Documents disponibles écrits par cet auteur

A discrete competitive facility location model with variable attractiveness / H. Küçükaydin in Journal of the operational research society (JORS), Vol. 62 N° 9 (Septembre 2011) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 62 N° 9 (Septembre 2011) . - pp. 1726–1741 Titre : A discrete competitive facility location model with variable attractiveness Type de document : texte imprimé Auteurs : H. Küçükaydin, Auteur ; N. Aras, Auteur ; Altinel, I. K., Auteur Année de publication : 2011 Article en page(s) : pp. 1726–1741 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Competitive  facility  location  Variable  facility  attractiveness  Mixed-integer  nonlinear  programming  Lagrangean  heuristic  Branch-and-bound Index. décimale : 001.424 Résumé : We consider the discrete version of the competitive facility location problem in which new facilities have to be located by a new market entrant firm to compete against already existing facilities that may belong to one or more competitors. The demand is assumed to be aggregated at certain points in the plane and the new facilities can be located at predetermined candidate sites. We employ Huff's gravity-based rule in modelling the behaviour of the customers where the probability that customers at a demand point patronize a certain facility is proportional to the facility attractiveness and inversely proportional to the distance between the facility site and demand point. The objective of the firm is to determine the locations of the new facilities and their attractiveness levels so as to maximize the profit, which is calculated as the revenue from the customers less the fixed cost of opening the facilities and variable cost of setting their attractiveness levels. We formulate a mixed-integer nonlinear programming model for this problem and propose three methods for its solution: a Lagrangean heuristic, a branch-and-bound method with Lagrangean relaxation, and another branch-and-bound method with nonlinear programming relaxation. Computational results obtained on a set of randomly generated instances show that the last method outperforms the others in terms of accuracy and efficiency and can provide an optimal solution in a reasonable amount of time. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n9/abs/jors2010136a.html [article] A discrete competitive facility location model with variable attractiveness [texte imprimé] / H. Küçükaydin, Auteur ; N. Aras, Auteur ; Altinel, I. K., Auteur . - 2011 . - pp. 1726–1741. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 62 N° 9 (Septembre 2011) . - pp. 1726–1741 Mots-clés : Competitive  facility  location  Variable  facility  attractiveness  Mixed-integer  nonlinear  programming  Lagrangean  heuristic  Branch-and-bound Index. décimale : 001.424 Résumé : We consider the discrete version of the competitive facility location problem in which new facilities have to be located by a new market entrant firm to compete against already existing facilities that may belong to one or more competitors. The demand is assumed to be aggregated at certain points in the plane and the new facilities can be located at predetermined candidate sites. We employ Huff's gravity-based rule in modelling the behaviour of the customers where the probability that customers at a demand point patronize a certain facility is proportional to the facility attractiveness and inversely proportional to the distance between the facility site and demand point. The objective of the firm is to determine the locations of the new facilities and their attractiveness levels so as to maximize the profit, which is calculated as the revenue from the customers less the fixed cost of opening the facilities and variable cost of setting their attractiveness levels. We formulate a mixed-integer nonlinear programming model for this problem and propose three methods for its solution: a Lagrangean heuristic, a branch-and-bound method with Lagrangean relaxation, and another branch-and-bound method with nonlinear programming relaxation. Computational results obtained on a set of randomly generated instances show that the last method outperforms the others in terms of accuracy and efficiency and can provide an optimal solution in a reasonable amount of time. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n9/abs/jors2010136a.html

