Abstract
The growing demand for logistics services for deliveries, collections, or home health services, have significantly increased. However, there is a need to have a technologically innovative information system for digitizing data in the operational logistics of these services, required for an increasingly better vehicle route planning. Unsurprisingly, for many years, there has been an increasing and steady growth in the interest and development of optimization tools to solve real-world problems, namely in the logistic domain. The evolution and support of computational power and the fact that advances in optimization solvers have allowed many of them to be developed as free or open-source software, to the detriment of some classic numerical calculation software. The main issue arises in the dynamic search for solutions obtained by open-source solvers and how they can be useful in solving complex combinatorial problems in real life, such as the optimal allocation of routes in logistics planning services. This work proposes an application that integrates the Google OR-Tools software and the Google Maps and Distance Matrix API. The approach developed in this work uses a VRP mathematical model to minimize the maximum route (considering as objective function the time or the distance) and provide a workload balancing, with the use of a cloud application to reduce costs and an online map service. Experimental results were obtained on simulated VRP instances in the district of Porto, where the quality of the computational solution is analyzed for training and easy usability in logistics problems.
This work has been supported by FCT – Fundação para a Ciência e a Tecnologia within the R&D Units Projects Scope: UIDB/00319/2020. Filipe Alves is supported by FCT Doctorate Grant Reference SFRH/BD/143745/2019.
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Alves, F., Pacheco, F., Rocha, A.M.A.C., Pereira, A.I., Leitão, P. (2021). Solving a Logistics System for Vehicle Routing Problem Using an Open-Source Tool. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12953. Springer, Cham. https://doi.org/10.1007/978-3-030-86976-2_27
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