The GRASP Metaheuristic for the Electric Vehicle Routing Problem

Woller, David; Kozák, Viktor; Kulich, Miroslav

doi:10.1007/978-3-030-70740-8_12

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Note that HGS does not support the CEVRP variant with battery constraints, so we are not able to embed directly HGS library to our IIL framework. To make it work, we add a post-processing step, similarly to the GRASP (Woller, Kozák, and Kulich 2021), to rebuild feasible solutions that fit the battery constraints. To test the generalizability of our approach, we run our IIL algorithms on Dataset 6 and compare the results with VNS and BACO (i.e., SOTA algorithms for the CEVRP instances), which clearly shows that IIL+HGS is much better than the heuristics in terms of both solution quality and running time.…”

Section: Methodsmentioning

confidence: 99%

Imitation Improvement Learning for Large-Scale Capacitated Vehicle Routing Problems

Bui

Mai

2023

ICAPS

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Recent works using deep reinforcement learning (RL) to solve routing problems such as the capacitated vehicle routing problem (CVRP) have focused on improvement learning-based methods, which involve improving a given solution until it becomes near-optimal. Although adequate solutions can be achieved for small problem instances, their efficiency degrades for large-scale ones. In this work, we propose a new improvement learning-based framework based on imitation learning where classical heuristics serve as experts to encourage the policy model to mimic and produce similar and better solutions. Moreover, to improve scalability, we propose Clockwise Clustering, a novel augmented framework for decomposing large-scale CVRP into subproblems by clustering sequentially nodes in clockwise order, and then learning to solve them simultaneously. Our approaches enhance state-of-the-art CVRP solvers while attaining competitive solution quality on several well-known datasets, including real-world instances with sizes up to 30,000 nodes. Our best methods are able to achieve new state-of-the-art solutions for several large instances and generalize to a wide range of CVRP variants and solvers. We also contribute new datasets and results to test the generalizability of our deep RL algorithms.

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Section: Methodsmentioning

confidence: 99%

Imitation Improvement Learning for Large-Scale Capacitated Vehicle Routing Problems

Bui

Mai

2023

ICAPS

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“…For the comparisons, the three winning algorithms of the CEVRP competition at the IEEE WCCI2020 conference, which are named VNS, SA and GA, are considered. Also, it is compared with the proposal of Woller et al called Greedy Randomized Adaptive Search Procedure (GRASP) (Woller et al, 2020). Furthermore, a comparison is also made with the results of Jia et al from 2021.…”

Section: Baseline Algorithms and Compared Methodsmentioning

confidence: 99%

A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem

Rodríguez-Esparza¹,

Masegosa²,

Oliva³

et al. 2022

Preprint

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Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming as a result of the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASA RL ) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The HH implemented improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition.

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