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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

CVRPTW Model for Cargo Collection with Heterogeneous Capacity-Fleet

verfasst von : Jorge Ivan Romero-Gelvez, William Camilo Gonzales-Cogua, Jorge Aurelio Herrera-Cuartas

Erschienen in: Applied Informatics

Verlag: Springer International Publishing

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Abstract

This work shows the application of the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) to collect different cargo-demand in several locations with low time disponibility to attend any vehicle. The objective of the model is to reduce the routing time in a problem with mixed vehicle-fleet. The initial step is the creation of a distance matrix by using the Google Maps API, then cargo capacities for every vehicle and time-windows for every demand point are included in the model. The problem is solved with Google-OR tools using as firt solution aproximated algoritm and as second solution one metaheuristic algorithm for local search.

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Vorheriges Kapitel Continuous Variable Binning Algorithm to Maximize Information Value Using Genetic Algorithm
Nächstes Kapitel Evaluation of Transfer Learning Techniques with Convolutional Neural Networks (CNNs) to Detect the Existence of Roads in High-Resolution Aerial Imagery
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Metadaten
Titel
CVRPTW Model for Cargo Collection with Heterogeneous Capacity-Fleet
verfasst von
Jorge Ivan Romero-Gelvez
William Camilo Gonzales-Cogua
Jorge Aurelio Herrera-Cuartas
Copyright-Jahr
2019
Buch
Applied Informatics

Print ISBN: 978-3-030-32474-2

Electronic ISBN: 978-3-030-32475-9

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-32475-9_13

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