Summary of Enhancing Courier Scheduling in Crowdsourced Last-mile Delivery Through Dynamic Shift Extensions: a Deep Reinforcement Learning Approach, by Zead Saleh et al.
Enhancing Courier Scheduling in Crowdsourced Last-Mile Delivery through Dynamic Shift Extensions: A Deep Reinforcement Learning Approach
by Zead Saleh, Ahmad Al Hanbali, Ahmad Baubaid
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 In this study, researchers tackled the problem of dynamically adjusting schedules for committed couriers on crowdsourced delivery platforms. They developed a Deep Q-Network (DQN) approach to maximize platform profit by determining shift extensions and request assignments. By comparing their model with a baseline policy, they found that allowing shift extensions leads to higher rewards, reduced lost order costs, and fewer lost requests. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Crowdsourced delivery platforms need to schedule couriers and customer orders efficiently. Researchers looked at how committed and occasional couriers are compensated differently. They created an algorithm to adjust schedules dynamically by extending courier shifts based on demand. This helped the platform make more money, with less wasted time or missed deliveries. The study shows that this approach can be very effective. |
