The Vehicle-Routing Problem

Daneshzand, Farzaneh

doi:10.1016/b978-0-12-385202-1.00008-6

Cited by 19 publications

(16 citation statements)

References 109 publications

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“…• CVRP-VRP capacitado: esta variante se ha conocido como la variación básica del VRP, donde se requiere que la carga transportada no supere la capacidad de los vehículos, y la ruta inicia y finaliza en el depósito o centro de distribución. Las versiones de esta variante son ACVRP cuando la matriz de costos es asimétrica, y SCVRP cuando la matriz de costos es simétrica (Daneshzand, 2011;Sörensen y Schittekat, 2013).…”

Section: Descripción De Algunas Variantes Del Problema De Ruteo De Vehículos (Vrp)unclassified

Solución del problema de enrutamiento de vehículos con entregas y recogidas simultáneas : una nueva matheurística

Silva¹,

Pablo²

2020

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Este libro, producto de mi tesis doctoral, presenta una metodología para resolver el problema de enrutamiento de vehículos homogéneos con recogidas y entregas simultáneas (VRPSPD) utilizando matheurística formada por el algoritmo genético especializado Chu -Beasley y técnicas exactas de programación lineal de enteros mixtos, basadas en el procedimiento Branch -and- Bound, aplicado a la mejor configuración obtenida del algoritmo genético con el apoyo de métodos heurísticos constructivos en la determinación de los subproblemas, que hacen parte de la generación de la población inicial, necesaria en la etapa de mejora local. El problema considera un conjunto de clientes, cuyas demandas de recogida y entrega de productos o personas son conocidas, y cuyo objetivo es obtener el conjunto de rutas de costo mínimo, que permitan satisfacer la demanda de los clientes, considerando las respectivas limitaciones del sistema y los vehículos necesarios para completar el mismo. En su desarrollo se ...

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Section: Descripción De Algunas Variantes Del Problema De Ruteo De Vehículos (Vrp)unclassified

Solución del problema de enrutamiento de vehículos con entregas y recogidas simultáneas : una nueva matheurística

Silva¹,

Pablo²

2020

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“…Common heuristic algorithm includes ant algorithm, genetic algorithm, simulated annealing algorithm, and particle swarm optimization. [3][4][5][6] These heuristic algorithms are the natural calculation of simulating the phenomenon principle in the natural world, which seeks for the best solution or similar best solution rapidly based on the priori knowledge, and the best solution is sought within the acceptable time. Recently, there are more algorithms about the neural network, especially, the rising of deep learning provides a kind of new support in theory for solving the logistics distribution path optimization.…”

Section: Relevant Researchmentioning

confidence: 99%

Road condition prediction and logistics distribution path optimization algorithm based on traffic big data

Liang

Jiang

et al. 2019

Journal of Algorithms & Computational Technology

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In view of the complex road conditions in today's cities, the traditional prediction methods for road conditions are not so accurate, and the optimization algorithm for the logistics distribution path is not sensitive to changes in the road conditions so that its application in an actual logistics distribution system is not effective. This article proposes a road condition prediction and logistics distribution path optimization algorithm based on traffic big data. First, it analyses the characteristics of the road condition information of traffic big data. By combining the powerful feature extraction and self-learning ability of a deep belief network, it establishes a road condition prediction model based on a deep belief network and completes the model training and verification through the learning of traffic big data. Then, it combines the road condition prediction (result) information, traffic network information, and logistics distribution information to construct the time-share weighted traffic network. It then modifies the access set and pheromone variables of the ant algorithm based on the time-share traffic network to establish the road condition prediction and logistics distribution path optimization algorithm based on traffic big data. Finally, it conducts comparative experiments with other logistics distribution path optimization algorithms. The experimental results show that the proposed algorithm is superior to other logistics distribution optimization algorithms. Therefore, this algorithm is an effective method for optimizing logistics distribution.

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“…The optimal routes of assigned vehicles to distribute products from one or multiple distributors to geographically dispersed retailers can be determined by applying the concepts of the vehicle routing problem (VRP) (Laporte et al, 1988). According to Daneshzand (2011), Chen (2018) and Liao et al (2017), the VRP determines a set of routes that starts and ends at the distributor minimizing the transportation cost and fulfilling the customer demands and operational constraints. As stated by Goetschalckx (2011), the simplest of the vehicle routing problems (VRP) is the traveling salesman problem (TSP) that determines the route of a single vehicle without vehicle capacity constraint.…”

Section: Introductionmentioning

confidence: 99%

Designing Clusters of Distribution Area and Delivery Route for Maximizing the Vehicle Utilization and Minimizing the Workload Gap and Transportation Cost

Santoso

Parung

Prayogo

et al. 2021

OSCM: An Int. Journal

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This paper deals with determining vehicle routes in product distribution from a distributor to a large number of retailers in a vast distribution area. To determine the vehicle routes in a vast distribution area and a large number of retailers that have to be served causes the VRP model to become complicated. Therefore, this paper applies the cluster firstroute second concept to design a distribution system in two stages. The first stage is to cluster the distribution area based on the distance between retailers. The second stage is to determine a vehicle route for each cluster using the multiobjective vehicle routing problem with time window and balanced driver workload (VRPTWBW) approach. The proposed VRPTWBW model has three objectives, i.e., (i) to minimize the number of assigned vehicles, (ii) to minimize the total delivery time, and (iii) to balance the driver workload. This second stage starts once the clusters of the distribution area are set. Contrasted with the current practice in the company, this proposed model offers the significant impacts: (i) the reduction of workload gap among driver about 29% (from 2.59 hours to 1.83 hours); (ii) the increased of vehicle utility by 44% (from 30% to 43%), and (iii) the increase savings of twoweek course fees of IDR. 11 million (about 23.84%).

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The Vehicle-Routing Problem

Cited by 19 publications

References 109 publications

Solución del problema de enrutamiento de vehículos con entregas y recogidas simultáneas : una nueva matheurística

Solución del problema de enrutamiento de vehículos con entregas y recogidas simultáneas : una nueva matheurística

Road condition prediction and logistics distribution path optimization algorithm based on traffic big data

Designing Clusters of Distribution Area and Delivery Route for Maximizing the Vehicle Utilization and Minimizing the Workload Gap and Transportation Cost

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