Complex Vehicle Routing with Memory Augmented Neural Networks

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Abstract

Complex real-life routing challenges can be modeled as variations of well-known combinatorial optimization problems. These routing problems have long been studied and are difficult to solve at scale. The particular setting may also make exact formulation difficult. Deep Learning offers an increasingly attractive alternative to traditional solutions, which mainly revolve around the use of various heuristics. Deep Learning may provide solutions which are less time-consuming and of higher quality at large scales, as it generally does not need to generate solutions in an iterative manner, and Deep Learning models have shown a surprising capacity for solving complex tasks in recent years. Here we consider a particular variation of the Capacitated Vehicle Routing (CVRP) problem and investigate the use of Deep Learning models with explicit memory components. Such memory components may help in gaining insight into the model's decisions as the memory and operations on it can be directly inspected at any time, and may assist in scaling the method to such a size that it becomes viable for industry settings.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020
PublisherInstitute of Electrical and Electronics Engineers
Pages303-308
Number of pages6
ISBN (Electronic)9781728163895
DOIs
Publication statusPublished - 10 Jun 2020
Event3rd IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020 - Virtual, Tampere, Finland
Duration: 10 Jun 202012 Jun 2020

Conference

Conference3rd IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020
CountryFinland
CityVirtual, Tampere
Period10/06/2012/06/20

Keywords

  • Combinatorial Optimization
  • Deep Learning
  • Recurrent Neural Network
  • Routing

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