Paper Citation Record · LEDGER

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes

As of 16 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2507.15307.

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

pith.paper-citation-record.v1
2507.15307 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:41:42.350033Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

31 of 31 outbound references displayed

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verified fuzzy13
unresolved18
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ffcc0d9e-1172-4394-82c8-216f1ee76781 · outbound

This paper cites Electric vehicles charging stations service area assessment using spatial analysis,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Electric vehicles charging stations service area assessment using spatial analysis,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.945217Z

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Observation bcc48273-1d1c-4ba7-9a78-69e634d1a49d · outbound

This paper cites Beyond the commute: Unlocking the potential of electric vehicles as future energy storage solutions (vision paper),.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Beyond the commute: Unlocking the potential of electric vehicles as future energy storage solutions (vision paper),

Reference 2

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Observation 033bf0a1-ddc0-4304-a7dd-4a64c6d2404b · outbound

This paper cites Ev scheduling framework for peak demand manage- ment in lv residential networks,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Ev scheduling framework for peak demand manage- ment in lv residential networks,

Reference 3

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Observation 2407c39f-5908-42a5-b76f-c8bc9787f18e · outbound

This paper cites A two-stage multi-objective stochastic optimization strategy to minimize cost for electric bus depot operators,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes A two-stage multi-objective stochastic optimization strategy to minimize cost for electric bus depot operators,

Reference 4

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Observation 7521da6d-6927-459a-8bd3-230add09d2a6 · outbound

This paper cites Trilevel mixed integer opti- mization for day-ahead spinning reserve management of electric vehicle aggregator with uncertainty,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Trilevel mixed integer opti- mization for day-ahead spinning reserve management of electric vehicle aggregator with uncertainty,

Reference 5

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Observation 05b6f0c6-ac65-4318-bab0-0c1daa19721d · outbound

This paper cites A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:42.232451Z

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Observation 98064344-4ae9-4162-92cc-77277aa88382 · outbound

This paper cites Unit commitment considering multiple charging and discharging scenarios of plug-in electric vehicles,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Unit commitment considering multiple charging and discharging scenarios of plug-in electric vehicles,

Reference 7

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Observation 7de3f653-54eb-449b-a7de-e7c3079391c8 · outbound

This paper cites Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems,

Reference 8

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Observation 515ce160-47b4-445e-907a-34b3970ec116 · outbound

This paper cites Collaborative ev routing and charging scheduling with power distribution and traffic networks interaction,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Collaborative ev routing and charging scheduling with power distribution and traffic networks interaction,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:42.246984Z

Source-reported events for the cited work

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Observation c16351cd-d606-4079-9fcd-b6e60e89e50e · outbound

This paper cites Joint routing and charging problem of multiple electric vehicles: A fast optimization algorithm,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Joint routing and charging problem of multiple electric vehicles: A fast optimization algorithm,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:42.251960Z

Source-reported events for the cited work

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Observation deb8af64-c869-4f1b-9029-246db57de0f6 · outbound

This paper cites Joint routing and scheduling for electric vehicles in smart grids with v2g,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Joint routing and scheduling for electric vehicles in smart grids with v2g,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:42.257682Z

Source-reported events for the cited work

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Observation 1e8f41dc-9222-4f49-80d2-7ace84a06593 · outbound

This paper cites Congestion-aware dynamic optimal traffic power flow in coupled transportation power systems,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Congestion-aware dynamic optimal traffic power flow in coupled transportation power systems,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:42.262356Z

Source-reported events for the cited work

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Observation 66c2b78d-9c7c-494a-a6ac-78a5528077ec · outbound

This paper cites Im- proving large scale day-ahead security constrained unit commitment performance,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Im- proving large scale day-ahead security constrained unit commitment performance,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.793733Z

Source-reported events for the cited work

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Observation 6dc49964-15b0-4f4a-ad5c-813cbcbae3ff · outbound

This paper cites Leveraging power grid topology in machine learning assisted optimal power flow,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Leveraging power grid topology in machine learning assisted optimal power flow,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.774487Z

Source-reported events for the cited work

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Observation 0d672309-de2a-4956-9650-08a958578b9f · outbound

This paper cites Supervised-learning-based hour- ahead demand response for a behavior-based home energy management system approximating milp optimization,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Supervised-learning-based hour- ahead demand response for a behavior-based home energy management system approximating milp optimization,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.753344Z

