Simulation of increased vehicle fleet to adapt transport optimization in a Model of competitiveness associated with a Smart City

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Programación Matemática y Software
Universidad Autónoma del Estado de Morelos
Dr.Marco Antonio Cruz Chávez
2007-3283
Volumen 10, Número 3 /Octubre de 2018
Periodo Octubre-Enero 2019
Artículo de Investigación
59-70
Computación
Octure del 2018
Cuatrimestral

Alberto Ochoa,G. Arroyo-Figueroa, Yasmin Hernandez, Guillermo Escobedo, Peter Oropeza

Maestría en Cómputo Aplicado, Universidad Autónoma de Ciudad Juárez 2Instituto Nacional de Electricidad y Energías Limpias (INNEL)

Recibido: 30 de julio de 2017 Aceptado: 2 de marzo de 2018 Publicado en línea: 12 noviembre de 2018

Abstract. The proposal of a model of intelligent logistics that allows help influence the optimization of a Smart City is presented in this investigation, and thereby achieve reduce traffic accidents specifically those related to transport any type of product to generate energy. In addition, the problem of routing of vehicles (VRP) is the approach from which faces the problem of logistics, management, management and distribution of goods from one point to a destination. When is required to distribute loads to the classic problem, then it is considered the VRP with the capacity extension or (CVRP). The present work focuses on using an instance with the purpose of implementing an improvement to transport of hydrocarbonsation in the Metropolitan Area of Cuernavaca (MAoC). It uses the language for technical calculation called (MATLAB). The algorithm used in this work is described below: a) Start from the depot, b) Examine the outputs that have not been served, outputs may be feasible and infeasible) For choose the best feasible outputs, for example having the shortest distance, insert it into the route and position before the last exit on that route, d) If there are more feasible outputs, repeat from point b), but to create a new route and start the flow from the point a), e) If all exits were covered, but finish initiate flow from the point a). The results show that the route can be optimized by distributing and reordering of units so that all points are covered.

Keywords: Smart City, CVRP, Matlab, Metropolitan Area, instances.