In-Person Poster presentation / poster accept

Learning to CROSS exchange to solve min-max vehicle routing problems

Minjun Kim · Junyoung Park · Jinkyoo Park

MH1-2-3-4 #71

Keywords: [ General Machine Learning ]

[ Abstract ]
[ Slides [ OpenReview
Tue 2 May 2:30 a.m. PDT — 4:30 a.m. PDT

Abstract: CROSS exchange (CE), a meta-heuristic that solves various vehicle routing problems (VRPs), improves the solutions of VRPs by swapping the sub-tours of the vehicles. Inspired by CE, we propose Neuro CE (NCE), a fundamental operator of \textit{learned} meta-heuristic, to solve various min-max VRPs while overcoming the limitations of CE, i.e., the expensive $\mathcal{O}(n^4)$ search cost. NCE employs graph neural network to predict the cost-decrements (i.e., results of CE searches) and utilizes the predicted cost-decrements to guide the selection of sub-tours for swapping, while reducing the search cost to $\mathcal{O}(n^2)$. As the learning objective of NCE is to predict the cost-decrement, the training can be simply done in a supervised fashion, whose training samples can be easily collected. Despite the simplicity of NCE, numerical results show that the NCE trained with min-max flexible multi-depot VRP (min-max FMDVRP) outperforms the meta-heuristic baselines. More importantly, it significantly outperforms the neural baselines when solving distinctive special cases of min-max FMDVRP (e.g., min-max MDVRP, min-max mTSP, min-max CVRP) without additional training.

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