TY - CHAP A1 - Borndörfer, Ralf A1 - Schenker, Sebastian A1 - Skutella, Martin A1 - Strunk, Timo ED - Greuel, G.-M. ED - Koch, Thorsten ED - Paule, Peter ED - Sommese, Andrew T1 - PolySCIP T2 - Mathematical Software – ICMS 2016, 5th International Conference, Berlin, Germany, July 11-14, 2016, Proceedings N2 - PolySCIP is a new solver for multi-criteria integer and multi-criteria linear programs handling an arbitrary number of objectives. It is available as an official part of the non-commercial constraint integer programming framework SCIP. It utilizes a lifted weight space approach to compute the set of supported extreme non-dominated points and unbounded non-dominated rays, respectively. The algorithmic approach can be summarized as follows: At the beginning an arbitrary non-dominated point is computed (or it is determined that there is none) and a weight space polyhedron created. In every next iteration a vertex of the weight space polyhedron is selected whose entries give rise to a single-objective optimization problem via a combination of the original objectives. If the ptimization of this single-objective problem yields a new non-dominated point, the weight space polyhedron is updated. Otherwise another vertex of the weight space polyhedron is investigated. The algorithm finishes when all vertices of the weight space polyhedron have been investigated. The file format of PolySCIP is based on the widely used MPS format and allows a simple generation of multi-criteria models via an algebraic modelling language. Y1 - 2016 SN - 978-3-319-42431-6 U6 - https://doi.org/10.1007/978-3-319-42432-3_32 VL - 9725 SP - 259 EP - 264 PB - Springer International Publishing ET - Mathematical Software – ICMS 2016 ER - TY - GEN A1 - Vierhaus, Ingmar A1 - Fügenschuh, Armin A1 - Gottwald, Robert Lion A1 - Grösser, Stefan N. T1 - Modern Nonlinear Optimization Techniques for an Optimal Control of System Dynamics Models N2 - We study System Dynamics models with several free parameters that can be altered by the user. We assume that the user's goal is to achieve a certain dynamic behavior of the model by varying these parameters. In order to the find best possible combination of parameter settings, several automatic parameter tuning methods are described in the literature and readily available within existing System Dynamic software packages. We give a survey on the available techniques in the market and describe their theoretical background. Some of these methods are already six decades old, and meanwhile newer and more powerful optimization methods have emerged in the mathematical literature. One major obstacle for their direct use are tabled data in System Dynamics models, which are usually interpreted as piecewise linear functions. However, modern optimization methods usually require smooth functions which are twice continuously differentiable. We overcome this problem by a smooth spline interpolation of the tabled data. We use a test set of three complex System Dynamic models from the literature, describe their individual transition into optimization problems, and demonstrate the applicability of modern optimization algorithms to these System Dynamics Optimization problems. T3 - ZIB-Report - 14-08 KW - System Dynamics KW - Optimal Control KW - Nonlinear Optimization KW - Spline Interpolation Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-48159 SN - 1438-0064 ER - TY - CHAP A1 - Brose, Achim A1 - Fügenschuh, Armin A1 - Gausemeier, Pia A1 - Vierhaus, Ingmar A1 - Seliger, Günther T1 - A System Dynamic Enhancement for the Scenario Technique T2 - Proc. 11th Global Conference on Sustainable Manufacturing N2 - The Scenario Technique is a strategic planning method that aims to describe and analyze potential developments of a considered system in the future. Its application consists of several steps, from an initial problem analysis over an influence analysis to projections of key factors and a definition of the scenarios to a final interpretation of the results. The technique itself combines qualitative and quantitative methods and is an enhancement of the standard Scenario Technique. We use the numerical values gathered during the influence analysis, and embed them in a System Dynamics framework. This yields a mathematically rigorous way to achieve predictions of the system‘s future behavior from an initial impulse and the feedback structure of the factors. The outcome of our new method is a further way of projecting the present into the future, which enables the user of the Scenario Technique to obtain a validation of the results achieved by the standard method. Y1 - 2013 SP - 561 EP - 566 PB - Universitätsverlag der TU Berlin CY - Berlin ER - TY - CHAP A1 - Scheumann, René A1 - Vierhaus, Ingmar A1 - Chang, Ya-Ju A1 - Fügenschuh, Armin A1 - Finkbeiner, Matthias T1 - Identification of trade-offs for sustainable manufacturing of a Bamboo Bike by System Dynamics T2 - Proceedings of the 27. Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management N2 - We develop a generic System Dynamic model to simulate the production, machines, employees, waste, and capital flows of a manufacturing company. In a second step, this model is specialised by defining suit-able input data to represent a bicycle manufacturing company in a developing country. We monitor a set of sustainability indicators to understand the social, environmental and economic impact of the company, and to estimate managerial decisions to be taken in order to improve on these criteria. We show that the social and environmental situation can be improved over time without sacrificing the economic success of the company's business. Y1 - 2013 SP - 523 EP - 531 ER - TY - CHAP A1 - Fügenschuh, Armin A1 - van Veldhuizen, Roel A1 - Vierhaus, Ingmar T1 - Production Planning for Non-Cooperating Companies with Nonlinear Optimization T2 - 11th Global Conference on Sustainable Manufacturing : Proceedings N2 - We consider a production planning problem where two competing companies are selling their items on a common market. Moreover, the raw material used in the production is a limited non-renewable resource. The revenue per item sold depends on the total amount of items produced by both players. If they collaborate they could apply a production strategy that leads to the highest combined revenue. Usually the formation of such syndicates is prohibited by law; hence we assume that one company does not know how much the other company will produce. We formulate the problem for company A to find an optimal production plan without information on the strategy of company B as a nonlinear mathematical optimization problem. In its naive formulation the model is too large, making its solution practically impossible. After a reformulation we find a much smaller model, which we solve by spatial branch-and-cut methods and linear programming. We discuss the practical implications of our solutions. Y1 - 2013 SP - 536 EP - 541 PB - Universitätsverlag der TU Berlin CY - Berlin ER - TY - CHAP A1 - Fügenschuh, Armin A1 - Vierhaus, Ingmar T1 - System Dynamic Optimization in the Sustainability Assessment of a World-Model T2 - 11th Global Conference on Sustainable Manufacturing : Proceedings N2 - The System Dynamics (SD) methodology is a framework for modeling and simulating the dynamic behavior of socioeconomic systems. Characteristic for the description of such systems is the occurrence of feedback loops together with stocks and flows. The mathematical equations that describe the system are usually nonlinear. Therefore seemingly simple systems can show a nonintuitive, nonpredictable behavior over time. Controlling a dynamical system means to define a desired final state in which the system should be, and to specify potential interventions from outside that should keep the system on the right track. The central question is how to compute such globally optimal control for a given SD model. We propose a branch-and-bound approach that is based on a bound propagation method, primal heuristics, and spatial branching. We apply our new SD-control method to a small System Dynamics model, that describes the evolution of a social-economic system over time. We examine the problem of steering this system on a sustainable consumption path. Y1 - 2013 SP - 530 EP - 535 PB - Universitätsverlag der TU Berlin CY - Berlin ER - TY - CHAP A1 - Scheumann, René A1 - Fügenschuh, Armin A1 - Schenker, Sebastian A1 - Vierhaus, Ingmar A1 - Borndörfer, Ralf A1 - Finkbeiner, Matthias ED - Seliger, Günther T1 - Global Manufacturing: How to Use Mathematical Optimisation Methods to Transform to Sustainable Value Creation BT - (GCSM 2012) T2 - Proceedings of the 10th Global Conference on Sustainable Manufacturing Y1 - 2012 SN - 978-605-63463-1-6 SP - 538 EP - 545 ER - TY - CHAP A1 - Schenker, Sebastian A1 - Vierhaus, Ingmar A1 - Borndörfer, Ralf A1 - Fügenschuh, Armin A1 - Skutella, Martin ED - Stark, Rainer ED - Seliger, Günther ED - Bonvoisin, Jérémy T1 - Optimisation Methods in Sustainable Manufacturing BT - Challenges, Solutions and Implementation Perspectives T2 - Sustainable Manufacturing N2 - Sustainable manufacturing is driven by the insight that the focus on the economic dimension in current businesses and lifestyles has to be broadened to cover all three pillars of sustainability: economic development, social development, and environmental protection. Y1 - 2017 SN - 978-3-319-48514-0 U6 - https://doi.org/10.1007/978-3-319-48514-0_15 SP - 239 EP - 253 PB - Springer International Publishing ER - TY - CHAP A1 - Vierhaus, Ingmar A1 - Fügenschuh, Armin T1 - Global and Local Optimal Control of a Resource Utilization Problem T2 - Proceedings of the 33rd International Conference of the System Dynamics Society N2 - System Dynamic models describe physical, technical, economical, or social systems using differential and algebraic equations. In their purest form, these models are intended to describe the evolution of a system from a given initial state. In many