Erratum—Exact Algorithms for the Clustered Vehicle Routing Problem

Hoogeboom, Maaike; Battarra, Maria; Erdoğan, Güneş; Vigo, Daniele

doi:10.1287/opre.2016.1491

Cited by 2 publications

(1 citation statement)

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“…Such an optimisation problem is much smaller in size than an overall optimisation procedure, since only the clusters in two consecutive periods are considered for reallocation. The formulation developed in this section is an adaptation of the Vehicle Routing Problem (VRP), see [17,19], where we minimise the route of the cleaning vehicle for each period, whilst considering energy loss as a penalty factor.…”

Section: Cluster Scheduling Local Searchmentioning

confidence: 99%

Heliostat field cleaning scheduling for Solar Power Tower plants: A heuristic approach

2019

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Section: Cluster Scheduling Local Searchmentioning

confidence: 99%

Heliostat field cleaning scheduling for Solar Power Tower plants: A heuristic approach

2019

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Bilevel Memetic Search Approach to the Soft-Clustered Vehicle Routing Problem

Zhou

Kou

Zhou

2023

Transportation Science

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This work addresses a soft-clustered vehicle routing problem that extends the classical capacitated vehicle routing problem with one additional constraint, that is, customers are partitioned into clusters and all customers of the same cluster must be served by the same vehicle. Its potential applications include parcel delivery in courier companies and freight transportation. Due to its NP-hard nature, solving it is computationally challenging. This paper presents an efficient bilevel memetic search method to do so, which explores search space at both cluster and customer levels. It integrates three distinct modules: a group matching-based crossover (to generate promising offspring solutions), a bilevel hybrid neighborhood search (to perform local optimization), and a tabu-driven population reconstruction strategy (to help the search escape from local optima). Extensive experiments on three sets of 390 widely used public benchmark instances are conducted. The results convincingly demonstrate that the proposed method achieves much better overall performance than state-of-the-art algorithms in terms of both solution quality and computation time. In particular, it is able to find 20 new upper bounds for large-scale instances while matching the best-known upper bounds for all but four of the remaining instances. Ablation studies on three key algorithm modules are also performed to demonstrate the novelty and effectiveness of the proposed ideas and strategies. Funding: This work was supported by the Macau Young Scholars Program [Grant AM2020011], Fundo para o Desenvolvimento das Cienciase da Tecnologia (FDCT) [Grant 0047/2021/A1], the National Natural Science Foundation of China [Grants 61903144, 71871142, and 71931007], and the Open Project of the Shenzhen Institute of Artificial Intelligence and Robotics for Society [Grant AC01202005002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1186 .

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Erratum—Exact Algorithms for the Clustered Vehicle Routing Problem

Cited by 2 publications

References 1 publication

Heliostat field cleaning scheduling for Solar Power Tower plants: A heuristic approach

Heliostat field cleaning scheduling for Solar Power Tower plants: A heuristic approach

Bilevel Memetic Search Approach to the Soft-Clustered Vehicle Routing Problem

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