are comprehensively evaluated by dynamically simulating the information flow, task flow and resource flow of each production link. Secondly, combined with the TOPSIS hierarchical analysis method, a multi-scenario optimization design is carried out on the simulation results, and the information flow and resource allocation are optimized through the weighting analysis of the key indexes, which improves the operational efficiency of the whole manufacturing process. In addition, the study introduces the theory of complex network nodes, which simplifies the key links in the simulation process and improves the operability and practical application value of the model. Finally, based on the simulation results, a specific implementation scheme to improve the resilience of the manufacturing chain is proposed, which can effectively monitor the production process, quickly identify the sources of failure and optimize the production decision.

National University of Defense Technology (CN)
Openalex Percentile: Top 66%
Manufacturing Process and Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.