Diverse policy generation for the flexible job-shop scheduling problem via deep reinforcement learning with a novel graph representation

Authors

Publication Details

Journal
Engineering Applications of Artificial Intelligence
Published
2024-10-24
DOI
https://doi.org/10.1016/j.engappai.2024.109488
Citations
9
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
3.14
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article

Diverse policy generation for the flexible job-shop scheduling problem via deep reinforcement learning with a novel graph representation

Imanol Echeverria, Roberto Santana, Maialen Murua
9 citations
Engineering Applications of Artificial Intelligence
Scheduling and Optimization Algorithms
3.14
article

Diverse policy generation for the flexible job-shop scheduling problem via deep reinforcement learning with a novel graph representation

Imanol Echeverria, Roberto Santana, Maialen Murua
article en
9 citations

Abstract

No abstract available for this paper.
Engineering Applications of Artificial IntelligenceVol. 139
Decent work and economic growth
Openalex Percentile: Top 8%
Scheduling and Optimization Algorithms
3.14
DOIOpen Access PDFOpenAlex
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Diverse policy generation for the flexible job-shop scheduling problem via deep reinforcement learning with a novel graph representation — Imanol Echeverria, Roberto Santana, et al. · Engineering Applications of Artificial Intelligence (2024) | TGRS Research Map | TGRS