applications, it is possible to intervene with the system in order to obtain a desired dynamic or a certain outcome in the end. On the mathematical side, this leads to control problems, where aside from the simulation one has to find optimal intervention functions over time that maximize a specific objective function. Using a dynamical model for the utilization of a natural nonrenewable resource of Behrens as a demonstrator example, we present two main mathematical solution strategies. They are distinguished by the quality certificate on their respective solution: one leads to proven local optimal solution, and the other technique yields proven global optimal solutions. We present implementational and numerical issues, and a comparison of both methods. Y1 - 2015 ER - TY - GEN A1 - Fügenschuh, Armin A1 - Vierhaus, Ingmar T1 - System Dynamic Optimization in the Sustainability Assessment of a World-Model N2 - The System Dynamics (SD) methodology is a framework for modeling and simulating the dynamic behavior of socioeconomic systems. Characteristic for the description of such systems is the occurrence of feedback loops together with stocks and flows. The mathematical equations that describe the system are usually nonlinear. Therefore seemingly simple systems can show a nonintuitive, nonpredictable behavior over time. Controlling a dynamical system means to define a desired final state in which the system should be, and to specify potential interventions from outside that should keep the system on the right track. The central question is how to compute such globally optimal control for a given SD model. We propose a branch-and-bound approach that is based on a bound propagation method, primal heuristics, and spatial branching. We apply our new SD-control method to a small System Dynamics model, that describes the evolution of a social-economic system over time. We examine the problem of steering this system on a sustainable consumption path. T3 - ZIB-Report - 13-23 KW - System Dynamics; Mixed-Integer Nonlinear Optimization Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-18148 UR - http://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/4026 SN - 1438-0064 ER - TY - GEN A1 - Brose, Achim A1 - Fügenschuh, Armin A1 - Gausemeier, Pia A1 - Vierhaus, Ingmar A1 - Seliger, Günther T1 - A System Dynamic Enhancement for the Scenario Technique N2 - The Scenario Technique is a strategic planning method that aims to describe and analyze potential developments of a considered system in the future. Its application consists of several steps, from an initial problem analysis over an influence analysis to projections of key factors and a definition of the scenarios to a final interpretation of the results. The technique itself combines qualitative and quantitative methods and is an enhancement of the standard Scenario Technique. We use the numerical values gathered during the influence analysis, and embed them in a System Dynamics framework. This yields a mathematically rigorous way to achieve predictions of the system‘s future behavior from an initial impulse and the feedback structure of the factors. The outcome of our new method is a further way of projecting the present into the future, which enables the user of the Scenario Technique to obtain a validation of the results achieved by the standard method. T3 - ZIB-Report - 13-24 KW - Scenario Technique; System Dynamics Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-18150 UR - http://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/4026 SN - 1438-0064 ER - TY - GEN A1 - Fügenschuh, Armin A1 - van Veldhuizen, Roel A1 - Vierhaus, Ingmar T1 - Production Planning for Non-Cooperating Companies with Nonlinear Optimization N2 - We consider a production planning problem where two competing companies are selling their items on a common market. Moreover, the raw material used in the production is a limited non-renewable resource. The revenue per item sold depends on the total amount of items produced by both players. If they collaborate they could apply a production strategy that leads to the highest combined revenue. Usually the formation of such syndicates is prohibited by law; hence we assume that one company does not know how much the other company will produce. We formulate the problem for company A to find an optimal production plan without information on the strategy of company B as a nonlinear mathematical optimization problem. In its naive formulation the model is too large, making its solution practically impossible. After a reformulation we find a much smaller model, which we solve by spatial branch-and-cut methods and linear programming. We discuss the practical implications of our solutions. T3 - ZIB-Report - 13-25 KW - Non-Cooperative Two-Person Games; Mixed-Integer Nonlinear Optimization Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-18163 UR - http://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/4026 SN - 1438-0064 ER - TY - CHAP A1 - Buchert, Tom