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Paper Citation Record · LEDGER

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems

As of 8 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2506.02255.

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

pith.paper-citation-record.v1
2506.02255 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:32:04.280424Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T18:22:36.891186Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:09:30.118345Z

Reference resolution

48 of 48 outbound references displayed

  • verified exact6
  • verified fuzzy18
  • unresolved23
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 61b4bb19-aa9d-40ad-9e7b-29e8d07e1ae0 · outbound

This paper cites Constrained policy optimization.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Constrained policy optimization

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:00.771822Z digest=sha256:b094b9c1b3e1f014a9baa75a90508af09b1832d3541c05f386cd456e9de65b08

Observation 963558d4-3461-40c8-8890-0340c2d8b3f9 · outbound

This paper cites Routledge, 2021.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Routledge, 2021

Reference 2

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source=pdf_text observed=2026-08-07T11:32:00.846267Z digest=sha256:d742eae127b483c71baddbdf62fe35479bebb54a1ce6f4bd43083bb8cf97c77f

Observation 82110d89-acf1-4b2c-af1e-78bf1524618b · outbound

This paper cites PC-Gym: Benchmark Environments For Process Control Problems.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems PC-Gym: Benchmark Environments For Process Control Problems

Reference 3

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source=pdf_text observed=2026-08-07T11:32:00.933042Z digest=sha256:c4ccad0f9dd15648b236a91bddf51a3633d52220baa5c42d1c1d390c94479ea0

Observation c823ab86-d027-4433-a3cf-573586a4c857 · outbound

This paper cites OpenAI Gym.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems OpenAI Gym

Reference 4

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source=pdf_text observed=2026-08-07T11:32:01.071746Z digest=sha256:3d4a99a1165db122a9608d4ec70ed88dfb86e7d2a8f2415ae93379c692cb6d04

Observation d19c23c3-1c0d-4e9b-8d34-ec6f43937918 · outbound

This paper cites Constante Flores, and Can Li.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Constante Flores, and Can Li

Reference 5

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source=pdf_text observed=2026-08-07T11:32:01.098289Z digest=sha256:e79c085e2771f4eebaa92807d64709bda6e536af79f3de80d5855e44f9164446

Observation ee78dae1-61cb-49f1-bbb7-9ea60b2bdecb · outbound

This paper cites Maravelias.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Maravelias

Reference 6

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verified exact
doi, observed 2026-08-07T11:32:04.764384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:01.173648Z digest=sha256:fa63cb3f0d022c82fbb5439529562825e6acb3d6767a923201a85b5356c63b66

Observation 680d83cb-0584-4604-a0a6-662a7859c9c2 · outbound

This paper cites A comprehensive survey on safe reinforcement learning.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems A comprehensive survey on safe reinforcement learning

Reference 7

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source=pdf_text observed=2026-08-07T11:32:01.262174Z digest=sha256:4087e0ac9c3416849cd94407e1a1b09118a1df06d36531665126d624ef24c0f6

Observation 0d6cb76d-128d-48dc-990e-0fad61852665 · outbound

This paper cites A Review of Safe Reinforcement Learning: Methods, Theory and Applications.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems A Review of Safe Reinforcement Learning: Methods, Theory and Applications

Reference 8

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source=pdf_text observed=2026-08-07T11:32:01.311859Z digest=sha256:0089390009d07d2e3fdb59b59a27daa7f7f9e09143a601cc662ff07f50ef1cfb

Observation 024bc4db-08ee-4953-a254-d6e1a5179c1e · outbound

This paper cites Gurobi Optimizer Reference Manual, 2025.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Gurobi Optimizer Reference Manual, 2025

Reference 9

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source=pdf_text observed=2026-08-07T11:32:01.369592Z digest=sha256:29a32e04cee3bc942af31b476450775d1a61677d6050288fa0ca5e1004a26565

Observation 20ba0d41-55b6-4493-b7e8-10874879b2f8 · outbound

This paper cites an unresolved cited work.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Unresolved cited work

Reference 10

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doi, observed 2026-08-07T11:32:04.653160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:01.440410Z digest=sha256:275862dcf29c2771e4b8a3786e8e57440b3fe3a5a802cec5da78f879931b24b9

Observation 31a9d86d-a3df-45f2-b106-3fa6dc851621 · outbound

This paper cites OR-Gym: A Reinforcement Learning Library for Operations Research Problems.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems OR-Gym: A Reinforcement Learning Library for Operations Research Problems

