A Multiperiod Maximal Covering Location Model for the Optimal Location of Intersection Safety Cameras on an Urban Traffic Network

Dell’Olmo, Paolo; Ricciardi, Nicoletta; Sgalambro, Antonino

doi:10.1016/j.sbspro.2013.12.824

Cited by 27 publications

(20 citation statements)

References 11 publications

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“…Fazel considered large scale dynamic MCLP. Dell'Olmo et al (2014) proposed a multi period MCLP for the optimal location of intersection safety cameras on an urban traffic network. Vatsa and Jayaswal (2016) present a new formulation for a multi-period MCLP with server uncertainty, motivated by its relevance with respect to primary health centers.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic capacitated maximal covering location problem by considering dynamic capacity

Bagherinejad¹,

Shoeib²

2018

10.5267/j.ijiec

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Capacitated maximal covering location problems (MCLP) have considered capacity constraint of facilities but these models have been studied in only one direction. In this paper, capacitated MCLP and dynamic MCLP are integrated with each other and dynamic capacity constraint is considered for facilities. Since MCLP is NP-hard and commercial software packages are unable to solve such problems in a rational time, Genetic algorithm (GA) and bee algorithm are proposed to solve this problem. In order to achieve better performance, these algorithms are tuned by Taguchi method. Sample problems are generated randomly. Results show that GA provides better solutions than bee algorithm in a shorter amount of time.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic capacitated maximal covering location problem by considering dynamic capacity

Bagherinejad¹,

Shoeib²

2018

10.5267/j.ijiec

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“…En [4] reconocen la complejidad de los modelos de localización no lineales en redes de transporte y proponen un modelo de programación entera mixta para la localización de sensores en autopistas. En [5] se presenta un modelo de localización multi-período para la maximización de la cobertura total de las cámaras considerando que la efectividad de los programas de prevención se aumenta al cambiar periódicamente la posición de las cámaras. Para el problema de localización en redes de transporte, en [6] consideran incertidumbre por medio de un modelo de programación estocástica que considera diferentes condiciones de tráfico en la red para encontrar el número óptimo de sensores que se deben usar.…”

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“…Debido a esto, se impondrá como restricción fuerte de diseño del sistema de vigilancia, que cada par de estaciones (nodos) que están conectadas directamente entre sí, posean al menos una cámara de vigilancia ubicada en alguna de estas estaciones. Por ejemplo, para las parejas de nodos en el grafo de la Figura 1: (1,2), (1,3), (2,3), (2,4), y (3,5), debe haber al menos una cámara de vigilancia localizada en una de las estaciones de cada uno de estos pares. Suponiendo que se desea construir la red de vigilancia para el sistema mostrado en la Figura 1 y se cuenta con presupuesto para instalar 3 cámaras, es posible encontrar varias soluciones al problema.…”

Section: Introductionunclassified

Modelos de localización de cámaras de vigilancia en una red de transporte público masivo

2017

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ResumenEste artículo estudia el problema de localización de cámaras de vigilancia aplicado a una red de transporte público masivo. Se considera una red de estaciones conectadas entre sí, mediante rutas predeterminadas de buses. El problema estudiado consiste en escoger las estaciones que deben ser vigiladas mediante cámaras con el fin de optimizar simultáneamente dos objetivos: El valor esperado del número de crímenes detectados por las cámaras, y la calidad de las imágenes captadas por el sistema de vigilancia completo. Se formulan dos modelos de optimización basados en programación entera para este problema considerando múltiples períodos, restricciones de presupuesto y restricciones de conectividad donde se busca garantizar que al menos se cuente con una cámara de vigilancia por cada pareja de estaciones conectadas directamente. Se realiza una comparación

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“…Rajagopalan, Saydam and Xiao [34] considered a multiperiod set covering location model in the field of application of emergency medical services. Dell'Olmo et al [16] tackled the optimal location of intersection safety cameras on an urban traffic network to minimize the impact of car accidents, through a multiperiod variant of maximal covering location.…”

Section: Introductionmentioning

confidence: 99%

The multimode covering location problem

Colombo

Cordone

Lulli

2016

Computers & Operations Research

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In this paper we introduce the Multimode Covering Location Problem. This is a generalization of the Maximal Covering Location Problem that consists in locating a given number of facilities of different types with a limitation on the number of facilities sharing the same site.The problem is challenging and intrinsically much harder than its basic version. Nevertheless, it admits a constant factor approximation guarantee, which can be achieved combining two greedy algorithms. To improve the greedy solutions, we have developed a Variable Neighborhood Search approach, based on an exponential-size neighborhood. This algorithm computes good quality solutions in short computational time. The viability of the approach here proposed is also corroborated by a comparison with a Heuristic Concentration algorithm, which is presently the most effective approach to solve large instances of the Maximal Covering Location Problem.

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A Multiperiod Maximal Covering Location Model for the Optimal Location of Intersection Safety Cameras on an Urban Traffic Network

Cited by 27 publications

References 11 publications

Dynamic capacitated maximal covering location problem by considering dynamic capacity

Dynamic capacitated maximal covering location problem by considering dynamic capacity

Modelos de localización de cámaras de vigilancia en una red de transporte público masivo

The multimode covering location problem

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