### Resumen

Idioma original | English (US) |
---|---|

Páginas (desde-hasta) | 556 - 564 |

Número de páginas | 9 |

Publicación | Communications in Computer and Information Science |

Volumen | 742 |

Estado | Published - ago 29 2017 |

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*Communications in Computer and Information Science*,

*742*, 556 - 564.

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*Communications in Computer and Information Science*, vol. 742, pp. 556 - 564.

**Solving the Interval Green Inventory Routing Problem Using Optimization and Genetic Algorithms.** / Franco Franco, Carlos Alberto; Figueroa-Garcia, Juan Carlos ; Lopéz-Santana, eduyn ramiro.

Resultado de la investigación: Contribución a Revista › Artículo

TY - JOUR

T1 - Solving the Interval Green Inventory Routing Problem Using Optimization and Genetic Algorithms

AU - Franco Franco, Carlos Alberto

AU - Figueroa-Garcia, Juan Carlos

AU - Lopéz-Santana, eduyn ramiro

PY - 2017/8/29

Y1 - 2017/8/29

N2 - 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.

AB - 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.

M3 - Article

VL - 742

SP - 556

EP - 564

JO - Communications in Computer and Information Science

JF - Communications in Computer and Information Science

SN - 1865-0929

ER -