Reference 11

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source=pdf_text observed=2026-08-07T11:32:01.514345Z digest=sha256:c0a9cb8dc94898897b5f9f48f23d4ec1edf18dfd671cc5abd5c74989ca5ab848

Observation 1e561115-3525-4ff6-88bf-b71c567e5bdc · outbound

This paper cites Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs

Reference 12

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source=pdf_text observed=2026-08-07T11:32:01.612049Z digest=sha256:1d94c9214dce9b5c6ed9f9f68ab79990ab7cf56180a3b41acca26eab62ba41f9

Observation 4e1578fa-907d-4623-97db-674db1d53f0a · outbound

This paper cites Safety gymnasium: A unified safe reinforcement learning benchmark.Advances in Neural Information Processing Systems, 36:18964–18993, 2023.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Safety gymnasium: A unified safe reinforcement learning benchmark.Advances in Neural Information Processing Systems, 36:18964–18993, 2023

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:32:01.701692Z digest=sha256:0a297a3bc0c4fd5b3efa03a51a52ecb5be39987a045e9f055ead0eaecee35566

Observation ec2fdcb9-ea61-4159-965a-9e15b5878c45 · outbound

This paper cites Omnisafe: An infrastructure for accelerating safe reinforcement learning research.Journal of Machine Learning Research, 25(285):1–6, 2024.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Omnisafe: An infrastructure for accelerating safe reinforcement learning research.Journal of Machine Learning Research, 25(285):1–6, 2024

Reference 14

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source=pdf_text observed=2026-08-07T11:32:01.782684Z digest=sha256:e32d3d551b507f8baea8303c0fbd5c98426649d6437f84380b0e14ff0b5b60f9

Observation b35bab0a-a797-430a-ab8b-3df32a2e304d · outbound

This paper cites On mixed-integer programming formulations for the unit commitment problem.INFORMS Journal on Computing, 32(4): 857–876, 2020.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems On mixed-integer programming formulations for the unit commitment problem.INFORMS Journal on Computing, 32(4): 857–876, 2020

Reference 15

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source=pdf_text observed=2026-08-07T11:32:01.849797Z digest=sha256:9746f495976a5bb0684c94b106a6df5b50dad70675da943abd0939612dd47f36

Observation e585d979-abf4-48c6-910c-94d51daf0329 · outbound

This paper cites an unresolved cited work.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Unresolved cited work

Reference 16

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:01.970272Z digest=sha256:8a563d87060824a67f7dd24534965d12c8aca1ea1a6061ebd77906c33b6f653c

Observation bbac67ff-0281-41d0-b213-0ded30ee4408 · outbound

This paper cites Lillicrap, Jonathan J.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Lillicrap, Jonathan J

Reference 17

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:02.060041Z digest=sha256:348fd52abfa545674df1075527d1c4fe2c5ee5506993bf26730eef5f6dc150cd

Observation 23147806-69be-4b36-b7f8-da24b011e394 · outbound

This paper cites Unified frameworks for optimal process planning and scheduling.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Unified frameworks for optimal process planning and scheduling

Reference 18

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raw_fallback, observed 2026-08-07T11:32:10.072484Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:02.187237Z digest=sha256:f9afe26d24a3f928abaae44d408be9d0a98756c7908d4dd907e2f4aaac73220f

Observation 0c3ecbaf-e821-40ce-a2bb-a98e951c56aa · outbound

This paper cites Reinforcement learning for process control: Review and benchmark problems.International Journal of Control, Automation and Systems, 23(1):1–40, 2025.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Reinforcement learning for process control: Review and benchmark problems.International Journal of Control, Automation and Systems, 23(1):1–40, 2025

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:02.272017Z digest=sha256:25628eff2ef2475c86cbea4d9cb5a0a6000c0916227c9a8dbc3bae30643b1e91

Observation 35520a46-a229-483c-b386-37c74047544b · outbound

This paper cites Algorithmic approaches to inventory management optimization.Processes, 9(1):102, 2021.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Algorithmic approaches to inventory management optimization.Processes, 9(1):102, 2021

Reference 20

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:02.333051Z digest=sha256:62ebf31aa775e4f05e9681ac2f4bffe60d444243a553cd2c9d6b28d5d7197172

