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ARXIV:2601.15038 · LOGISTICS OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.15038LOGISTICS OPTIMIZATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A curriculum-based DRL framework optimizing electric vehicle routing in logistics.
Opportunity summary
Pain A curriculum-based DRL framework optimizing electric vehicle routing in logistics.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A curriculum-based DRL framework optimizing electric vehicle routing in logistics. Although deep reinforcement learning (DRL) has shown great potential as an alternative to classical heuristics and exact solvers, existing DRL models often struggle to…
The electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel distance, fleet size, and battery usage while satisfying strict customer…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Trained exclusively on small instances with N=10 customers, the model demonstrates robust generalization to unseen instances ranging from N=5 to N=100, significantly outperforming standard…
Logistics Optimization moved forward this cycle; last verified April 2026. Public score 7.0/10.
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A curriculum-based DRL framework optimizing electric vehicle routing in logistics.
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Paper Pack
10.48550/arXiv.2601.15038A curriculum-based DRL framework optimizing electric vehicle routing in logistics.
Abstract
The electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel distance, fleet size, and battery usage while satisfying strict customer time constraints. Although deep reinforcement learning (DRL) has shown great potential as an alternative to classical heuristics and exact solvers, existing DRL models often struggle to maintain training stability-failing to converge or generalize when constraints are dense. In this study, we propose a curriculum-based deep reinforcement learning (CB-DRL) framework designed to resolve this instability. The framework utilizes a structured three-phase curriculum that gradually increases problem complexity: the agent first learns distance and fleet optimization (Phase A), then battery management (Phase B), and finally the full EVRPTW (Phase C). To ensure stable learning across phases, the framework employs a modified proximal policy optimization algorithm with phase-specific hyperparameters, value and advantage clipping, and adaptive learning-rate scheduling. The policy network is built upon a heterogeneous graph attention encoder enhanced by global-local attention and feature-wise linear modulation. This specialized architecture explicitly captures the distinct properties of depots, customers, and charging stations. Trained exclusively on small instances with N=10 customers, the model demonstrates robust generalization to unseen instances ranging from N=5 to N=100, significantly outperforming standard baselines on medium-scale problems. Experimental results confirm that this curriculum-guided approach achieves high feasibility rates and competitive solution quality on out-of-distribution instances where standard DRL baselines fail, effectively bridging the gap between neural speed and operational reliability.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 7.0
PROBLEM
A curriculum-based DRL framework optimizing electric vehicle routing in logistics. Although deep reinforcement learning (DRL) has shown great potential as an alternative to classical heuristics and exact solvers, existing DRL models often struggle to maintain training stability-...
METHOD
The electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel distance, fleet size, and battery usage while satisfying strict customer time constraints. Although deep...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Trained exclusively on small instances with N=10 customers, the model demonstrates robust generalization to unseen instances ranging from N=5 to N=100, significantly outperforming standard baselines on me...
WHY NOW
Logistics Optimization moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A curriculum-based DRL framework optimizing electric vehicle routing in logistics. Although deep reinforcement learning (DRL) has shown great potential as an alternative to classical heuristics and exact solvers, existing DRL models often struggle to maintain training stability-failing to converge or generalize when constraints are dense.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel distance, fleet size, and battery usage while satisfying strict customer time constraints. Although deep reinforcement learning (DRL) has shown great potential as an alternative to classical heuristics and exact solvers, existing DRL models often struggle to maintain training stability-failing to converge or generalize when constraints are dense.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Trained exclusively on small instances with N=10 customers, the model demonstrates robust generalization to unseen instances ranging from N=5 to N=100, significantly outperforming standard baselines on medium-scale problems.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Logistics Optimization moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A curriculum-based DRL framework optimizing electric vehicle routing in logistics.
Segment
Logistics Optimization
Adoption evidence
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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Evidence coverage
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stale
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Build readiness
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passport absent
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Artifact maturity
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stale
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Technical feasibility
partial
Current read
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Gaps
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Evidence
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ARTIFACTS
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DEFENSIBILITY
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