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Paper Citation Record · LEDGER

A Study of Data-driven Methods for Inventory Optimization

As of 18 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.08673.

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

pith.paper-citation-record.v1
2505.08673 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:53:15.377100Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

28 of 28 outbound references displayed

  • verified exact1
  • verified fuzzy19
  • unresolved8
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 48b52071-a7b4-412d-b30f-1c55b309f4a2 · outbound

This paper cites Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization.

A Study of Data-driven Methods for Inventory Optimization Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Reference 1

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unresolved
no resolver link, observed 2026-08-15T21:53:15.218638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e41c383f-4b0a-486f-9373-6b8d8818aad3 · outbound

This paper cites Deep reinforcement learning: A brief survey.

A Study of Data-driven Methods for Inventory Optimization Deep reinforcement learning: A brief survey

Reference 2

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unresolved
no resolver link, observed 2026-08-15T21:53:15.224639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1ace2afe-4fb0-498e-ab7d-9b746377263c · outbound

This paper cites Lost-sales inventory theory: A review.

A Study of Data-driven Methods for Inventory Optimization Lost-sales inventory theory: A review

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.820544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation cc23f108-3409-4d20-a704-bcd09ebfdb3e · outbound

This paper cites Optimal policy structure characterization for a two-period dual-sourcing inventory control model with forecast updating.

A Study of Data-driven Methods for Inventory Optimization Optimal policy structure characterization for a two-period dual-sourcing inventory control model with forecast updating

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.807470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.235110Z digest=sha256:bdb735222284cdac25ff3f34e816f6bbafd1d4e4ac3a3116eb3f186c5110663c

Observation 4775e42d-ae3f-4331-8545-7804d68a1880 · outbound

This paper cites Learning to order for inventory systems with lost sales and uncertain supplies.

A Study of Data-driven Methods for Inventory Optimization Learning to order for inventory systems with lost sales and uncertain supplies

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.796097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation db16d2fd-e9b8-411b-b98e-ecca4890fd09 · outbound

This paper cites Strategy, planning, and operation.

A Study of Data-driven Methods for Inventory Optimization Strategy, planning, and operation

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.782828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b9369826-e024-426b-82b7-a49a6017c2b0 · outbound

This paper cites Supermarket policies on less-healthy food at checkouts: natural experimental evaluation using interrupted time series analyses of purchases.

A Study of Data-driven Methods for Inventory Optimization Supermarket policies on less-healthy food at checkouts: natural experimental evaluation using interrupted time series analyses of purchases

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.764069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.255861Z digest=sha256:cec740de4905c4f9a5562028baa1bef9c6269d7d533977fb431d83acaa9e2363

Observation 04354b37-0a48-4114-9d41-6af0ba7cc597 · outbound

This paper cites Centralized planning models for multi-echelon inventory systems under uncertainty.

A Study of Data-driven Methods for Inventory Optimization Centralized planning models for multi-echelon inventory systems under uncertainty

Reference 8

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verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.749463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.272134Z digest=sha256:e6969ee3d992f7bfed93ae7e1ff87db17c7e6a1e1a6da8b885830ca7e004e7a4

Observation 0e4bbe96-a8f9-4444-811b-a7d0d97f24ee · outbound

This paper cites The new science of retailing: how analytics are transforming the supply chain and improving performance.

A Study of Data-driven Methods for Inventory Optimization The new science of retailing: how analytics are transforming the supply chain and improving performance

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.735646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.284166Z digest=sha256:2cd19682d5a55ca56a0df09c0ef35bc30c5087ca4394993af91509ca84ed0297

Observation 0ea542ea-d7a4-4d7d-ad10-1fea803056fa · outbound

This paper cites Analysis of a dual sourcing inventory model with normal unit demand and erlang mixture lead times.

A Study of Data-driven Methods for Inventory Optimization Analysis of a dual sourcing inventory model with normal unit demand and erlang mixture lead times

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.722705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 64a8a07a-1660-4abd-8b32-bb2a3041a627 · outbound

This paper cites Deep reinforcement learning in inventory management.

A Study of Data-driven Methods for Inventory Optimization Deep reinforcement learning in inventory management

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.709566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d7ae3fcb-5a27-40e2-81f6-cb0991515f51 · outbound

This paper cites Variable selection using random forests.

A Study of Data-driven Methods for Inventory Optimization Variable selection using random forests

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.697360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.302873Z digest=sha256:91f064814017b73db5e137f34aba95a8b3f0e272c29f48259d673f29eb08d607

Observation 0a0452f1-5c87-43c7-8a61-de89d6840692 · outbound

This paper cites Can deep reinforcement learning improve inventory management? performance on lost sales, dual-sourcing, and multi-echelon problems.

A Study of Data-driven Methods for Inventory Optimization Can deep reinforcement learning improve inventory management? performance on lost sales, dual-sourcing, and multi-echelon problems

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.685134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.307173Z digest=sha256:7435a9c07e8871eecacecd69d43307ecf0788cbf39686a3cee4da82d1735210a

Observation 7121f848-bb56-44d3-8add-903f281f5504 · outbound

This paper cites Forecasting the forecastability quotient for inventory management.