Observation bcebe945-e01f-43c5-bcfe-400da2d02b52 · outbound

This paper cites Re- inforcement learning for efficient power systems planning: A review of operational and ex- pansion strategies.Energies, 17(9), 2024.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Re- inforcement learning for efficient power systems planning: A review of operational and ex- pansion strategies.Energies, 17(9), 2024

Reference 21

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doi, observed 2026-08-07T11:32:04.541133Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:02.435302Z digest=sha256:c7f1612d48da10dfb19a813de934a52087b361dc4ce5c1fa065d24987b8197a7

Observation b67f19cf-3384-4bbc-a372-6bd119fec031 · outbound

This paper cites Long Duration Battery Sizing, Siting, and Operation Under Wildfire Risk Using Progressive Hedging.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Long Duration Battery Sizing, Siting, and Operation Under Wildfire Risk Using Progressive Hedging

Reference 22

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local_arxiv, observed 2026-08-07T11:32:05.151515Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:02.514632Z digest=sha256:ec14d8d84a9139854b4bce62c146039bab59fd9425213f715821178aa2e1a858

Observation d4e0ec13-29eb-46fa-885a-dd9bc7e0cb1c · outbound

This paper cites A Tutorial on Multi-time Scale Optimization Models and Algorithms.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems A Tutorial on Multi-time Scale Optimization Models and Algorithms

Reference 23

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local_arxiv, observed 2026-08-07T11:32:04.951229Z

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source=pdf_text observed=2026-08-07T11:32:02.599613Z digest=sha256:4ce18dc740cdc3f4629c3cd4066cae18b651f3d1d3cef141ee18ec42cf791bb5

Observation 9c6730b4-5401-417d-a511-01f3d0bcbf6a · outbound

This paper cites Prentice Hall Upper Saddle River, NJ, 1998.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Prentice Hall Upper Saddle River, NJ, 1998

Reference 24

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:02.686982Z digest=sha256:8edbc38f1fbd57746ca2203de1556fe1b558136d9d2ef9847509d91b0b6bf9f6

Observation e78abb14-1057-4130-8668-4f583eacd316 · outbound

This paper cites Benchmarking Safe Exploration in Deep Reinforcement Learning.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Benchmarking Safe Exploration in Deep Reinforcement Learning

Reference 25

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raw_fallback, observed 2026-08-07T11:32:09.260888Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:02.757810Z digest=sha256:21965006c50e54d74a42cd26842875c644045bd3dff60351f3ec9a3616ba3fcd

Observation 45547f6f-583e-49d8-8256-14b22f7b90fe · outbound

This paper cites Trust region policy optimization.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Trust region policy optimization

Reference 26

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raw_fallback, observed 2026-08-07T11:32:09.067408Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:02.827275Z digest=sha256:344410b952770aa3503d706d31aff4f2355ac8b0b8cbcf14ca650a485dbc43c1

Observation b039c111-175d-4152-8354-d51e969a8161 · outbound

This paper cites MuJoCo: A physics engine for model-based control.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems MuJoCo: A physics engine for model-based control

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:32:02.936228Z digest=sha256:636401351945efe0242ec8fd3f374551d46c6c35a8a5369baaab9bcda2646d97

Observation 508b018d-30e7-40bb-9518-79a7f0f4c175 · outbound

This paper cites Xenos, Georgios M.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Xenos, Georgios M

Reference 28

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doi, observed 2026-08-07T11:32:04.419329Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.009205Z digest=sha256:80b79d9ed4ed34ec569d975961e4c90bae7151d5175409dc580c89d4119891a9

Observation 0be6881f-ab1c-40c5-8c0c-49ea42d7d187 · outbound

This paper cites Crpo: A new approach for safe reinforcement learning with convergence guarantee.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Crpo: A new approach for safe reinforcement learning with convergence guarantee

Reference 29

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raw_fallback, observed 2026-08-07T11:32:08.857888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.095482Z digest=sha256:2b92b9bd3a6de0f662a14cdedb0b7ae042979d8e8f76714aefbc91be9b03f7b8

Observation 419a0363-ada2-44ae-b4be-f89978500c28 · outbound

This paper cites Sustaingym: Reinforcement learning environments for sustainable energy systems.Advances in Neural Information Processing Systems, 36:59464–59476, 2023.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Sustaingym: Reinforcement learning environments for sustainable energy systems.Advances in Neural Information Processing Systems, 36:59464–59476, 2023

