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Evidence record 18695 · automatically gathered

Partially Observable Learning for Multi-Platform Dispatch Optimization

Instant delivery platforms have become a critical component of urban logistics, increasingly relying on crowdsourced couriers to fulfill highly dynamic orders. In real-world systems, couriers are not exclusive to a single platform and may concurrently serve multiple platforms, while each platform can only observe its own orders and couriers' interactions due to privacy and operational constraints. This results in a multi-platform dispatch environment with inherent partial observability. However,

Record details

Published: 11 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Privacy · Environment
Retrieved: 12 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (11 August 2026), “Partially Observable Learning for Multi-Platform Dispatch Optimization,” evidence record 18695, https://ethics.ai/record/18695 (originally published by arXiv cs.LG).

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