arXiv (All)
open access
An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources
Efficient job-shop scheduling with transportation resources is critical for high-performance manufacturing. With the rise of "decentralized factories", multi-agent reinforcement learning has emerged as a promising approach for the combined scheduling of production and transportation tasks. Prior wor
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training#423982
- Ensuring Data Freshness in Multi-Rate Task Chains Scheduling#2727
- LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturing#1156636
- Distributed Optimization with Streaming Data: A Temporal Weighting Perspective#441814
- Multi-Agent Agentic Graph Learning via Structural Signatures#987303
Record · ID 1050865
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive