A decision-support tool for a capacitated location-routing problem

Lopes, Rui Borges; Barreto, Sérgio; Ferreira, Carlos; Santos, Beatriz Sousa

doi:10.1016/j.dss.2008.07.007

Cited by 52 publications

(22 citation statements)

References 20 publications

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“…Researchers in various fields employ WMAs to develop geospatial Web applications. Example usages of these WMAs are development of a web-based tool to provide tourism information for tourists in Charleston, South Carolina (Pan, Crotts, and Muller 2007); development of a decision-support tool to help analyze optimal locations for depots such that location and distribution costs are minimized (Lopes et al 2008); development of a Web map for route and direction guidance to referral hospitals (Kobayashi et al 2010); analysis of an origin-destination travel time matrix (Wang and Xu 2011); and development of a Web map that provides personalized optimal routes to people with physical disabilities (Karimi, Zhang, and Benner 2013). However, despite such projects, currently there is no study that compares the routes WMAs generate.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of routes generated by Web Mapping APIs

Socharoentum

Karimi

2014

Cartography and Geographic Information Science

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Web Mapping APIs (WMAs), such as Google Maps API, are widely used by researchers across different fields to develop geospatial Web applications. Among maps and map functionalities provided through WMAs, route and direction are prominent and commonly available. Given that each WMA uses a different map database and a different set of assumptions, the routes they generate, for the same pairs of origin and destination addresses, are different. Considering the current void in literature on WMAs and the routes they generate, in this paper, select common WMAs are compared and analyzed based on their routing techniques. The results of these comparisons will benefit researchers by helping them better understand the behavior of WMAs in producing routes, which in turn can be used for selecting suitable WMAs for research projects or developing applications (such as navigation and location-based services). The process in which routes are evaluated can also be used as a guideline to help researchers explore behavior of WMAs in generating routes.

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Section: Introductionmentioning

confidence: 99%

A comparative analysis of routes generated by Web Mapping APIs

Socharoentum

Karimi

2014

Cartography and Geographic Information Science

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“…Luego se procede a asignar dichos clústeres a un depósito potencial, para luego resolver el correspondiente problema de ruteo de vehículos (VRP). Tres trabajos han considerado técnicas basadas en clústeres para el CLRP [5], [13] y [14].…”

Section: B Algoritmos Heurísticos Basados En Clústeresunclassified

Problema de Localización y Ruteo con Restricciones de Capacidad: Revisión de la Literatura

2015

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ResumenEn este artículo se hace una revisión exhaustiva del estado del arte de las metodologías de solución existentes para el problema combinado de localización y ruteo con restricciones de capacidad (CLRP). El problema de CLRP tiene una gran cantidad de aplicaciones prácticas en temas relacionados con transporte. Se ha propuesto el siguiente esquema de clasificación de acuerdo al método de solución: (1) Algoritmos Heurísticos Constructivos, (2) Algoritmos Heurísticos Basados en Clústeres, (3) Algoritmos Heurísticos Basados en Trayectoria, (4) Algoritmos Heurísticos Basados en Población, (5) Algoritmos Heurísticos Combinados, (6) Métodos Exactos. Se hace especial énfasis en fortalezas y debilidades de cada metodología publicada, identificando oportunidades de investigación y desarrollo en el área, en el contexto de la aplicación práctica de la problemática.Palabras claves: Problemas de localización y ruteo, Revisión Literatura, Algoritmos Metaheurísticos, Métodos Exactos. AbstractIn this paper, we review the state of the art of the published solution methods for combined problems of location and routing with capacity constraints (CLRP). The CLRP has several practical application in topics related to transportation. We have proposed the following classification scheme based on the solution method: (1) Constructive

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“…The comparative results showed that their proposed method obtained the same or better results than the best known solutions (BKS). Lopes et al (2008) developed a decision tool and user interface to solve the CLRP. The user interface can help managers who do not have any background on modeling and optimization methods, to make more scientific decision, in a way that is easily understandable.…”

Section: Literature Review and Problem Definitionmentioning

confidence: 99%

Using greedy clustering method to solve capacitated location-routing problem

Nadizadeh¹,

Sahraeian²,

Zadeh³

et al. 2011

Afr. J. Bus. Manage.

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This paper introduces a new heuristic method to solve the location-routing problem (LRP). Facility location problem (FLP) and vehicle routing problem (VRP) are considered simultaneously in the LRP. The problem selects the location of depot(s) to be established among a set of potential sites. On the other hand, the allocation of customers to depot(s), and the distribution routes between the customers and depot(s) are decided, too. In this paper, capacitated LRP (CLRP) is considered, in which the vehicles and the depots have a predefined capacity to serve the customers. A greedy clustering method (GCM-LRP) in four phases is proposed. The method clusters the customers using a greedy search algorithm, selects the most appropriate location of depot(s), allocates the clusters to the depot(s), and finally sets routes between the depot(s) and customers using ant colony system (ACS). The numerical experiments on a set of benchmark instances show the efficiency of the proposed method.

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A decision-support tool for a capacitated location-routing problem

Cited by 52 publications

References 20 publications

A comparative analysis of routes generated by Web Mapping APIs

A comparative analysis of routes generated by Web Mapping APIs

Problema de Localización y Ruteo con Restricciones de Capacidad: Revisión de la Literatura

Using greedy clustering method to solve capacitated location-routing problem

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