TY - JOUR A1 - Schweiger, Jonas A1 - Liers, Frauke T1 - A Decomposition Approach for Optimal Gas Network Extension with a Finite Set of Demand Scenarios JF - Optimization and Engineering N2 - Today's gas markets demand more flexibility from the network operators which in turn have to invest into their network infrastructure. As these investments are very cost-intensive and long-living, network extensions should not only focus on a single bottleneck scenario, but should increase the flexibility to fulfill different demand scenarios. In this work, we formulate a model for the network extension problem for multiple demand scenarios and propose a scenario decomposition in order to solve the arising challenging optimization tasks. In fact, each subproblem consists of a mixed-integer nonlinear optimization problem (MINLP). Valid bounds on the objective value are derived even without solving the subproblems to optimality. Furthermore, we develop heuristics that prove capable of improving the initial solutions substantially. Results of computational experiments on realistic network topologies are presented. It turns out that our method is able to solve these challenging instances to optimality within a reasonable amount of time. Y1 - 2018 VL - 19 IS - 2 SP - 297 EP - 326 PB - Springer ER - TY - THES A1 - Schweiger, Jonas T1 - Application of Multistage Stochastic Programming in Strategic Telecommunication Network Planning N2 - Telecommunication is fundamental for the information society. In both, the private and the professional sector, mobile communication is nowadays taken for granted. Starting primarily as a service for speech communication, data service and mobile Internet access are now driving the evolution of network infrastructure. In the year 2009, 19 million users generated over 33 million GB of traffic using mobile data services. The 3rd generation networks (3G or UMTS) in Germany comprises over 39,000 base stations with some 120,000 cells. From 1998 to 2008, the four network operators in Germany invested over 33 billion Euros in their infrastructure. A careful allocation of the resources is thus crucial for the profitability for a network operator: a network should be dimensioned to match customers demand. As this demand evolves over time, the infrastructure has to evolve accordingly. The demand evolution is hard to predict and thus constitutes a strong source of uncertainty. Strategic network planning has to take this uncertainty into account, and the planned network evolution should adapt to changing market conditions. The application of superior planning methods under the consideration of uncertainty can improve the profitability of the network and creates a competitive advantage. Multistage stochastic programming is a suitable framework to model strategic telecommunication network planning. We present mathematical models and effective optimization procedures for strategic cellular network design. The demand evolution is modeled as a continuous stochastic process which is approximated by a discrete scenario tree. A tree-stage approach is used for the construction of non-uniform scenario trees that serve as input of the stochastic program. The model is calibrated by historical traffic observations. A realistic system model of UMTS radio cells is used that determines coverage areas and cell capacities and takes signal propagation and interferences into account. The network design problem is formulated as a multistage stochastic mixed integer linear program, which is solved using state-of-the-art commercial MIP solvers. Problem specific presolving is proposed to reduce the problem size. Computational results on realistic data is presented. Optimization for the expected profit and the conditional value at risk are performed and compared. Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-12206 ER - TY - GEN A1 - Schweiger, Jonas T1 - Gas network extension planning for multiple demand scenarios N2 - Today's gas markets demand more flexibility from the network operators which in turn have to invest into their network infrastructure. As these investments are very cost-intensive and long-living, network extensions should not only focus on one bottleneck scenario, but should increase the flexibility to fulfill different demand scenarios. We formulate a model for the network extension problem for multiple demand scenarios and propose a scenario decomposition. We solve MINLP single-scenario sub-problems and obtain valid bounds even without solving them to optimality. Heuristics prove capable of improving the initial solutions substantially. Results of computational experiments are presented. T3 - ZIB-Report - 14-29 KW - Gas Network Planning KW - Scenario Decomposition Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-51030 SN - 1438-0064 ER - TY - THES A1 - Schweiger, Jonas T1 - Exploiting structure in non-convex quadratic optimization and gas network planning under uncertainty N2 - The amazing success of computational mathematical optimization over the last decades has been driven more by insights into mathematical structures than by the advance of computing technology. In this vein, we address applications, where nonconvexity in the model and uncertainty in the data pose principal difficulties. The first part of the thesis deals with non-convex quadratic programs. Branch&Bound methods for this problem class depend on tight relaxations. We contribute in several ways: First, we establish a new way to handle missing linearization variables in the well-known Reformulation-Linearization-Technique (RLT). This is implemented into the commercial software CPLEX. Second, we study the optimization of a quadratic objective over the standard simplex or a knapsack constraint. These basic structures appear as part of many complex models. Exploiting connections to the maximum clique problem and RLT, we derive new valid inequalities. Using exact and heuristic separation methods, we demonstrate the impact of the new inequalities on the relaxation and the global optimization of these problems. Third, we strengthen the state-of-the-art relaxation for the pooling problem, a well-known non-convex quadratic problem, which is, for example, relevant in the petrochemical industry. We propose a novel relaxation that captures the essential non-convex structure of the problem but is small enough for an in-depth study. We provide a complete inner description in terms of the extreme points as well as an outer description in terms of inequalities defining its convex hull (which is not a polyhedron). We show that the resulting valid convex inequalities significantly strengthen the standard relaxation of the pooling problem. The second part of this thesis focuses on a common challenge in real world applications, namely, the uncertainty entailed in the input data. We study the extension of a gas transport network, e.g., from our project partner Open Grid Europe GmbH. For a single scenario this maps to a challenging non-convex MINLP. As the future transport patterns are highly uncertain, we propose a robust model to best prepare the network operator for an array of scenarios. We develop a custom decomposition approach that makes use of the hierarchical structure of network extensions and the loose coupling between the scenarios. The algorithm used the single-scenario problem as black-box subproblem allowing the generalization of our approach to problems with the same structure. The scenario-expanded version of this problem is out of reach for today's general-purpose MINLP solvers. Yet our approach provides primal and dual bounds for instances with up to 256 scenarios and solves many of them to optimality. Extensive computational studies show the impact of our work. N2 - Der bemerkenswerte Erfolg der angewandten mathematischen Optimierung in den letzten Dekaden ist mehr auf Einsichten in mathematische Strukturen zurückzuführen, als auf eine Steigerung der Rechenleistung. In diesem Sinne adressieren wir Anwendungen, in denen Nichtkonvexität und Unsicherheit in den Daten die Hauptschwierigkeiten darstellen. Der erste Teil dieser Arbeit beschäftigt sich mit nichtkonvexen quadratischen Optimierungsproblemen. Relaxierungen sind integraler Bestandteil von \BranchAndBound{}-Lösungsmethoden für diese Problemkategorie. Wir leisten folgende Beiträge: Erstens beschreiben wir eine neue Art fehlende Linearisierungsvariablen, in der so genannten Reformulation-Linearization-Technique (RLT), zu behandeln. Diese wird inzwischen in der kommerziellen Software CPLEX verwendet. Zweitens beschäftigen wir uns mit der Optimierung einer quadratischen Zielfunktion über die Standardsimplex oder einen so genannten Knapsack-Constraint. Solche grundlegenden Strukturen sind Teil vieler komplexer Modelle. Wir benutzen bekannte Verbindungen zum maximalen Cliquenproblem sowie zu RLT, um neue gültige Ungleichungen herzuleiten, die die Relaxierung