TY - GEN A1 - Steinbach, Marc T1 - General Information Constraints in Stochastic Programs N2 - Scenario tree models of stochastic programs arise naturally under standard nonanticipativity assumptions. We demonstrate how tree-sparse programs cover the general case, with \emph{arbitrary} information constraints. Detailed examples and intuitive interpretations illuminate the basic thoughts behind the abstract but elementary construction. T3 - ZIB-Report - 01-24 KW - information constraints KW - general nonanticipativity KW - sigma-fields KW - filtrations KW - scenarios KW - stochastic programs KW - tree-sparse programs Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6502 ER - TY - GEN A1 - Garrido, Izaskun A1 - Steinbach, Marc T1 - A Multistage Stochastic Programming Approach in Real-Time Process Control N2 - Standard model predictive control for real-time operation of industrial production processes may be inefficient in the presence of substantial uncertainties. To avoid overly conservative disturbance corrections while ensuring safe operation, random influences should be taken into account explicitly. We propose a multistage stochastic programming approach within the model predictive control framework and apply it to a distillation process with a feed tank buffering external sources. A preliminary comparison to a probabilistic constraints approach is given and first computational results for the distillation process are presented. T3 - ZIB-Report - 01-05 KW - Process Control KW - Real-Time Optimization KW - Multistage Stochastic Programming Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6317 ER - TY - GEN A1 - Steinbach, Marc T1 - Tree-Sparse Convex Programs N2 - Dynamic stochastic programs are prototypical for optimization problems with an inherent tree structure inducing characteristic sparsity patterns in the KKT systems of interior methods. We propose an integrated modeling and solution approach for such tree-sparse programs. Three closely related natural formulations are theoretically analyzed from a control-theoretic viewpoint and compared to each other. Associated KKT solution algorithms with linear complexity are developed and comparisons to other interior approaches and related problem formulations are discussed. T3 - ZIB-Report - 01-08 KW - Convex program KW - tree KW - discrete-time optimal control multistage stochastic program KW - recursive factorization KW - local projection Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6340 ER - TY - GEN A1 - Steinbach, Marc T1 - Robust Process Control by Dynamic Stochastic Programming N2 - Unnecessarily conservative behavior of standard process control techniques can be avoided by stochastic programming models when the distribution of random disturbances is known. In an earlier study we have investigated such an approach for tank level constraints of a distillation process. Here we address techniques that have accelerated the numerical solution of the large and expensive stochastic programs by a factor of six, and then present a refined optimization model for the same application. T3 - ZIB-Report - 04-20 KW - Process control KW - random disturbance KW - multistage stochastic program KW - relatively complete recourse KW - tree-sparse NLP Y1 - 2004 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-7959 ER - TY - GEN A1 - Steinbach, Marc T1 - Markowitz Revisited: Single-Period and Multi-Period Mean-Variance Models N2 - Mean-variance portfolio analysis provided the first quantitative treatment of the tradeoff between profit and risk. We investigate in detail the interplay between objective and constraints in a number of single-period variants, including semi-variance models. Particular emphasis is laid on avoiding the penalization of overperformance. The results are then used as building blocks in the development and theoretical analysis of multi-period models based on scenario trees. A key property is the possibility to remove surplus money in future decisions, yielding approximate downside risk minimization. T3 - ZIB-Report - SC-99-30 KW - mean-variance analysis KW - downside risk KW - multi-period model Y1 - 1999 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-4183 ER - TY - GEN A1 - Steinbach, Marc T1 - Recursive Direct Algorithms for Multistage Stochastic Programs in Financial Engineering N2 - Multistage stochastic programs can be seen as discrete optimal control problems with a characteristic dynamic structure induced by the scenario tree. To exploit that structure, we propose a highly efficient dynamic programming recursion for the computationally intensive task of KKT systems solution within an interior point method. Test runs on a multistage portfolio selection problem demonstrate the performance of the algorithm. T3 - ZIB-Report - SC-98-23 KW - multistage