Analytics and machine learning in vehicle routing research

@article{Bai2021AnalyticsAM,
  title={Analytics and machine learning in vehicle routing research},
  author={Ruibin Bai and Xinan Chen and Zhi-Long Chen and Tianxiang Cui and Shuhui Gong and Wentao He and Xiaoping Jiang and Huan Jin and Jiahuan Jin and Graham Kendall and Jiawei Li and Zhengyong Lu and Jianfeng Ren and P. Weng and Ning Xue and Huayan Zhang},
  journal={International Journal of Production Research},
  year={2021},
  volume={61},
  pages={4 - 30}
}
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… 
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14 Citations
Background Citations
6
Methods Citations
2

14 Citations

An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem

This paper reviews recent advances in learning-based optimization (LBO) techniques and divides relevant approaches into end-to-end approaches and step-by-step approaches, and concludes the applicable types of problems for different LBO algorithms.

Analytics and machine learning in scheduling and routing research

This special issue largely originated from various discussions during several cross-domain, multi-disciplinary conferences and workshops, especially the 9th Multidisciplinary International Scheduling…

Combining Constructive and Perturbative Deep Learning Algorithms for the Capacitated Vehicle Routing Problem

The Combined Deep Constructor and Perturbator is developed, which combines two powerful constructive and perturbative Deep Learning-based heuristics, using attention mechanisms at their core, and demonstrates a cost improvement in common datasets when compared against other multiple Deep Learning methods.

Analysis of box and ellipsoidal robust optimization, and attention model based reinforcement learning for a robust vehicle routing problem

In this work, we consider a class of vehicle routing problem that uses simultaneous pickup and delivery and is constrained by a hard service time window with an objective to minimize costs. In a…

Attention, Filling in The Gaps for Generalization in Routing Problems

This paper aims at encouraging the consolidation of the Attention model through understanding and improving current existing models, namely the attention model by Kool et al, and identifies two discrepancy categories for VRP generalization.

A Routing Method for Ridesharing Service by Applying CPLEX

This paper presents a VRPTW solving model that applies Mixed-Integer Programming (MIP) to optimize transport costs and number of vehicles and results show that the cost andnumber of vehicles are reasonably optimized for this model.

Multiple-Drones-Multiple-Trucks Routing Problem for Disruption Assessment

This study proposes a multiple-drones-multiple-trucks (MDMT) routing problem to assess infrastructure in the areas of disruption epicenters using multiple drones that operate in synchronicity with…

Neural Combinatorial Optimization for Coverage Planning in UGV Reconnaissance

The urban coverage reconnaissance task is formulated as a vehicle routing problem, and a new traversal model is proposed, which takes account the connectivity characteristics of the road and proposes a neural combinatorial optimization technique to solve the problem.

Digital Twin Applications in Urban Logistics: An Overview

Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external effects pertaining to pollution and congestion. In order to…

In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning

A low-cost machine learning system that uses in-vehicle data is proposed to solve three categorization problems; road surface conditions, road traffic conditions and driving style.

References

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