Reference 30

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.175365Z digest=sha256:5e6f1a4ee637966eb7b7896a699930625766ba046bba6e2e945ed8e087ec7aaa

Observation 6bc3645a-7918-4151-a5ee-b4b84385df62 · outbound

This paper cites Penalized proximal policy optimization for safe reinforcement learning.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Penalized proximal policy optimization for safe reinforcement learning

Reference 31

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raw_fallback, observed 2026-08-07T11:32:08.552714Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.230262Z digest=sha256:caf27e6d1102dc8e25f8a72c12d8cd8b6e5ed4a541f8b718921e055cec9b00e2

Observation 1012ca51-be2a-4e4b-8f96-61003b4a8176 · outbound

This paper cites Grossmann, Clara F.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Grossmann, Clara F

Reference 32

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:32:03.274856Z digest=sha256:ec2ad7571f3a59274e9b802970bc61a4581b8d95c70fdaf8728c6cacadaeabed

Observation 8740be19-5a77-4434-be49-9c9ffdbc7b34 · outbound

This paper cites an unresolved cited work.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Unresolved cited work

Reference 35

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.377485Z digest=sha256:b67a4e5cc2d8d655b68eb488b439302a1d44b301da00ff2459b2c2f7123bf22e

Observation e18a99ec-4f9b-4b20-8af8-d3c86fef6598 · outbound

This paper cites Xr,t+1 =X r,t +p t,r,0 pt =p t−1,r,1:τmax ⊕ X i max{νi,r,0} ·afinal i,t (1).

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Xr,t+1 =X r,t +p t,r,0 pt =p t−1,r,1:τmax ⊕ X i max{νi,r,0} ·afinal i,t (1)

Reference 36

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raw_fallback, observed 2026-08-07T11:32:08.175147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.475255Z digest=sha256:1084c23ee70da5d09c015dabaa9e03abbad47f7ea4bf434cd231a94b12bf5535

Observation 74867053-cea8-485b-8367-2d8f63739cc8 · outbound

This paper cites A part of the cost is calculated based on this.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems A part of the cost is calculated based on this

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:32:08.022092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.579547Z digest=sha256:613f6f3ea88d53d1e44e8767aebf42ddad656972b586f3d89009f4eafc99d665

Observation 8ac320ab-4e1f-409f-a827-fa1f2d6c8b12 · outbound

This paper cites an unresolved cited work.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:32:07.861052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.676758Z digest=sha256:f19c4f3bdf8bca26ce84babd693fc016f5543184aa4b75c8890b505a56c0b34d

Observation 29c3ff90-a4fb-4af6-9131-99cab3040995 · outbound

This paper cites Xs,t+1 =X s,t +p t,s,0 pt+1,s =p t,s,1:τmax ⊕ X e X i max(νi,s,0)·a final i,e,t (2).

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Xs,t+1 =X s,t +p t,s,0 pt+1,s =p t,s,1:τmax ⊕ X e X i max(νi,s,0)·a final i,e,t (2)

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:32:07.655416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.723543Z digest=sha256:dc7a5c02aa903b72307013d4457929469b3acfbe6ae6a3a61b141e19967264e0

Observation 993405ec-c93c-4a6d-bf64-a2da5dec5d97 · outbound

This paper cites Violations of these bounds contribute to the constraint cost.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Violations of these bounds contribute to the constraint cost

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:32:07.473629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.810716Z digest=sha256:89f492535922486ed28b44ec01a5639a03792e0b4d1d1bf1516112047ced9335

Observation e33421c7-3ebc-47b7-9b74-f555aa4649b2 · outbound

This paper cites 33 Sales and Backlog Update.Sales are Sr,m,t = min Dr,m,t +B r,m,t−1, Ir,t , then Ir,t ←I r,t −S r,m,t, B r,m,t =D r,m,t +B r,m,t−1 −S r,m,t.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems 33 Sales and Backlog Update.Sales are Sr,m,t = min Dr,m,t +B r,m,t−1, Ir,t , then Ir,t ←I r,t −S r,m,t, B r,m,t =D r,m,t +B r,m,t−1 −S r,m,t

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:32:07.243614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.876922Z digest=sha256:f1fa041e3c270c5a1e7258947187b31c92df3a005cd199ee4ef4b38ff13ba8aa

