Attention, Filling in The Gaps for Generalization in Routing Problems

by   Ahmad Bdeir, et al.

Machine Learning (ML) methods have become a useful tool for tackling vehicle routing problems, either in combination with popular heuristics or as standalone models. However, current methods suffer from poor generalization when tackling problems of different sizes or different distributions. As a result, ML in vehicle routing has witnessed an expansion phase with new methodologies being created for particular problem instances that become infeasible at larger problem sizes. This paper aims at encouraging the consolidation of the field through understanding and improving current existing models, namely the attention model by Kool et al. We identify two discrepancy categories for VRP generalization. The first is based on the differences that are inherent to the problems themselves, and the second relates to architectural weaknesses that limit the model's ability to generalize. Our contribution becomes threefold: We first target model discrepancies by adapting the Kool et al. method and its loss function for Sparse Dynamic Attention based on the alpha-entmax activation. We then target inherent differences through the use of a mixed instance training method that has been shown to outperform single instance training in certain scenarios. Finally, we introduce a framework for inference level data augmentation that improves performance by leveraging the model's lack of invariance to rotation and dilation changes.


Towards Omni-generalizable Neural Methods for Vehicle Routing Problems

Learning heuristics for vehicle routing problems (VRPs) has gained much ...

A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems

Recent researches show that machine learning has the potential to learn ...

Analytics and Machine Learning in Vehicle Routing Research

The Vehicle Routing Problem (VRP) is one of the most intensively studied...

Solving Routing Problems via Important Cuts

We introduce a novel approach of using important cuts which allowed us t...

Few-shots Parameter Tuning via Co-evolution

Generalization, i.e., the ability of addressing problem instances that a...

Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning

Boosting techniques and neural networks are particularly effective machi...

RP-DQN: An application of Q-Learning to Vehicle Routing Problems

In this paper we present a new approach to tackle complex routing proble...

Please sign up or login with your details

Forgot password? Click here to reset