Paper Citation Record · LEDGER

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints

As of 18 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2506.13566.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.13566 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:34:02.733025Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

verified exact1
verified fuzzy20
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4c484813-4d40-425b-9418-b86e6b30398c · outbound

This paper cites an unresolved cited work.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:34:09.409793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 56668683-2db8-46cc-98c9-861f891c5e85 · outbound

This paper cites Jodlbauer, Produktionsoptimierung: wertschaffende sowie kunde- norientierte Planung und Steuerung , 2nd ed.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Jodlbauer, Produktionsoptimierung: wertschaffende sowie kunde- norientierte Planung und Steuerung , 2nd ed

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:09.053066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cb08ba2c-6907-4daa-8746-ce4dd05932b2 · outbound

This paper cites Review of job shop scheduling research and its new perspectives under Industry 4.0,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Review of job shop scheduling research and its new perspectives under Industry 4.0,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:08.802470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 34e74294-74ce-423e-9888-fc505bb4162d · outbound

This paper cites an unresolved cited work.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:34:08.434781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 07132646-8924-4ce1-95a9-3fc4ef096c01 · outbound

This paper cites A Reinforcement Learning Environment For Job-Shop Scheduling.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints A Reinforcement Learning Environment For Job-Shop Scheduling

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T00:33:59.916151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be5bc7fd-dda5-416e-92d8-348beffc1978 · outbound

This paper cites Application of Machine Learning and Rule Scheduling in a Job-Shop Production Control System,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Application of Machine Learning and Rule Scheduling in a Job-Shop Production Control System,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:08.179279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1390604a-0584-410d-8ddf-9e2efd8e90d6 · outbound

This paper cites Reinforcement Learning of Dispatching Strategies for Large-Scale Industrial Scheduling,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Reinforcement Learning of Dispatching Strategies for Large-Scale Industrial Scheduling,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:07.883398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d9e3e37e-65cb-4f6f-8a13-0e05304fae0f · outbound

This paper cites Optimization of job shop scheduling problem based on deep reinforcement learning,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Optimization of job shop scheduling problem based on deep reinforcement learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:07.630790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cd9aa91b-8f3f-427f-94c1-076e0d80aad1 · outbound

This paper cites Reinforcement learning for an intelligent and autonomous production control of complex job-shops under time constraints,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Reinforcement learning for an intelligent and autonomous production control of complex job-shops under time constraints,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:07.412961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0b48f462-7847-4751-9f07-e806b33c6c07 · outbound

This paper cites Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:34:03.068156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 101fe69c-b102-429e-965f-914b111ef163 · outbound

This paper cites Deep Reinforcement Learning for Dynamic Flexible Job Shop Scheduling with Random Job Arrival,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Deep Reinforcement Learning for Dynamic Flexible Job Shop Scheduling with Random Job Arrival,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:07.119985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dae97216-9da0-400e-b215-a54570256dc0 · outbound

This paper cites Deep reinforcement learning for dynamic scheduling of a flexible job shop,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Deep reinforcement learning for dynamic scheduling of a flexible job shop,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:06.844604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0de49be3-c531-4701-959c-e78317b43c07 · outbound

This paper cites A deep reinforcement learn- ing model for dynamic job-shop scheduling problem with uncertain processing time,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints A deep reinforcement learn- ing model for dynamic job-shop scheduling problem with uncertain processing time,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:06.614792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 69c40738-4d09-4a60-a019-c6725c30e387 · outbound

This paper cites Real-time data-driven dy- namic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Real-time data-driven dy- namic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:06.364769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e3f1e88c-fc66-4fdd-b16f-e9cfaf1b72cf · outbound

This paper cites Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:06.066122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 39adc041-28b9-4444-ac00-d287afebab54 · outbound

This paper cites Reinforcement learning for sustainability enhancement of production lines,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Reinforcement learning for sustainability enhancement of production lines,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:05.798592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9a02902e-38d0-422a-9f00-8ea9c7d17b48 · outbound

This paper cites Efficient Multi-Objective Optimization on Dynamic Flexible Job Shop Scheduling Using Deep Reinforcement Learning Approach,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Efficient Multi-Objective Optimization on Dynamic Flexible Job Shop Scheduling Using Deep Reinforcement Learning Approach,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:05.587048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 07e75799-c2e9-44e3-8a7f-c6b061eb0f38 · outbound

This paper cites A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:05.314275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4d8dac47-a837-463a-b42e-39492aefb36d · outbound

This paper cites Job shop smart manufactur- ing scheduling by deep reinforcement learning,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Job shop smart manufactur- ing scheduling by deep reinforcement learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:05.004746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ce885ff8-10b0-4b04-9e1e-9d3b14e92aa6 · outbound

This paper cites Learning to Schedule Job- Shop Problems via Hierarchical Reinforcement Learning,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Learning to Schedule Job- Shop Problems via Hierarchical Reinforcement Learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:04.795901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7683cf27-5dab-4dac-a445-a4fdbb9c2a7c · outbound

This paper cites De- signing an adaptive production control system using reinforcement learning,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints De- signing an adaptive production control system using reinforcement learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:04.545864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bd5c499c-8bb7-4e20-a3f9-cadbb56dc986 · outbound

This paper cites Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T00:34:02.134736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c859de3-7438-4920-9ddf-57ccfea67bf2 · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T00:34:02.274730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9aa4035-01fd-4f0f-9f9e-716c5a79b786 · outbound

This paper cites Probabilistic Learning Combinations of Local Job-Shop Scheduling Rules.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Probabilistic Learning Combinations of Local Job-Shop Scheduling Rules

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:04.317688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5f4762e2-3ae0-4356-ba7e-f08c7e9189a9 · outbound

This paper cites an unresolved cited work.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:34:03.885847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4371b8d0-a70a-4498-b0e8-eed2e9d1eaed · outbound

This paper cites Benchmarks for Basic Scheduling Problems,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Benchmarks for Basic Scheduling Problems,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:03.651535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5e565957-2430-4dda-895f-10cd59aaa1ae · outbound

This paper cites Stable-Baselines3: Reliable Reinforcement Learning Implementations,.

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints Stable-Baselines3: Reliable Reinforcement Learning Implementations,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:34:03.367455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.