Summary of Contextual Stochastic Vehicle Routing with Time Windows, by Breno Serrano et al.
Contextual Stochastic Vehicle Routing with Time Windows
by Breno Serrano, Alexandre M. Florio, Stefan Minner, Maximilian Schiffer, Thibaut Vidal
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 This research paper introduces the contextual stochastic vehicle routing problem with time windows (VRPTW), which involves making decisions based on observed features before determining optimal routes. The study focuses on minimizing total transportation cost and expected late arrival penalties, given uncertain travel times and feature variables. To tackle this challenge, the authors propose novel data-driven prescriptive models using historical data, including point-based, sample average, and penalty-based approximations. These methods are designed to handle stochastic travel times and features. The paper also develops specialized branch-price-and-cut algorithms to solve these models efficiently. Comparative experiments on instances with up to one hundred customers demonstrate that a feature-dependent sample average approximation outperforms existing and novel methods in most settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about creating the best routes for vehicles that need to visit specific locations within certain time frames, even when there’s uncertainty involved. The researchers came up with new ways to solve this problem by using historical data and different approaches. They tested their methods on various scenarios and found that one approach, which takes into account both travel times and other important factors, performed better than others in most cases. |
