Using VRPSolver to efficiently solve the Differential Harvest Problem

Volte, Gabriel; Bourreau, Eric; Giroudeau, Rodolphe; Naud, Olivier

doi:10.1016/j.cor.2022.106029

Cited by 3 publications

(1 citation statement)

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“…VRPSolver is freely available for academic purposes, new applications can be developed by defining a suitable model in a C++ (Sadykov and Vanderbeck, 2021) or Julia/JuMP language interface 1 . At the time of writing, VRPSolver website lists 23 papers that used that tool on a variety of problems, including for example, multishuttle crane scheduling (Polten and Emde, 2022) or the differential harvesting problem (Volte et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

VRPSolverEasy: A Python Library for the Exact Solution of a Rich Vehicle Routing Problem

Errami,

Queiroga,

Sadykov

et al. 2023

INFORMS Journal on Computing

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The optimization community has made significant progress in solving vehicle routing problems (VRPs) to optimality using sophisticated branch-cut-and-price (BCP) algorithms. VRPSolver is a BCP algorithm with excellent performance in many VRP variants. However, its complex underlying mathematical model makes it hardly accessible to routing practitioners. To address this, VRPSolverEasy provides a Python interface to VRPSolver that does not require any knowledge of mixed integer programming modeling. Instead, routing problems are defined in terms of familiar elements, such as depots, customers, links, and vehicle types. VRPSolverEasy can handle several popular VRP variants and arbitrary combinations of them. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: This work was supported by Faperj [Grant E-26/202.887/2017] and Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grant 305684/2022-1]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0103 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0103 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

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Section: Introductionmentioning

confidence: 99%