Source-reported events for the cited work

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Observation c5ed92a0-538e-434e-bfc0-b182b1816398 · outbound

This paper cites A supervised-learning-based strategy for optimal demand response of an hvac system in a multi-zone office building,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes A supervised-learning-based strategy for optimal demand response of an hvac system in a multi-zone office building,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.732783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 13daad4c-3d57-4854-b588-70ec15fe0ecd · outbound

This paper cites An online model for scheduling electric vehicle charging at park-and-ride facilities for flat- tening solar duck curves,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes An online model for scheduling electric vehicle charging at park-and-ride facilities for flat- tening solar duck curves,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.703086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e5f73662-e778-428f-bb77-07883e93efe0 · outbound

This paper cites Learning the optimal strategy of power system operation with varying renewable generations,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Learning the optimal strategy of power system operation with varying renewable generations,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.682829Z

Source-reported events for the cited work

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Observation 2c26a444-adbf-4216-8945-714a8dec0af4 · outbound

This paper cites Feasibility layer aided machine learning ap- proach for day-ahead operations,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Feasibility layer aided machine learning ap- proach for day-ahead operations,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.664746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c2bed4c6-4a78-4e0a-91b2-ea83861f58d6 · outbound

This paper cites Integrating learning and explicit model predictive control for unit commitment in microgrids,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Integrating learning and explicit model predictive control for unit commitment in microgrids,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.646141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 84c0d877-b43f-4973-867e-97e65196f05d · outbound

This paper cites Deep learning to optimize: Security-constrained unit commitment with uncertain wind power gen- eration and besss,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Deep learning to optimize: Security-constrained unit commitment with uncertain wind power gen- eration and besss,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.629448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8ed62717-b784-42c1-b171-52b7f832e04e · outbound

This paper cites An efficient method for computing traffic equilibria in networks with asymmetric transportation costs,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes An efficient method for computing traffic equilibria in networks with asymmetric transportation costs,

Reference 22

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Observation f002e258-90cb-494b-9ef3-e5511a4c4037 · outbound

This paper cites Gurobi Optimizer Reference Manual,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Gurobi Optimizer Reference Manual,

Reference 23

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Observation 0391d90f-5ec1-458f-9d1e-01ae3cdc6f07 · outbound

This paper cites Battery-based energy stor- age transportation for enhancing power system economics and security,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Battery-based energy stor- age transportation for enhancing power system economics and security,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:42.323071Z

Source-reported events for the cited work

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Observation eeeb41cd-a7f3-475c-a4b3-83b2f8bda1ee · outbound

This paper cites An interval power flow method based on linearized distflow equations for radial distribution systems,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes An interval power flow method based on linearized distflow equations for radial distribution systems,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:42.327567Z

Source-reported events for the cited work

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Observation 32fc1e3e-c5e7-4afc-b385-16c114dbc4a2 · outbound

This paper cites Time series classification from scratch with deep neural networks: A strong baseline,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Time series classification from scratch with deep neural networks: A strong baseline,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.542955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9e4f6af0-2c51-4ee8-bab0-c69c0a2595ed · outbound

This paper cites Asymmetric loss for multi-label classification,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Asymmetric loss for multi-label classification,

Reference 27

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Source-reported events for the cited work

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Observation a9db84fd-483a-49fa-b942-3a52ca3ef9cd · outbound

This paper cites Solcast API,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Solcast API,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.510440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 903ebc23-555f-4219-9a70-f8acfda77a2a · outbound

This paper cites Optuna: A next- generation hyperparameter optimization framework,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Optuna: A next- generation hyperparameter optimization framework,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:42.345646Z

Source-reported events for the cited work

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Observation 93d405da-8f8c-4859-b95a-e7b884f8c7bc · outbound

This paper cites Reinforce- ment learning for electric vehicle applications in power systems:a critical review,.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Reinforce- ment learning for electric vehicle applications in power systems:a critical review,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:41:42.479521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2716c2c9-9931-4c29-b2a4-53821f622762 · outbound

This paper cites Available: https://www.gurobi.com.

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes Available: https://www.gurobi.com

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T15:41:42.318580Z

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Pith citing papers

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