2412. Neural Deconstruction Search for Vehicle Routing Problems
Invited abstract in session WB-12: AI and Optimization for Warehousing, stream Artificial Intelligence, Machine Learning and Optimization.
Wednesday, 10:45-12:15Room: H10
Authors (first author is the speaker)
| 1. | André Hottung
|
| Decision and Operation Technologies, Bielefeld University | |
| 2. | Paula Wong-Chung
|
| University of British Columbia | |
| 3. | Kevin Tierney
|
| Business Decisions and Analytics, University of Vienna |
Abstract
Autoregressive construction approaches generate solutions to vehicle routing problems in a step-by-step fashion, leading to high-quality solutions that are nearing the performance achieved by handcrafted operations research techniques. We challenge the conventional paradigm of sequential solution construction and introduce an iterative search framework where solutions are instead deconstructed by a neural policy. Throughout the search, the neural policy collaborates with a simple greedy insertion algorithm to rebuild the deconstructed solutions. Our approach matches or surpasses the performance of state-of-the-art operations research methods across three challenging vehicle routing problems of various problem sizes.
Keywords
- Artificial Intelligence
- Combinatorial Optimization
- Routing
Status: accepted
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