Bilevel facility location problems: theory and applications.

In this doctoral thesis we focus on studying facility location problems considering customer preferences. In these problems, there is a set of customers or users who demand a service or product that must be supplied by one or more facilities. By facilities it is understood some object or structure that offers some service to customers. One of the most important assumptions is that customers have established their own preferences over the facilities and should be taken into account in the customer-facility assignment. In real life, customers choose facilities based on costs, preferences, a predetermined contract, or a loyalty coefficient, among others. That is, they are free to choose the facilities that will serve them. The situation described above is commonly modeled by bilevel programming, where the upper level corresponds to location decisions to optimize a predefined criteria, such as, minimize location and distribution costs or maximize the demand covered by the facilities; and the lower level is associated to -customer allocation- to optimize customer preferences. The hierarchy among both levels is justified because the decision taken in the upper level directly affects the decision’s space in the lower level

Un modelo bi-objetivo de localización de hubs considerando costos y cobertura

Tesis (Magíster en Ciencias de la Ingeniería mención Logística y Gestión de Operaciones)La localización de hubs busca determinar las mejores ubicaciones para este tipo de instalación. La red resultante es llamada hub-and-spoke, y permite conectar a múltiples pares origen-destino. Esta investigación se enfoca en definir y formular un nuevo modelo de localización hub para un problema de maximización de cobertura, definida por un tiempo límite de cobertura; y a su vez minimizar los costos totales. El modelo fue resuelto de forma exacta a través del software AMPL, utilizando el método NISE (Non-Inferior Set Estimation) para ver las curvas trade-off entre ambos objetivos. Para esto se definieron diferentes escenarios para analizar la incidencia de diferentes parámetros en las curvas de trade-off obtenidas, utilizando los primeros 40 nodos de la red de Turquía. Se obtuvo que el nivel de servicio es el parámetro determinante del modelo. Además como investigaciones futuras se podría determinar exógenamente la cantidad de hubs a localizar o disminuir los tiempos computacionales para instancias de tamaño considerables

The P-Hub maximal covering problem and extensions for gradual decay functions

Cataloged from PDF version of article.The p-hub maximal covering problem aims to find the best locations for hubs so as to maximize demands within a coverage distance with a predetermined number of hubs. Classically, the problem is defined in the framework of binary coverage only; an origin-destination pair is covered if the cost (time, etc.) is lower than the critical value, and not covered at all if the cost is greater than the critical value. In this paper, we extend the definition of coverage, introducing "partial coverage", which changes with distance. We present new and efficient mixed-integer programming models that are also valid for partial coverage for single and multiple allocations. We present and discuss the computational results with different data sets. (C) 2015 Elsevier Ltd. All rights reserved

Un problema de diseño de redes multi-período con capacidad y congestión en los hubs

Tesis (Magíster en Ciencias de la Ingeniería, mención Logística y Gestión de Operaciones)El diseño de redes es un área amplia dentro de Investigación de Operaciones, que busca construir redes para satisfacer algún tipo de demanda. Un área activa corresponde al diseño de redes hub-and-spoke, que usan instalaciones especiales para consolidar flujos, comúnmente llamadas hubs. En la literatura se han estudiado problemas de diseño de redes con múltiples períodos de planificación, congestión en los nodos, o con múltiples objetivos, pero no con todas estas características simultáneamente. La contribución de este trabajo es la formulación y resolución exacta para instancias de tamaño pequeño de un problema de diseño de redes hub-and-spoke. Nuestro modelo busca simultáneamente maximizar la demanda captura y minimizar los costos totales de la red, para un horizonte temporal discreto y finito. Se usan funciones lineales por tramos en el flujo entrante a los hubs para modelar la congestión en ellos. Se estudia el impacto de la congestión, capacidad en los hubs y la longitud del horizonte temporal. Los experimentos computacionales demuestran que el modelo propuesto logra representar la congestión dentro de una red de múltiples períodos de tiempo, ya que, al aumentar la demanda, los hubs fueron mostrando una saturación creciente al ir aumentando la demanda y junto a esto, los tiempos de viaje promedio también fueron mostrando un aumento en cada período de tiempo, indicación clara de la saturación de la red. Como trabajo futuro se propone encontrar la ubicación óptima de los hubs dentro de la red mediante un modelo de localización, al igual que considerar costos fijos al momento de habilitarlos y la existencia de capacidad en los arcos de la red, todo esto con el fin de complementar el modelo propuesto