Observation 23e14cd6-1a62-4585-9abf-5b071a58c012 · outbound

This paper cites Let the resulting action vector be at = pg,t g∈G ∥ cn,t n∈N ∥ pd n,t n∈N ∥ ℓn,t n∈N ∥ θn,t n∈N \{1}, withθ 1,t ≡0.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Let the resulting action vector be at = pg,t g∈G ∥ cn,t n∈N ∥ pd n,t n∈N ∥ ℓn,t n∈N ∥ θn,t n∈N \{1}, withθ 1,t ≡0

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:32:06.997162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.906905Z digest=sha256:24aad83adc772bbd693361ffffaef2fa91d8154186fa19adc4ea490eaec641c7

Observation 6008c2d5-8dbe-465f-8d6b-103554c18471 · outbound

This paper cites an unresolved cited work.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Unresolved cited work

Reference 43

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:32:06.670757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:03.949187Z digest=sha256:794d4d11168749170a512eced6d0ea07270b09d31dde42fb896d5e312f1f81f8

Observation 431340fe-5d2a-44c8-8f7a-2f19f3598bea · outbound

This paper cites an unresolved cited work.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:32:06.437399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:04.025750Z digest=sha256:9866e3a2446f07d505bdc2d7853d986f598628a4730e80bbe4b4213f5b693048

Observation 5fe802bd-14c4-4f32-a3f6-cb8466165ef4 · outbound

This paper cites an unresolved cited work.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Unresolved cited work

Reference 45

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:32:06.218668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:04.075692Z digest=sha256:c21176c45b18563e1fbe3c18e947295e615ffa704e2b3566ecef74646fa83460

Observation bcad44a6-12a6-4714-85be-14f6c2e3603e · outbound

This paper cites an unresolved cited work.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Unresolved cited work

Reference 46

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:32:05.934491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:04.146138Z digest=sha256:16ee81107d2bf4f7f036b1d1750f25bac4ad4f9b149c75d5e2d3fd1f206a434c

Observation c1a6870d-ba19-4f04-9529-4bafb4747b26 · outbound

This paper cites The nodal power-balance residual is: ∆n,t =P n,t − X j∈N Bnj(θn,t −θ j,t), and the network-balance penalty is: C bal t =ϕ bal X n∈N |∆n,t|.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems The nodal power-balance residual is: ∆n,t =P n,t − X j∈N Bnj(θn,t −θ j,t), and the network-balance penalty is: C bal t =ϕ bal X n∈N |∆n,t|

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:32:05.667389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:04.215700Z digest=sha256:05e3893c8f1c84e6955c559523b663e576f09be60c3d965c67fd1a0114d90125

Observation 85772ac9-40e5-4cf9-ad58-e4810ee2e5fb · outbound

This paper cites Work” (active production) and “Off.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Work” (active production) and “Off

Reference 48

Resolution
malformed identifier
raw_fallback, observed 2026-08-07T11:32:05.423083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:32:04.280424Z digest=sha256:fa860342a20b3ac6dd293d3f18e0d9264aa07877b72c1f5548e5ceae393037e6

Observation 1e789084-1a41-45d8-85ca-099a04c35cb8 · outbound

This paper cites Continuous control with deep reinforcement learning.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems Continuous control with deep reinforcement learning

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-07T11:32:02.130807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:32:02.130807Z digest=sha256:58ab85102360850c9d5e50f5882f0ab0ef9737e86ff62a8d1d2260e0d947ad8c

Observation 46015323-1c64-40a8-8f4c-587787a22b41 · outbound

This paper cites PC-Gym: Benchmark Environments For Process Control Problems.

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems PC-Gym: Benchmark Environments For Process Control Problems

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T11:32:01.018749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:32:01.018749Z digest=sha256:db04da2a257dc7a46753be1f03894167a1429935024675a76ababe357b9f1ce5

Pith citing papers

Observation 4e50de92-f92c-4dfa-82c0-8feea0a431a5 · inbound

CRAX: Fast Safe Reinforcement Learning Benchmarking cites this paper.

CRAX: Fast Safe Reinforcement Learning Benchmarking SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-07-17T00:20:38.825561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T18:22:36.891186Z digest=sha256:e14036806ee6666ba6f645b28b05f6509c54f7ab39e2fea0813c8398d2dc4811