Summary of Spatial-temporal-demand Clustering For Solving Large-scale Vehicle Routing Problems with Time Windows, by Christoph Kerscher and Stefan Minner
Spatial-temporal-demand clustering for solving large-scale vehicle routing problems with time windows
by Christoph Kerscher, Stefan Minner
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); 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 The proposed decompose-route-improve (DRI) framework uses clustering to group customers based on spatial, temporal, and demand data. This decomposition phase is formulated to reflect the vehicle routing problem’s objective function and constraints, resulting in sub-routing problems that can be solved independently using any suitable algorithm. Pruning is applied between solved subproblems using a pruned local search (LS) approach that incorporates customers’ similarity information obtained during decomposition. In a computational study, the DRI outperforms classic cluster-first, route-second approaches solely based on spatial information and achieves high-quality solutions faster for large-scale vehicle routing problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to solve big logistics problems. It uses machine learning to group customers together based on their locations, times, and demands. This helps reduce the complexity of the problem, making it easier to find good solutions quickly. The approach is tested against other methods and shown to be better at solving large-scale vehicle routing problems. |
Keywords
* Artificial intelligence * Clustering * Machine learning * Objective function * Pruning