verstärken. Drittens beschäftigen wir uns mit dem Pooling Problem, das z.B. in der Erdölindustrie relevant ist. Wie leiten eine neue Relaxierung her, die die wesentliche nicht-konvexe Struktur des Problems erfasst, aber klein genug für eine grundlegende Untersuchung ist. Wir geben eine innere Beschreibung in Form der Extrempunkte, sowie eine äußere Beschreibung in Form von Ungleichungen, die die konvexe Hülle (welche im Allgemeinen kein Polyeder ist) beschreiben, an. Wir zeigen, dass neuen die Ungleichungen die Relaxierung des Pooling Problems erheblich verstärken. Der zweite Teil der Arbeit befasst sich mit einer weiteren Herausforderung in realen Anwendungen, nämlich Unsicherheit in den Eingabedaten. Konkret untersuchen wir die Optimierung des Ausbaus eines Gastransportnetzes, wie z.B. von unserem Projektpartner Open Grid Europe GmbH. Dieses Problem ist bereits bei gegebenen Eingabedaten ein schweres nicht-konvexes gemischt-ganzzahliges Optimierungsproblem. Da zukünftige Nutzungsmuster des Netzes mit großer Unsicherheit behaftet sind, beschreiben wir ein robustes Modell, um den Netzbetreiber gegen verschiedene Szenarien abzusichern. Wir entwickeln einen speziellen Dekompositionsalgorithmus unter Berücksichtigung der hierarchischen Struktur der Ausbauten und der schwachen Kopplung zwischen den Szenarien. Unser Ansatz liefert primale und duale Schranken für Instanzen mit bis zu 256 Szenarien und löst viele beweisbar optimal. Umfangreiche Rechnungen bestätigen die Effizient der vorgestellten Methoden. KW - Nonconvexity KW - Uncertainty KW - Quadratic Programming KW - Relaxations KW - Cutting Planes KW - Standard Quadratic Programming KW - Pooling Problem KW - Gas Network Planning KW - Robust Optimization KW - Decomposition KW - Computations Y1 - 2017 UR - http://dx.doi.org/10.14279/depositonce-6015 ER - TY - GEN A1 - Schweiger, Jonas T1 - Exploiting structure in non-convex quadratic optimization N2 - The amazing success of computational mathematical optimization over the last decades has been driven more by insights into mathematical structures than by the advance of computing technology. In this vein, we address applications, where nonconvexity in the model poses principal difficulties. This paper summarizes the dissertation of Jonas Schweiger for the occasion of the GOR dissertation award 2018. We focus on the work on non-convex quadratic programs and show how problem specific structure can be used to obtain tight relaxations and speed up Branch&Bound methods. Both a classic general QP and the Pooling Problem as an important practical application serve as showcases. T3 - ZIB-Report - 18-35 KW - Nonconvexity KW - Quadratic Programming KW - Relaxations KW - Cutting Planes KW - Standard Quadratic Programming KW - Pooling Problem Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-69476 SN - 1438-0064 ER - TY - JOUR A1 - Pfetsch, Marc A1 - Fügenschuh, Armin A1 - Geißler, Björn A1 - Geißler, Nina A1 - Gollmer, Ralf A1 - Hiller, Benjamin A1 - Humpola, Jesco A1 - Koch, Thorsten A1 - Lehmann, Thomas A1 - Martin, Alexander A1 - Morsi, Antonio A1 - Rövekamp, Jessica A1 - Schewe, Lars A1 - Schmidt, Martin A1 - Schultz, Rüdiger A1 - Schwarz, Robert A1 - Schweiger, Jonas A1 - Stangl, Claudia A1 - Steinbach, Marc A1 - Vigerske, Stefan A1 - Willert, Bernhard T1 - Validation of Nominations in Gas Network Optimization: Models, Methods, and Solutions JF - Optimization Methods and Software N2 - In this article we investigate methods to solve a fundamental task in gas transportation, namely the validation of nomination problem: Given a gas transmission network consisting of passive pipelines and active, controllable elements and given an amount of gas at every entry and exit point of the network, find operational settings for all active elements such that there exists a network state meeting all physical, technical, and legal constraints. We describe a two-stage approach to solve the resulting complex and numerically difficult feasibility problem. The first phase consists of four distinct algorithms applying linear, and methods for complementarity constraints to compute possible settings for the discrete decisions. The second phase employs a precise continuous programming model of the gas network. Using this setup, we are able to compute high quality solutions to real-world industrial instances that are significantly larger than networks that have appeared in the mathematical programming literature before. Y1 - 2014 U6 - https://doi.org/10.1080/10556788.2014.888426 PB - Taylor & Francis ER - TY - GEN A1 - Pfetsch, Marc A1 - Fügenschuh, Armin A1 - Geißler, Björn A1 - Geißler, Nina A1 - Gollmer, Ralf A1 - Hiller, Benjamin A1 - Humpola, Jesco A1 - Koch, Thorsten A1 - Lehmann, Thomas A1 - Martin, Alexander A1 - Morsi, Antonio A1 - Rövekamp, Jessica A1 - Schewe, Lars A1 - Schmidt, Martin A1 - Schultz, Rüdiger A1 - Schwarz, Robert A1 - Schweiger, Jonas A1 - Stangl, Claudia A1 - Steinbach, Marc A1 - Vigerske, Stefan A1 - Willert, Bernhard T1 - Validation of Nominations in Gas Network Optimization: Models, Methods, and Solutions N2 - In this article we investigate methods to solve a fundamental task in gas transportation, namely the validation of nomination problem: Given a gas transmission network consisting of passive pipelines and active, controllable elements and given an amount of gas at every entry and exit point of the network, find operational settings for all active elements such that there exists a network state meeting all physical, technical, and legal constraints. We describe a two-stage approach to solve the resulting complex and numerically difficult mixed-integer non-convex nonlinear feasibility problem. The first phase consists of four distinct algorithms facilitating mixed-integer linear, mixed-integer nonlinear, reduced nonlinear, and complementarity constrained methods to compute possible settings for the discrete decisions. The second phase employs a precise continuous nonlinear programming model of the gas network. Using this setup, we are able to compute high quality solutions to real-world industrial instances whose size is significantly larger than networks that have appeared in the literature previously. T3 - ZIB-Report - 12-41 Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-16531 SN - 1438-0064 ER - TY - CHAP A1 - Martin, Alexander A1 - Geißler, Björn A1 - Heyn, Christine A1 - Hiller, Benjamin A1 - Humpola, Jesco A1 - Koch, Thorsten A1 - Lehmann, Thomas A1 - Morsi, Antonio A1 - Pfetsch, Marc A1 - Schewe, Lars A1 - Schmidt, Martin A1 - Schultz, Rüdiger A1 - Schwarz, Robert A1 - Schweiger, Jonas A1 - Steinbach, Marc A1 - Willert, Bernhard T1 - Optimierung Technischer Kapazitäten in Gasnetzen T2 - Optimierung in der Energiewirtschaft Y1 - 2011 SP - 105 EP - 114 PB - VDI-Verlag, Düsseldorf ER - TY - GEN A1 - Martin, Alexander A1 - Geißler, Björn A1 - Hayn, Christine A1 - Hiller, Benjamin A1 - Humpola, Jesco A1 - Koch, Thorsten A1 - Lehmann, Thomas A1 - Morsi, Antonio A1 - Pfetsch, Marc A1 - Schewe, Lars A1 - Schmidt, Martin A1 - Schultz, Rüdiger A1 - Schwarz, Robert A1 - Schweiger, Jonas A1 - Steinbach, Marc A1 - Willert, Bernhard T1 - Optimierung Technischer Kapazitäten in Gasnetzen N2 - Die mittel- und längerfristige Planung für den Gastransport hat sich durch Änderungen in den regulatorischen Rahmenbedingungen stark verkompliziert. Kernpunkt ist die Trennung von Gashandel und -transport. Dieser Artikel diskutiert die hieraus resultierenden mathematischen Planungsprobleme, welche als Validierung von Nominierungen und Buchungen, Bestimmung der technischen Kapazität und Topologieplanung bezeichnet werden. Diese mathematischen Optimierungsprobleme werden vorgestellt und Lösungsansätze skizziert. T3 - ZIB-Report - 11-56 KW - Gasnetzplanung KW - Technische Kapazitäten KW - Nominierungsvalidierung KW - Buchungsvalidierung KW - Topologieplanung Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-15121 SN - 1438-0064 ER - TY - GEN A1 - Luedtke, James A1 - D'Ambrosio, Claudia A1 - Linderoth, Jeff A1 - Schweiger, Jonas T1 - Strong Convex Nonlinear Relaxations of the Pooling Problem: Extreme Points N2 - We investigate new convex relaxations for the pooling problem, a classic nonconvex production planning problem in which products are mixed in intermediate pools in order to meet quality targets at their destinations. In this technical report, we characterize the extreme points of the convex hull of our non-convex set, and show that they are not finite, i.e., the convex hull is not polyhedral. This analysis was used to derive valid nonlinear convex inequalities and show that, for a specific case, they characterize the convex hull of our set. The new valid inequalities and computational results are presented in ZIB Report 18-12. T3 - ZIB-Report - 18-13 KW - Pooling Problem KW - Relaxation KW - Extreme Points Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-67801 SN - 1438-0064 ER -