A1 - Neugebauer, Sabrina A1 - Schenker, Sebastian A1 - Lindow, Kai A1 - Stark, Rainer T1 - Multi-criteria Decision Making as a Tool for Sustainable Product Development - Benefits and Obstacles T2 - Procedia CIRP Y1 - 2015 U6 - https://doi.org/10.1016/j.procir.2014.07.110 VL - 26 SP - 70 EP - 75 ER - TY - CHAP A1 - Gausemeier, Pia A1 - Seliger, Günther A1 - Schenker, Sebastian A1 - Borndörfer, Ralf T1 - Nachhaltige Technologiepfade für unterschiedliche Entwicklungsniveaus mithilfe mehrkriterieller Entscheidungsfindung T2 - Vorausschau und Technologieplanung Y1 - 2014 SN - 978-3-942647-53-3 VL - 334 SP - 65 EP - 94 ER - TY - CHAP A1 - Sprösser, Gunther A1 - Schenker, Sebastian A1 - Pittner, Andreas A1 - Borndörfer, Ralf A1 - Rethmeier, Michael A1 - Chang, Ya-Ju A1 - Finkbeiner, Matthias T1 - Sustainable Welding Process Selection based on Weight Space Partitions T2 - Procedia CIRP Y1 - 2016 U6 - https://doi.org/10.1016/j.procir.2016.01.077 VL - 40 SP - 127 EP - 132 ER - TY - CHAP A1 - Schenker, Sebastian A1 - Steingrímsson, Jón Garðar A1 - Borndörfer, Ralf A1 - Seliger, Günther T1 - Modelling of Bicycle Manufacturing via Multi-criteria Mixed Integer Programming T2 - Procedia CIRP Y1 - 2015 U6 - https://doi.org/10.1016/j.procir.2014.07.068 VL - 26 SP - 276 EP - 280 ER - TY - CHAP A1 - Vierhaus, Ingmar A1 - Fügenschuh, Armin T1 - A Global Approach to the Optimal Control of System Dynamics Models T2 - Proceedings of the 31st International Conference of the System Dynamics Society N2 - The System Dynamics (SD) methodology is a framework for modeling and simulating the dynamic behavior of socioeconomic systems. Characteristic for the description of such systems is the occurrence of feedback loops together with stocks and flows. The mathematical equations that describe the system are usually ordinary differential equations and nonlinear algebraic constraints. Seemingly simple systems can show a nonintuitive, unpredictable behavior over time. Controlling a dynamical system means to specify potential interventions from outside that should keep the system on the desired track, and to define an evaluation schema to compare different controls among each other, so that a ``best'' control can be defined in a meaningful way. The central question is how to compute such globally optimal control for a given SD model, that allows the transition of the system into a desired state with minimum effort. We propose a mixed-integer nonlinear programming (MINLP) reformulation of the System Dynamics Optimization (SDO) problem. MINLP problems can be solved by linear programming based branch-and-bound approach. We demonstrate that standard MINLP solvers are not able to solve SDO problem. To overcome this obstacle, we introduce a special-tailored bound propagation method. Numerical results for these test cases are presented. Y1 - 2013 ER - TY - CHAP A1 - Vierhaus, Ingmar A1 - Fügenschuh, Armin A1 - Gottwald, Robert Lion A1 - Grösser, Stefan T1 - Modern Nonlinear Optimization Techniques for an Optimal Control of System Dynamics Models T2 - Proceedings of the 32nd International Conference of the System Dynamics Society N2 - We study System Dynamics models with several free parameters that can be altered by the user. We assume that the user's goal is to achieve a certain dynamic behavior of the model by varying these parameters. In order to find best possible combination of parameter settings, several automatic parameter tuning methods are described in the literature and readily available within existing System Dynamic software packages. We give a survey on the available techniques in the market and describe their theoretical background. Some of these methods are already six decades old, and meanwhile newer and more powerful optimization methods have emerged in the mathematical literature. One major obstacle for their direct use are tabled data in System Dynamics models, which are usually interpreted as piecewise linear functions. However, modern optimization methods usually require smooth functions which are twice continuously differentiable. We overcome this problem by a smooth spline interpolation of the tabled data. We use a test set of three complex System Dynamic models from the literature, describe their individual transition into optimization problems, and demonstrate the applicability of modern optimization algorithms to these System Dynamics Optimization problems. Y1 - 2014 ER - TY - THES A1 - Wirsching, Marie T1 - Der Einfluss von Langzahlarithmetik auf das Gewichtsraumpolyeder in mehrkriterieller Optimierung N2 - Die Arbeit befasst sich mit einem gewichtsraumbasierten Algorithmus, der ganzzahlige und lineare Optimierungsprobleme mit mehreren Zielfunktionen löst und die Menge der unterstützt nicht dominierten Punkte ermittelt. Die dabei erzeugten Gewichtsraumpolyeder sind das entscheidende Mittel, um die gesuchte Lösungsmenge zu bestimmen. Aus softwaretechnischer Sicht sind numerische Ungenauigkeiten potentielle Fehlerquellen, die sich negativ auf das Endergebnis auswirken. Aus diesem Grund untersuchen wir anhand von Zuweisungs- und Rucksackinstanzen mit 3 Zielfunktionen, inwieweit der Gebrauch von Langzahlarithmetik die Gewichtsraumpolyeder und die damit verbundene Menge der unterstützt nicht dominierten Punkte beeinflusst. Y1 - 2018 ER - TY - GEN A1 - Schenker, Sebastian A1 - Borndörfer, Ralf A1 - Skutella, Martin T1 - A novel partitioning of the set of non-dominated points N2 - We consider a novel partitioning of the set of non-dominated points for general multi-objective integer programs with $k$ objectives. The set of non-dominated points is partitioned into a set of non-dominated points whose efficient solutions are also efficient for some restricted subproblem with one less objective; the second partition comprises the non-dominated points whose efficient solutions are inefficient for any of the restricted subproblems. We show that the first partition has the nice property that it yields finite rectangular boxes in which the points of the second partition are located. T3 - ZIB-Report - 16-55 Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-61286 SN - 1438-0064 ER - TY - GEN A1 - Fügenschuh, Armin A1 - Grösser, Stefan N. A1 - Vierhaus, Ingmar T1 - A Global Approach to the Control of an Industry Structure System Dynamics Model N2 - We consider a system dynamics model that describes the effect of human activity on natural resources. The central stocks are the accumulated profit, the industry structures, and the water resources. The model can be controlled through two time-dependent parameters. The goal in this paper is to find a parameter setting that leads to a maximization of a performance index, which reflects both environmental and economic aspects. Thus, the goal is to identify the most sustainable stock of industry structures within the model's constraints and assumptions. In order to find a proven global optimal parameter set, we formulate the System Dynamics Optimization model as a mixed-integer nonlinear problem that is accessible for numerical solvers. Due to the dynamic structure of the model, certain steps of the solution process must be handled with greater care, compared to standard non-dynamic problems. We describe our approach of solving the industry structure model and present computational results. In addition, we discuss the limitations of the approach and next steps. T3 - ZIB-Report - 13-67 KW - System Dynamics; Mixed-Integer Nonlinear Optimization Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-42932 SN - 1438-0064 ER - TY - GEN A1 - Gamrath, Gerald A1 - Fischer, Tobias A1 - Gally, Tristan A1 - Gleixner, Ambros A1 - Hendel, Gregor A1 - Koch, Thorsten A1 - Maher, Stephen J. A1 - Miltenberger, Matthias A1 - Müller, Benjamin A1 - Pfetsch, Marc A1 - Puchert, Christian A1 - Rehfeldt, Daniel A1 - Schenker, Sebastian A1 - Schwarz, Robert A1 - Serrano, Felipe A1 - Shinano, Yuji A1 - Vigerske, Stefan A1 - Weninger, Dieter A1 - Winkler, Michael A1 - Witt, Jonas T. A1 - Witzig, Jakob T1 - The SCIP Optimization Suite 3.2 N2 - The SCIP Optimization Suite is a software toolbox for generating and solving various classes of mathematical optimization problems. Its major components are the modeling language ZIMPL, the linear programming solver SoPlex, the constraint integer programming framework and mixed-integer linear and nonlinear programming solver SCIP, the UG framework for parallelization of branch-and-bound-based solvers, and the generic branch-cut-and-price solver GCG. It has been used in many applications from both academia and industry and is one of the leading non-commercial solvers. This paper highlights the new features of version 3.2 of the SCIP Optimization Suite. Version 3.2 was released in July 2015. This release comes with new presolving steps, primal heuristics, and branching rules within SCIP. In addition, version 3.2 includes a reoptimization feature and improved handling of quadratic constraints and special ordered sets. SoPlex can now solve LPs exactly over the rational number and performance improvements have been achieved by exploiting sparsity in more situations. UG has been tested successfully on 80,000 cores. A major new feature of UG is the functionality to parallelize a customized SCIP solver. GCG has been enhanced with a new separator, new primal heuristics, and improved column management. Finally, new and improved extensions of SCIP are presented, namely solvers for multi-criteria optimization, Steiner tree problems, and mixed-integer semidefinite programs. T3 - ZIB-Report - 15-60 KW - mixed-integer linear and nonlinear programming KW - MIP solver KW - MINLP solver KW - linear programming KW - LP solver KW - simplex method KW - modeling KW - parallel branch-and-bound KW - branch-cut-and-price framework KW - generic column generation KW - Steiner tree solver KW - multi-criteria optimization KW - mixed-integer semidefinite programming Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-57675 SN - 1438-0064 ER -