P-hub maximal covering problem and extensions for gradual decay functions

Ankara : The Department of IndustrialEngineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Master's) -- Bilkent University, 2013.Includes bibliographical references leaves 71-75.Hubs are special facilities that serve as switching, transshipment and sorting nodes in many to many distribution systems. The hub location problem deals with the selection of the locations of hub facilities and finding assignments of demand nodes to hubs simultaneously. The p-hub maximal covering problem, that is one of the variations of the hub location problems, aims to find locations of hubs so as to maximize the covered demand that are within the coverage distance with a predetermined number of hubs. In the literature of hub location, p-hub maximal covering problem is conducted in the framework of only binary coverage; origin-destination pairs are covered if the total path length is less than coverage distance and not covered at all if the path length exceeds the coverage distance. Throughout this thesis, we extend the definition of coverage and introduce “partial coverage” that changes with the distance, to the hub location literature. In this thesis, we study the p-hub maximal covering problem for single and multiple allocations and provide new formulations that are also valid for partial coverage. The problems are proved to be NP-Hard. We even show that assignment problem with a given set of hubs for the single allocation version of the problem is also NP-Hard. Computational results for all the proposed formulations with different data sets are presented and discussed.Peker, MeltemM.S

Model and solution methods for some hub location problems

In this thesis we study some hub location problems in the context of transportation networks. These are combinatorial optimization problems appearing in situations where there is a need of transporting some traffic, like items, people, and information, from many origins to many destinations. Instead of sending these flows using a direct shipment between all pairs of nodes in the network, a subset of these nodes is selected to use as hubs, with the aim of consolidating and distribute the flows. Thus, hubs induce a subnetwork that sends the traffic more efficiently and at a cheaper cost, allowing economies of scale when large amounts of traffic between nodes on this subnet are transported. We study different variants of hub location problems that try to model several real world situations and characteristics. In all of them, we aim to minimize the cost of sending traffic through the transportation network.In this thesis we study some hub location problems in the context of transportation networks. These are combinatorial optimization problems appearing in situations where there is a need of transporting some traffic, like items, people, and information, from many origins to many destinations. Instead of sending these flows using a direct shipment between all pairs of nodes in the network, a subset of these nodes is selected to use as hubs, with the aim of consolidating and distribute the flows. Thus, hubs induce a subnetwork that sends the traffic more efficiently and at a cheaper cost, allowing economies of scale when large amounts of traffic between nodes on this subnet are transported. We study different variants of hub location problems that try to model several real world situations and characteristics. In all of them, we aim to minimize the cost of sending traffic through the transportation network

El problema de localización de hubs con flota limitada

Tesis (Ingeniero Industrial)En los modelos matemáticos de localización de hubs en donde se busca maximizar la cobertura no considera una flota limitada, tanto en capacidad como en cantidad, o bien, un presupuesto limitado. Pero, ¿Cómo solucionar los problemas de cobertura en organizaciones que no cuentan con una flota, o un presupuesto grande? Se podría considerar minimizar la distancia de cada viaje, esto involucra realizar transbordos de vehículos, pero realizar esta acción, la gran mayoría de las veces, incrementa los costos. Es por esto que, en la siguiente investigación, se propone un nuevo modelo que podrá ser muy útil para organizaciones de distribución en la toma decisiones, sobre cómo realizar la distribución de sus centros logísticos, cuantos vehículos necesitará y cuantos clientes logrará atender con el presupuesto disponible

The P-Hub maximal covering problem and extensions for gradual decay functions

The p-hub maximal covering problem aims to find the best locations for hubs so as to maximize demands within a coverage distance with a predetermined number of hubs. Classically, the problem is defined in the framework of binary coverage only; an origin-destination pair is covered if the cost (time, etc.) is lower than the critical value, and not covered at all if the cost is greater than the critical value. In this paper, we extend the definition of coverage, introducing "partial coverage", which changes with distance. We present new and efficient mixed-integer programming models that are also valid for partial coverage for single and multiple allocations. We present and discuss the computational results with different data sets. © 2015 Elsevier Ltd