An Optimization Model of a Sustainable City Logistics Network Design Based on Goal Programming

Abstract

This paper investigates the joint optimization problem on the logistics infrastructure investment and CO2 emission taxes for a sustainable city logistics network design by a goal programming approach where the cost recovery, service level and CO2 emission reduction goals are involved. The above multi-objective logistics infrastructure capacity investment and CO2 emission taxes problem is formulated as a bi-level goal programming model. Given the priority structure of the goals, the total deviations from predetermined goals are minimized in the upper level, while the lower level of the model serves as the service route choice equilibrium problem of logistics users. To solve the proposed model, a genetic algorithm is developed, where the method of successive average (MSA) is embedded. The case study focusing on the urban logistics network of Changsha, China demonstrates the effectiveness of the bi-level goal programming model and the genetic algorithm. The findings reveal that the priority rankings of the goals have a significant impact on the joint decisions of CO2 emission taxes and logistics infrastructure capacity investment. The proposed methodology provides an avenue to balance multiple conflicting objectives and obtain an economical and environmental city logistics network.

Los indicadores de Desempeño de la Distribución Urbana de Mercancías: Un Análisis Bibliométrico

“…En términos de calidad ambiental, la Calidad de Vida examina cómo las operaciones de transporte afectan la calidad de vida de los ciudadanos, considerando aspectos como la contaminación del aire, el ruido, la seguridad vial y la experiencia de los visitantes en zonas antiguas de las ciudades [9]. El Objetivo de Nivel de Servicio, contribuyó a mejorar la calidad de vida al reducir la contaminación y mejorar la eficiencia logística [65].…”

“…Entre este tipo se encontraron en la revisión sistemática las siguientes: algoritmo HY-NSGA-II K. [76] y HY-NSGA-II [77]. Se destacan por su enfoque evolutivo multiobjetivo Los algoritmos genéticos, inspirados en la evolución biológica [35,36,65]. Por otro lado, los Algoritmos Inmunológicos (IA) e Inmunológico Híbrido (HIA) incorporan conceptos de inmunidad y enfermedades epidémicas [2].…”

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