Summary of Dynamic Demand Management For Parcel Lockers, by Daniela Sailer et al.
Dynamic Demand Management for Parcel Lockers
by Daniela Sailer, Robert Klein, Claudius Steinhardt
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed solution aims to dynamically control parcel locker availability to maximize served requests weighted by priority, while considering different compartment sizes. To tackle the curse of dimensionality, the authors develop a framework combining Sequential Decision Analytics and Reinforcement Learning techniques, including cost function approximation, parametric value function approximation, and truncated online rollout. Their approach outperforms myopic and industry-inspired benchmarks by 13.7% and 12.6%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary To help customers get their packages quickly and efficiently, a company wants to decide which lockers are available for each delivery request. This is like solving a big puzzle with many pieces that fit together in different ways. The authors of this paper come up with a new way to solve this problem by using computer algorithms that work together to make good decisions. Their approach does better than simple solutions and helps the company deliver more packages successfully. |
Keywords
* Artificial intelligence * Reinforcement learning
