Paper Citation Record · LEDGER

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers

As of 22 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2507.15385.

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

pith.paper-citation-record.v1
2507.15385 v1

Coverage vector

measured 31 of 31 reference resolution

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measured 31 of 31 standing notices

One-hop event checks from named stored sources.

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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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External citation measurements

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Outbound references

Observation 6ce35256-5066-4cbf-b4be-93f570de2e19 · outbound

This paper cites Routing and scheduling of electric buses for resilient restoration of distribution system,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Routing and scheduling of electric buses for resilient restoration of distribution system,

Reference 1

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Observation afb45999-fc44-4146-904e-a32c821bb330 · outbound

This paper cites Global ev outlook 2021,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Global ev outlook 2021,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:22.210508Z

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Observation d9117b27-8b30-4727-b31e-e25ff3cd3b3a · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Beyond the commute: Unlocking the potential of electric vehicles as future energy storage solutions (vision paper),

Reference 3

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Observation 94b066e1-7a7e-49a6-9a20-0a86e4015252 · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Unit commitment considering multiple charging and discharging scenarios of plug-in electric vehicles,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:22.072364Z

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Observation 8d31b728-a88f-477e-bdc3-3fb6bda45da2 · 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,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles,

Reference 5

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Observation a5301747-9b43-49f4-8376-3b6b7bfd8f5c · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Ev scheduling framework for peak demand manage- ment in lv residential networks,

Reference 6

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Observation 5450fc0c-f5b7-4847-834f-ffcfadfbba53 · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers A two-stage multi-objective stochastic optimization strategy to minimize cost for electric bus depot operators,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:21.528935Z

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Observation efdabc2e-5bee-444a-9fd0-2dc1df837071 · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Trilevel mixed integer opti- mization for day-ahead spinning reserve management of electric vehicle aggregator with uncertainty,

Reference 8

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Observation 8d1a7e33-94ab-41f3-b9e9-6734febaac28 · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Joint routing and scheduling for electric vehicles in smart grids with v2g,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:21.159301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 755acb77-81c3-48ad-87db-dd99c0a2cf14 · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Joint routing and charging problem of multiple electric vehicles: A fast optimization algorithm,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:20.953824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c027fae6-410b-4660-9916-4a20a9ffc15d · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Congestion-aware dynamic optimal traffic power flow in coupled transportation power systems,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:20.787097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 55669983-8aa5-4196-9ac0-841a077604dc · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Collaborative ev routing and charging scheduling with power distribution and traffic networks interaction,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:20.614862Z

Source-reported events for the cited work

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Observation 552e932f-4a68-452f-8ea5-945755ebe185 · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:20.403020Z

Source-reported events for the cited work

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Observation 8bc48297-8e78-437e-b113-99e126f03cb2 · outbound

This paper cites Equilibrium analysis of electricity markets with day-ahead market power mitigation and real-time intercept bidding,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Equilibrium analysis of electricity markets with day-ahead market power mitigation and real-time intercept bidding,

Reference 14

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Observation f1ce4fc1-10fd-45c4-9049-980ecce21470 · outbound

This paper cites Gurobi Optimizer Reference Manual,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Gurobi Optimizer Reference Manual,

Reference 15

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Observation d37900c2-ee92-4e30-b8f2-bdfdf45b4a12 · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers An efficient method for computing traffic equilibria in networks with asymmetric transportation costs,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:19.939598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ad3ee73c-cb88-45eb-b41d-fb7f4cbced75 · outbound

This paper cites Q-learning-based model predictive control for energy management in residential aggregator,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Q-learning-based model predictive control for energy management in residential aggregator,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:19.771302Z

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Observation de3077a8-f523-4834-b9e3-c7376d296dbe · outbound

This paper cites Siphyr: An end-to-end learning-based optimization framework for dynamic grid reconfiguration,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Siphyr: An end-to-end learning-based optimization framework for dynamic grid reconfiguration,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:19.516517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 403c64dc-bd01-472d-b86f-71cff3b077c8 · outbound

This paper cites Optimal control of microgrids with multi-stage mixed-integer nonlinear programming guided q-learning algorithm,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Optimal control of microgrids with multi-stage mixed-integer nonlinear programming guided q-learning algorithm,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:19.292249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a9abe5d4-2cfe-429b-800e-1a25c13c5cc1 · outbound

This paper cites A hybrid approach for home energy management with imitation learning and online optimization,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers A hybrid approach for home energy management with imitation learning and online optimization,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:19.108924Z

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Observation b6f48be9-ebbb-433e-b0cf-0ef4d6a68279 · outbound

This paper cites Combining deep learning and optimization for preventive security-constrained dc optimal power flow,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Combining deep learning and optimization for preventive security-constrained dc optimal power flow,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.924899Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation df282974-fb67-4c8d-bdf9-188758ae4989 · outbound

This paper cites Machine learning-additional decision constraints for improved milp day-ahead unit commitment method,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Machine learning-additional decision constraints for improved milp day-ahead unit commitment method,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.770125Z

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Observation c77a7968-6e67-4fda-aff6-51340459fb26 · outbound

This paper cites Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.578777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 68a42d14-fe38-4b60-b27c-fe3b01dbee08 · outbound

This paper cites Learning-assisted variables reduc- tion method for large-scale milp unit commitment,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Learning-assisted variables reduc- tion method for large-scale milp unit commitment,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.440740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 56e4ef53-f5a1-42aa-a643-b139dfdd61a9 · outbound

This paper cites Data- augmentation acceleration framework by graph neural network for near- optimal unit commitment,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Data- augmentation acceleration framework by graph neural network for near- optimal unit commitment,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.270696Z

Source-reported events for the cited work

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Observation 85af733d-0f8e-4cd9-8d4c-5bec096002b1 · outbound

This paper cites Joint optimisation of electric vehicle routing and scheduling: A deep learning-driven approach for dynamic fleet sizes,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Joint optimisation of electric vehicle routing and scheduling: A deep learning-driven approach for dynamic fleet sizes,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.155060Z

Source-reported events for the cited work

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Observation e0850970-194a-42b9-b0bb-c56d92b759c2 · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers An interval power flow method based on linearized distflow equations for radial distribution systems,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:17.971355Z

Source-reported events for the cited work

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Observation e33afe1d-fc13-4b88-9ac5-3c54919d3eef · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Battery-based energy stor- age transportation for enhancing power system economics and security,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:17.785532Z

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Observation 9bf6c259-2229-455a-af8e-627ca5a12835 · outbound

This paper cites Attention is all you need,.

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Attention is all you need,

Reference 29

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Asymmetric loss for multi-label classification,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:17.565321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8d694712-a228-4f63-84e5-00dc46287af2 · outbound

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

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers Available: https://www.gurobi.com

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:16.100287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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