Optimal placement, scheduling, and routing to maximize lifetime in sensor networks / Y. B. Türkoullari in Journal of the operational research society (JORS), Vol. 61 N° 6 (Juin 2010) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 61 N° 6 (Juin 2010) . - pp. 1000–1012 Titre : Optimal placement, scheduling, and routing to maximize lifetime in sensor networks Type de document : texte imprimé Auteurs : Y. B. Türkoullari, Auteur ; N. Aras, Auteur ; Altinel, I. K., Auteur Année de publication : 2011 Article en page(s) : pp. 1000–1012 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Sensor  networks  Heuristics  Integer  programming  Telecommunications Index. décimale : 001.424 Résumé : A wireless sensor network is a network consisting of distributed autonomous electronic devices called sensors. Sensors have limited energy and capability for sensing, data processing, and communicating, but they can collectively behave to provide an effective network that monitors an area and transmit information to gateway nodes or sinks, either directly or through other sensor nodes. In most applications the network must operate for long periods of time, so the available energy resources of the sensors must be managed efficiently. In this paper, we first develop a mixed integer linear programming model to maximize network lifetime by optimally determining locations of sensors and sinks, activity schedules of deployed sensors, and data flow routes from sensors to sinks over a finite planning horizon subject to coverage, flow conservation, energy consumption, and budget constraints. Unfortunately, it is difficult to solve this model exactly even for small instances. Therefore, we propose two approximate solution methods: a Lagrangean heuristic and a two-stage heuristic in which sensors are deployed and an activity schedule is found in the first stage, whereas sinks are located and sensor-to-sink data flow routes are determined in the second stage. Computational experiments performed on various test instances indicate that the Lagrangean heuristic is both efficient and accurate and also outperforms the two-stage heuristic. DEWEY : 001.424 ISSN : 0361-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n6/abs/jors2008187a.html [article] Optimal placement, scheduling, and routing to maximize lifetime in sensor networks [texte imprimé] / Y. B. Türkoullari, Auteur ; N. Aras, Auteur ; Altinel, I. K., Auteur . - 2011 . - pp. 1000–1012. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 61 N° 6 (Juin 2010) . - pp. 1000–1012 Mots-clés : Sensor  networks  Heuristics  Integer  programming  Telecommunications Index. décimale : 001.424 Résumé : A wireless sensor network is a network consisting of distributed autonomous electronic devices called sensors. Sensors have limited energy and capability for sensing, data processing, and communicating, but they can collectively behave to provide an effective network that monitors an area and transmit information to gateway nodes or sinks, either directly or through other sensor nodes. In most applications the network must operate for long periods of time, so the available energy resources of the sensors must be managed efficiently. In this paper, we first develop a mixed integer linear programming model to maximize network lifetime by optimally determining locations of sensors and sinks, activity schedules of deployed sensors, and data flow routes from sensors to sinks over a finite planning horizon subject to coverage, flow conservation, energy consumption, and budget constraints. Unfortunately, it is difficult to solve this model exactly even for small instances. Therefore, we propose two approximate solution methods: a Lagrangean heuristic and a two-stage heuristic in which sensors are deployed and an activity schedule is found in the first stage, whereas sinks are located and sensor-to-sink data flow routes are determined in the second stage. Computational experiments performed on various test instances indicate that the Lagrangean heuristic is both efficient and accurate and also outperforms the two-stage heuristic. DEWEY : 001.424 ISSN : 0361-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n6/abs/jors2008187a.html

Variable neighbourhood search heuristics for the probabilistic multi-source Weber problem / Altinel, I. K. in Journal of the operational research society (JORS), Vol. 62 N° 10 (Octobre 2011) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 62 N° 10 (Octobre 2011) . - pp. 1813–1826 Titre : Variable neighbourhood search heuristics for the probabilistic multi-source Weber problem Type de document : texte imprimé Auteurs : Altinel, I. K., Auteur ; N. Aras, Auteur ; Özkisacik, K. C., Auteur Année de publication : 2011 Article en page(s) : pp. 1813–1826 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Facility  location-allocation  Variable  neighbourhood  search  Probabilistic  Weber  problem  Heuristics Index. décimale : 001.424 Résumé : The Multi-source Weber Problem (MWP) is concerned with locating m facilities in the Euclidean plane, and allocating these facilities to n customers at minimum total cost. The deterministic version of the problem, which assumes that customer locations and demands are known with certainty, is a non-convex optimization problem and difficult to solve. In this work, we focus on a probabilistic extension and consider the situation where customer locations are randomly distributed according to a bivariate distribution. We first present a mathematical programming formulation for the probabilistic MWP called the PMWP. For its solution, we propose two heuristics based on variable neighbourhood search (VNS). Computational results obtained on a number of test instances show that the VNS heuristics improve the performance of a probabilistic alternate location-allocation heuristic referred to as PALA. In its original form, the applicability of the new heuristics depends on the existence of a closed-form expression for the expected distances between facilities and customers. Unfortunately, such an expression exists only for a few distance function and probability distribution combinations. We therefore use two approximation methods for the expected distances, which make the VNS heuristics applicable for any distance function and bivariate distribution of customer locations. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n10/abs/jors2010159a.html [article] Variable neighbourhood search heuristics for the probabilistic multi-source Weber problem [texte imprimé] / Altinel, I. K., Auteur ; N. Aras, Auteur ; Özkisacik, K. C., Auteur . - 2011 . - pp. 1813–1826. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 62 N° 10 (Octobre 2011) . - pp. 1813–1826 Mots-clés : Facility  location-allocation  Variable  neighbourhood  search  Probabilistic  Weber  problem  Heuristics Index. décimale : 001.424 Résumé : The Multi-source Weber Problem (MWP) is concerned with locating m facilities in the Euclidean plane, and allocating these facilities to n customers at minimum total cost. The deterministic version of the problem, which assumes that customer locations and demands are known with certainty, is a non-convex optimization problem and difficult to solve. In this work, we focus on a probabilistic extension and consider the situation where customer locations are randomly distributed according to a bivariate distribution. We first present a mathematical programming formulation for the probabilistic MWP called the PMWP. For its solution, we propose two heuristics based on variable neighbourhood search (VNS). Computational results obtained on a number of test instances show that the VNS heuristics improve the performance of a probabilistic alternate location-allocation heuristic referred to as PALA. In its original form, the applicability of the new heuristics depends on the existence of a closed-form expression for the expected distances between facilities and customers. Unfortunately, such an expression exists only for a few distance function and probability distribution combinations. We therefore use two approximation methods for the expected distances, which make the VNS heuristics applicable for any distance function and bivariate distribution of customer locations. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n10/abs/jors2010159a.html