Solving the Interval Green Inventory Routing Problem Using Optimization and Genetic Algorithms

Carlos Alberto Franco Franco, Juan Carlos Figueroa-Garcia, eduyn ramiro Lopéz-Santana

Resultado de la investigación: Contribución a RevistaArtículo

1 Cita (Scopus)

Resumen

In this paper, we present a genetic algorithm embedded with mathematical optimization to solve a green inventory routing problem with interval fuel consumption. Using the idea of column generation in which only attractive routes are generated to the mathematical problem, we develop a genetic algorithm that allow us to determine speedily attractive routes that are connected to a mathematical model. We code our genetic algorithm using the idea of a integer number that represents all the feasible set of routes in which the maximum number allowed is the binary number that represents if a customer is visited or not. We approximate the fuel consumption as an interval number in which we want to minimize the overall fuel consumption of distribution. This is the first approximation made in the literature using this type of methodology so we cannot compare our approach with those used in the literature.
Idioma originalEnglish (US)
Páginas (desde-hasta)556 - 564
Número de páginas9
PublicaciónCommunications in Computer and Information Science
Volumen742
EstadoPublished - ago 29 2017

Huella dactilar

Routing Problem
Fuel consumption
Optimization Algorithm
Genetic algorithms
Genetic Algorithm
Interval
Interval number
Column Generation
Customers
Mathematical Model
Mathematical models
Binary
Minimise
Integer
Optimization
Methodology
Approximation

Citar esto

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abstract = "In this paper, we present a genetic algorithm embedded with mathematical optimization to solve a green inventory routing problem with interval fuel consumption. Using the idea of column generation in which only attractive routes are generated to the mathematical problem, we develop a genetic algorithm that allow us to determine speedily attractive routes that are connected to a mathematical model. We code our genetic algorithm using the idea of a integer number that represents all the feasible set of routes in which the maximum number allowed is the binary number that represents if a customer is visited or not. We approximate the fuel consumption as an interval number in which we want to minimize the overall fuel consumption of distribution. This is the first approximation made in the literature using this type of methodology so we cannot compare our approach with those used in the literature.",
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Solving the Interval Green Inventory Routing Problem Using Optimization and Genetic Algorithms. / Franco Franco, Carlos Alberto; Figueroa-Garcia, Juan Carlos ; Lopéz-Santana, eduyn ramiro.

En: Communications in Computer and Information Science, Vol. 742, 29.08.2017, p. 556 - 564.

Resultado de la investigación: Contribución a RevistaArtículo

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