A Study of Data-driven Methods for Inventory Optimization Forecasting the forecastability quotient for inventory management

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.669477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.310951Z digest=sha256:8782beee4e954b48f948e9ad7e51117d77c6fc49e3d15669c45b7391a5f5c798

Observation 4086e3e4-968c-44b2-81f3-8cf5d433c81c · outbound

This paper cites The application of time-series forecasting models in grocery retail industry.

A Study of Data-driven Methods for Inventory Optimization The application of time-series forecasting models in grocery retail industry

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.648157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.314613Z digest=sha256:c594e16f048e43a27cfa3252a46e0cddeef90bd28869c7eb11102e5bea09599a

Observation 42763760-c797-46d4-8f4d-b65cc5bc7936 · outbound

This paper cites Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control.

A Study of Data-driven Methods for Inventory Optimization Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-08-15T21:53:15.450409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.319378Z digest=sha256:cc36f67736e0cf827b84b44ac0f9320c9326c069189015c2e328e8b1c9d2b9b7

Observation 8d56de98-f179-4bd7-9353-04ecada98d95 · outbound

This paper cites Deep Inventory Management.

A Study of Data-driven Methods for Inventory Optimization Deep Inventory Management

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-15T21:53:15.323524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e4aca32e-e873-46da-92ef-651f64fe9e65 · outbound

This paper cites Applied time series analysis: A practical guide to modeling and forecasting.

A Study of Data-driven Methods for Inventory Optimization Applied time series analysis: A practical guide to modeling and forecasting

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.625353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3a4989e8-536d-4f76-ad78-2aaf730b72ce · outbound

This paper cites Human-level control through deep reinforcement learning.

A Study of Data-driven Methods for Inventory Optimization Human-level control through deep reinforcement learning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-15T21:53:15.332017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:53:15.332017Z digest=sha256:b74f05c36d83827f6d2088067f473a871bba7055eaff5cdc3a485b0e6521bfbe

Observation cddddc12-1faa-4ddc-b074-50bb5ec56b14 · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

A Study of Data-driven Methods for Inventory Optimization Asynchronous methods for deep reinforcement learning

Reference 20

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:53:15.335996Z digest=sha256:48489aa1edea68519be52ccf06792626e68398e2af0580b643698b1c88ed9b30

Observation 256209f2-0039-4949-8057-f88235055b1a · outbound

This paper cites A data-driven approach to predict the success of bank telemarketing.

A Study of Data-driven Methods for Inventory Optimization A data-driven approach to predict the success of bank telemarketing

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.589591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6aa8ca5f-4b9a-4ff7-b1d2-c49cd6a3e985 · outbound

This paper cites A deep q-network for the beer game: Deep reinforcement learning for inventory optimization.

A Study of Data-driven Methods for Inventory Optimization A deep q-network for the beer game: Deep reinforcement learning for inventory optimization

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.569197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 67e0434e-d3d4-436a-9491-f4cb409aaf6e · outbound

This paper cites How many trees in a random forest? In Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012.

A Study of Data-driven Methods for Inventory Optimization How many trees in a random forest? In Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.554836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 057eebba-a4c3-459a-b558-f52302b23159 · outbound

This paper cites Approximate Dynamic Programming: Solving the curses of dimensionality, volume 703.

A Study of Data-driven Methods for Inventory Optimization Approximate Dynamic Programming: Solving the curses of dimensionality, volume 703

Reference 24

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unresolved
no resolver link, observed 2026-08-15T21:53:15.354893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:53:15.354893Z digest=sha256:59bfe680af9e2f8fd55cf928c05a6b53ac154c58651f1c0fea71c1a3bbf9c9b7

Observation eab84e2d-3374-4f2d-b9ec-0cf5563073a8 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search.

A Study of Data-driven Methods for Inventory Optimization Mastering the game of go with deep neural networks and tree search

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-15T21:53:15.360568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:53:15.360568Z digest=sha256:69f1b040bc1d0ce00b2970134c0501f8de1c434fe883e1bafe0819c9fe4b655a

Observation df72badf-7161-42c3-86e9-a346fca8e0dd · outbound

This paper cites Improving organizational decision-making using random forests and big data analytics.

A Study of Data-driven Methods for Inventory Optimization Improving organizational decision-making using random forests and big data analytics

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.517799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.364751Z digest=sha256:f9207474549f785440aada132a12cd25380deb63c9a349690d23c355aab597de

Observation ee0aa724-adcc-493b-a9ae-f7c26cb1d1d0 · outbound

This paper cites Missforest—non-parametric missing value imputation for mixed-type data.

A Study of Data-driven Methods for Inventory Optimization Missforest—non-parametric missing value imputation for mixed-type data

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-15T21:53:15.372789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:53:15.372789Z digest=sha256:ecec0b5b6b02908a51a5c4b87478ad2c6c0a11c568881121054e9a13800ca1f6

Observation 7c0a2926-8cff-4a04-bedc-f04fec44508e · outbound

This paper cites Mining data with random forests: A survey and results of new tests.

A Study of Data-driven Methods for Inventory Optimization Mining data with random forests: A survey and results of new tests

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:53:15.488639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-15T21:53:15.377100Z digest=sha256:efdd36b03f376bf8567972958984f8d02c811d5b8b3d96e4e15fc214d6b0d73b

Pith citing papers

No inbound Pith citation observations are available.