Efficiently Solving the Practical Vehicle Routing Problem

Duan, Liwei; Yang, Zhan; Hu, Haoyuan; Gong, Yu; Wei, Jiangwen; Zhang, Xiaodong; Xu, Yinghui

doi:10.1145/3394486.3403356

Cited by 58 publications

(12 citation statements)

References 10 publications

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“…Rather than relying on a single decoder for sequence generation, Xin et al [100] proposed a multi-decoder mechanism to generate several partial solutions simultaneously and combine them using tree search to expand the searching space. Duan et al [98] on the other hand, focused on more effective feature representation ability of the network itself. They augmented the structure with GCN, and develop a joint learning approach using both DRL and supervised learning.…”

Section: Sequence Generation Methodsmentioning

confidence: 99%

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Zong¹,

Feng²,

Tong³

et al. 2021

Preprint

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Recent technology development brings the booming of numerous new Demand-Driven Services (DDS) into urban lives, including ridesharing, on-demand delivery, express systems and warehousing. In DDS, a service loop is an elemental structure, including its service worker, the service providers and corresponding service targets. The service workers should transport either humans or parcels from the providers to the target locations. Various planning tasks within DDS can thus be classified into two individual stages: 1) Dispatching, which is to form service loops from demand/supply distributions, and 2)Routing, which is to decide specific serving orders within the constructed loops. Generating high-quality strategies in both stages is important to develop DDS but faces several challenging. Meanwhile, deep reinforcement learning (DRL) has been developed rapidly in recent years. It is a powerful tool to solve these problems since DRL can learn a parametric model without relying on too many problem-based assumptions and optimize long-term effect by learning sequential decisions. In this survey, we first define DDS, then highlight common applications and important decision/control problems within. For each problem, we comprehensively introduce the existing DRL solutions, and further summarize them in https://github.com/tsinghua-fib-lab/DDS Survey. We also introduce open simulation environments for development and evaluation of DDS applications. Finally, we analyze remaining challenges and discuss further research opportunities in DRL solutions for DDS.

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Section: Sequence Generation Methodsmentioning

confidence: 99%

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Zong¹,

Feng²,

Tong³

et al. 2021

Preprint

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show abstract

“…Later, [24] extended the attention method to the VRP outperforming [31], followed by [40] who also expanded their model to the VRP case obtaining lower gaps. A specialized VRP model combined reinforcement and supervised learning to learn to construct solutions, outperforming [24], but trained on different distributions of node locations [8]. Another VRP method, named neural large neighborhood search (NLNS) [15] proposed integrating learning methods and classical search.…”

Section: Related Workmentioning

confidence: 99%

“…These problems are harder to solve than the TSP due to the added constraints and usually require tailored heuristics. Both problems have also been subject of the recent interest in combining machine learning and combinatorial optimization [8,16,19,34]. However, few previously proposed models can be seamlessly used in multiple routing problems [24,40].…”

Section: Introductionmentioning

confidence: 99%

Learning 2-Opt Heuristics for Routing Problems via Deep Reinforcement Learning

et al. 2021

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Recent works using deep learning to solve routing problems such as the traveling salesman problem (TSP) have focused on learning construction heuristics. Such approaches find good quality solutions but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance. However, few studies have focused on improvement heuristics, where a given solution is improved until reaching a near-optimal one. In this work, we propose to learn a local search heuristic based on 2-opt operators via deep reinforcement learning. We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution. Moreover, we introduce a policy neural network that leverages a pointing attention mechanism, which can be easily extended to more general k-opt moves. Our results show that the learned policies can improve even over random initial solutions and approach near-optimal solutions faster than previous state-of-the-art deep learning methods for the TSP. We also show we can adapt the proposed method to two extensions of the TSP: the multiple TSP and the Vehicle Routing Problem, achieving results on par with classical heuristics and learned methods.

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“…They showed that this approach is comparable to Genetic Algorithm (GA) for mediumsized instances (≈ 50 cities), but its performance becomes better than GA for largersized instances (> 100 cities) while taking much less computational time. Duan et al (2020) proposed a technique that combines training with reinforcement learning and supervised learning. The method was based on graph convolutional network to encode a problem instance with node and edge features.…”

Section: Learning To Construct Vrp Solutionsmentioning

confidence: 99%

Analytics and Machine Learning in Vehicle Routing Research

Bai¹,

Chen²,

Chen³

et al. 2021

Preprint

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The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed. To tackle the complexities, uncertainties and dynamics involved in real-world VRP applications, Machine Learning (ML) methods have been used in combination with analytical approaches to enhance problem formulations and algorithmic performance across different problem solving scenarios. However, the relevant papers are scattered in several traditional research fields with very different, sometimes confusing, terminologies. This paper presents a first, comprehensive review of hybrid methods that combine analytical techniques with ML tools in addressing VRP problems. Specifically, we review the emerging research streams on ML-assisted VRP modelling and ML-assisted VRP optimisation. We conclude that ML can be beneficial in enhancing VRP modelling, and improving the performance of algorithms for both online and offline VRP optimisations. Finally, challenges and future opportunities of VRP research are discussed.

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Efficiently Solving the Practical Vehicle Routing Problem

Cited by 58 publications

References 10 publications

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Learning 2-Opt Heuristics for Routing Problems via Deep Reinforcement Learning

Analytics and Machine Learning in Vehicle Routing Research

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