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Description: This diploma thesis deals with the restoration problem in telecommunication networks. The goal is to find a cost minimal capacity capacity assignment on the edges and nodes of a network such that given demands can be satisfied even in case of the failure of an edge or node in the network. Moreover, restrictions on the routing paths (like length restrictions) and hardware constraints have to be satisfied. A Mixed Integer Programming model is presented which takes into account restoration requirements as well as hardware constraints and which abstracts from a particular restoration protocol and failure situation. This abstraction provides new insight into the structure of the network restoration problem and shows that from a mathematical point of view, the commonly used restoration techniques Link Restoration, Path Restoration and Reservation are not as different as they seem to be from a practical point of view. In addition, our model allows (but is not limited to) optimizing working capacity, intended for normal use, and spare capacity, intended for rerouting purposes in case of a failure, in one step. Furthermore, our formulation of capacity cost allows taking into account the effects of discrete, non-linear cost structures which are common in practice. Up to our knowledge, no publication in the existing literature covers all these aspects, let alone in one model, although they are of major practical interest. The model has been implemented in a Branch and Cut framework. The theoretical background of the algorithmic procedure is presented in detail, including computational complexity investigations on the pricing problem. The abstraction from a particular restoration protocol turns out to be useful both from a theoretical and computational point of view. In fact, our investigations suggest a distinction into Local Restoration and Global Restoration rather than into Link Restoration,Path Restoration, Reservation and mixtures of these concepts. In addition to the theoretical aspects of the algorithmic procedure, some implementational details are briefly discussed. Our implementation has been tested on 14 real world instances, which is described in detail. One part of the computational results consists of a comparison of optimal network cost values using diffeent restoration mechanisms, applied to securing either all single node failures, all single edge failures or both. In addition, the effects of a discrete cost structure are investigated, which has rarely been considered yet in literature. Furthermore, the cost ifference between joint and successive working and spare capacity optimization is investigated. In the second part of the computational results, several heuristics for the network restoration problem are compared with respect to both solution quality and time. This diploma thesis deals with the restoration problem in telecommunication networks. The goal is to find a cost minimal capacity capacity assignment on the edges and nodes of a network such that given demands can be satisfied even in case of the failure of an edge or node in the network. Moreover, restrictions on the routing paths (like length restrictions) and hardware constraints have to be satisfied. A Mixed Integer Programming model is presented which takes into account restoration requirements as well as hardware constraints and which abstracts from a particular restoration protocol and failure situation. This abstraction provides new insight into the structure of the network restoration problem and shows that from a mathematical point of view, the commonly used restoration techniques Link Restoration, Path Restoration and Reservation are not as different as they seem to be from a practical point of view. In addition, our model allows (but is not limited to) optimizing working capacity, intended for normal use, and spare capacity, intended for rerouting purposes in case of a failure, in one step. Furthermore, our formulation of capacity cost allows taking into account the effects of discrete, non-linear cost structures which are common in practice. Up to our knowledge, no publication in the existing literature covers all these aspects, let alone in one model, although they are of major practical interest. The model has been implemented in a Branch and Cut framework. The theoretical background of the algorithmic procedure is presented in detail, including computational complexity investigations on the pricing problem. The abstraction from a particular restoration protocol turns out to be useful both from a theoretical and computational point of view. In fact, our investigations suggest a distinction into Local Restoration and Global Restoration rather than into Link Restoration, Path Restoration, Reservation and mixtures of these concepts. In addition to the theoretical aspects of the algorithmic procedure, some implementational details are briefly discussed. Our implementation has been tested on 14 real world instances, which is described in detail. One part of the computational results consists of a comparison of optimal network cost values using different restoration mechanisms, applied to securing either all single node failures, all single edge failures or both. In addition, the effects of a discrete cost structure are investigated, which has rarely been considered yet in literature. Furthermore, the cost difference between joint and successive working and spare capacity optimization is investigated. In the second part of the computational results, several heuristics for the network restoration problem are compared with respect to both solution quality and time.

Description: In der vorliegenden Dissertation untersuchen wir die Optimierung von ausfallsicheren Telekommunikationsnetzwerken. Wir präsentieren unterschiedliche gemischt-ganzzahlige Modelle für die diskrete Kapazitätsttruktu,, sowie für die Sicherung des Netzes gegen den Ausfall einzelner Komponenten. Die Modelle wurden in einer Kooperation mit der E-Plus Mobilfunk GmbH verwendet. Die theoretischen Resultate wurden in Algorithmen umgesetzt und in das von uns entiickllte Netzwerksoptimierungswerkzeug Discnet (Dimensioning Survivable Capaiitated NETworks) integriert, welches seit mehreren Jahren in der Planung bei E-Plus eingesetzt wird. Wir betrachten das Transportnetzllanungsproblem eines Telekommunikationsanbieters. Dieses Problem setzt auf logischen Kommunikattonsanforerrungen zwischen den Standorten (Knoten) des zu planenden Netzes und potentiell inslallirrbaren Verbindungen (Kanten) zwischen derselben Knotenmenge auf. Ein Kapazitätsmodell stellt die Information bereit, welche Kapazitäten auf den potentiellen Kanten verfügbar sind. Wir betrachten zwei Modelle. Entweder ist eine explizite Liste der verfügbaren Kapazittten gegeben oder eine Menge von sogenannten Basiskapazitäten, die auf jeder Kante indiviuelll kombiniert werden können. Die Basiskapazitäten müßen paarweise ganzzahlige Vielfache voneinander sein. Man beachte, daß diese Eigenschaft von den internationalen Standards PDH und SDH erfüllt wiid. Ein Ausfallsicherheitsmodell stellt die Information bereit, wie das zu planende Netz gegen den Ausfall einzelner Netzkomponenten geschützt werden soll. Wir betrachten sinnvolle Kombinationen der Modelle Diversification, Reservation und Path Restoration. Das erste Modell garantiert Ausfallsicherheit durch kommunikationsbedarfsabhängige Beschränkung des Prozentsatzes, der durch einzelne Netzkomponenten geroutet werden darf. Bei den beiden anderen Modelle können Kommunikationsbedarfe bei Ausfall einer Netzkomponente auf unterschiedliche Weise neu geroutet werden. Ziel der Planung ist eine ktstenminimlle Kapatitätsentscheidung, die eine Routenllanung aller Kommunikationsbedarfe gemäß den Ausfallsicherheitsanforderungen ermöglicht. Wir entwickeln ein Schnittebenenverfahren zur Lösung der betrachteten Optimiergngsrrobleme. Zu diesem Zweck untersuchen wir Polyeder, die mit den verschiedenen Problemen assoziiert sind. Wir präsentieren neue Klassen von Ungleichungen, entwickeln Separationsalgorithmen und Heuristiken. Mit dem Schnittebenenverfahren werden untere und obere Schranken für den Wert von Oitimallösungen berechnet, und daher ist es möglich, Qualitätsgarantien für die berechneten Löungen anzugeben. Parallel zur Beschreibung der implementierten Algorithmen präsentieren wir umfangreiche Tests mit praktisch relevanten Daten, die zu Problemen mit mehr als 2 Billionen Variablen führen.