Task Scheduling Strategy for 3DPCP Considering Multidynamic Information Perturbation in Green Scene

The 3D printing cloud platform (3DPCP) plays a pivotal role in breaking down the information silos between supply and demand, effectively reducing waste through information integration and intelligent production. However, due to the complexity of 3DPCP scheduling in green scenes and the multidynamic information perturbations, unveils problems in traditional task scheduling methods in 3DPCP. These issues manifest as incomplete considerations, subpar green performance, and weak adaptability to dynamic changes. There is an urgent need to design practical methods to realize the multidynamic information perturbations in green scenes within 3DPCP. Therefore, this article first defines the 3DPCP task scheduling problem for multidynamic information perturbation in green scenes. Second, the article proposes a task scheduling model and a heuristic task scheduling strategy to minimize both the average cost and carbon dioxide (CO2) emissions per unit of quality product. Finally, the article validates effectiveness and superiority of the proposed strategy through simulation experiments.

Authors

Institutions

Publication Details

Journal
Journal of Global Information Management
Published
2024-08-09
DOI
https://doi.org/10.4018/jgim.351156
Citations
3
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
Field-Weighted Citation Impact
0.61
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Task Scheduling Strategy for 3DPCP Considering Multidynamic Information Perturbation in Green Scene

Keng Siau, Jianjia He, Jian Wu
3 citations
Journal of Global Information Management
Additive Manufacturing and 3D Printing Technologies
0.61
article

Task Scheduling Strategy for 3DPCP Considering Multidynamic Information Perturbation in Green Scene

Keng Siau, Jianjia He, Jian Wu
article en
3 citations

Abstract

The 3D printing cloud platform (3DPCP) plays a pivotal role in breaking down the information silos between supply and demand, effectively reducing waste through information integration and intelligent production. However, due to the complexity of 3DPCP scheduling in green scenes and the multidynamic information perturbations, unveils problems in traditional task scheduling methods in 3DPCP. These issues manifest as incomplete considerations, subpar green performance, and weak adaptability to dynamic changes. There is an urgent need to design practical methods to realize the multidynamic information perturbations in green scenes within 3DPCP. Therefore, this article first defines the 3DPCP task scheduling problem for multidynamic information perturbation in green scenes. Second, the article proposes a task scheduling model and a heuristic task scheduling strategy to minimize both the average cost and carbon dioxide (CO2) emissions per unit of quality product. Finally, the article validates effectiveness and superiority of the proposed strategy through simulation experiments.

Journal of Global Information ManagementVol. 32(1)
University of Shanghai for Science and Technology (CN), City University of Hong Kong (HK)
Responsible consumption and production
Openalex Percentile: Top 34%
Additive Manufacturing and 3D Printing Technologies
0.61
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.