stochastic programs KW - discrete dynamics KW - tree-sparse QP KW - KKT recursion KW - finance Y1 - 1998 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-3661 ER - TY - GEN A1 - Steinbach, Marc T1 - Recursive Direct Optimization and Successive Refinement in Multistage Stochastic Programs N2 - The paper presents a new algorithmic approach for multistage stochastic programs which are seen as discrete optimal control problems with a characteristic dynamic structure induced by the scenario tree. To exploit that structure, we propose a highly efficient dynamic programming recursion for the computationally intensive task of KKT systems solution within a primal-dual interior point method. Convergence is drastically enhanced by a successive refinement technique providing both primal and dual initial estimates. Test runs on a multistage portfolio selection problem demonstrate the performance of the method. T3 - ZIB-Report - SC-98-27 KW - Multistage stochastic programs KW - discrete dynamics KW - tree-sparse QP KW - KKT recursion KW - successive refinement Y1 - 1998 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-3706 ER - TY - GEN A1 - Steinbach, Marc T1 - Hierarchical Sparsity in Multistage Convex Stochastic Programs N2 - Interior point methods for multistage stochastic programs involve KKT systems with a characteristic global block structure induced by dynamic equations on the scenario tree. We generalize the recursive solution algorithm proposed in an earlier paper so that its linear complexity extends to a refined tree-sparse KKT structure. Then we analyze how the block operations can be specialized to take advantage of problem-specific sparse substructures. Savings of memory and operations for a financial engineering application are discussed in detail. T3 - ZIB-Report - 00-15 KW - Multistage Stochastic Programs KW - Hierarchical KKT Sparsity Y1 - 2000 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-5837 ER - TY - GEN A1 - Henrion, Rene A1 - Li, Pu A1 - Möller, Andris A1 - Steinbach, Marc A1 - Wendt, Moritz A1 - Wozny, Günter T1 - Stochastic Optimization for Operating Chemical Processes Under Uncertainty N2 - Mathematical optimization techniques are on their way to becoming a standard tool in chemical process engineering. While such approaches are usually based on deterministic models, uncertainties such as external disturbances play a significant role in many real-life applications. The present article gives an introduction to practical issues of process operation and to basic mathematical concepts required for the explicit treatment of uncertainties by stochastic optimization. T3 - ZIB-Report - 01-04 KW - Uncertainty KW - Chemical Processes KW - Multistage Stochastic Programs KW - Probabilistic Constraints Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6304 ER - TY - GEN A1 - Borndörfer, Ralf A1 - Dovica, Ivan A1 - Nowak, Ivo A1 - Schickinger, Thomas T1 - Robust Tail Assignment N2 - We propose an efficient column generation method to minimize the probability of delay propagations along aircraft rotations. In this way, delay resistant schedules can be constructed. Computational results for large-scale real-world problems demonstrate substantial punctuality improvements. The method can be generalized to crew and integrated scheduling problems. T3 - ZIB-Report - 10-08 KW - Flugzeugumlaufplanung KW - robuste Optimierung KW - Spaltenerzeugung KW - Tail Assignment KW - Robust Optimization KW - Column Generation Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-11707 SN - 1438-0064 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 - Eisenblätter, Andreas A1 - Schweiger, Jonas T1 - Multistage Stochastic Programming in Strategic Telecommunication Network Planning N2 - Mobile communication is nowadays taken for granted. Having started primarily as a service for speech communication, data service and mobile Internet access are now driving the evolution of network infrastructure. Operators are facing the challenge to match the demand by continuously expanding and upgrading the network infrastructure. However, the evolution of the customer's demand is uncertain. We introduce a novel (long-term) network planning approach based on multistage stochastic programming, where demand evolution is considered as a stochastic process and the network is extended as to maximize the expected profit. The approach proves capable of designing large-scale realistic UMTS networks with a time-horizon of several years. Our mathematical optimization model, the solution approach, and computational results are presented in this paper. T3 - ZIB-Report - 11-06 KW - UMTS KW - Network Evolution KW - Multistage KW - Stochastic Programming Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-12232 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 -