TY - GEN 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 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. T3 - ZIB-Report - 13-32 KW - System Dynamics KW - Modelling KW - Life Cycle Sustainability Assessment KW - Sustainability Indicators Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-18895 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 - GEN A1 - Fügenschuh, Armin A1 - Vierhaus, Ingmar T1 - A Global Approach to the Optimal Control of System Dynamics Models 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. Therefore 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. We apply our new method to a predator-prey model with additional hunting activity as control, and to a mini-world model with the consumption level as control. Numerical results for these test cases are presented. T3 - ZIB-Report - 13-28 KW - System Dynamics KW - Global Optimal Control KW - Mixed-Integer Nonlinear Optimization KW - Bounds Strengthening Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-18600 SN - 1438-0064 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 - JOUR A1 - Vierhaus, Ingmar A1 - Fügenschuh, Armin A1 - Gottwald, Robert Lion A1 - Grösser, Stefan T1 - Using white-box nonlinear optimization methods in system dynamics policy improvement JF - System Dynamics Review N2 - We present a new strategy for the direct optimization of the values of policy functions. This approach is particularly well suited to model actors with a global perspective on the system and relies heavily on modern mathematical white-box optimization methods. We demonstrate our strategy on two classical models: market growth and World2. Each model is first transformed into an optimization problem by defining how the actor can influence the models' dynamics and by choosing objective functions to measure improvements. To improve comparability between different runs, we also introduce a comparison measure for possible interventions. We solve the optimization problems, discuss the resulting policies and compare them to the existing results from the literature. In particular, we present a run of the World2 model which significantly improves the published “towards a global equilibrium” run with equal cost of intervention. Y1 - 2017 U6 - https://doi.org/10.1002/sdr.1583 VL - 33 IS - 2 SP - 138 EP - 168 ER - TY - GEN A1 - Scheumann, Rene A1 - Fügenschuh, Armin A1 - Schenker, Sebastian A1 - Vierhaus, Ingmar A1 - Borndörfer, Ralf A1 - Finkbeiner, Matthias T1 - Global Manufacturing: How to Use Mathematical Optimisation Methods to Transform to Sustainable Value Creation N2 - It is clear that a transformation to sustainable value creation is needed, because business as usual is not an option for preserving competitive advantages of leading industries. What does that mean? This contribution proposes possible approaches for a shift in existing manufacturing paradigms. In a first step, sustainability aspects from the German Sustainability Strategy and from the tools of life cycle sustainability assessment are chosen to match areas of a value creation process. Within these aspects are indicators, which can be measured within a manufacturing process. Once these data are obtained they can be used to set up a mathematical linear pulse model of manufacturing in order to analyse the evolution of the system over time, that is the transition process, by using a system dynamics approach. An increase of technology development by a factor of 2 leads to an increase of manufacturing but also to an increase of climate change. Compensation measures need to be taken. This can be done by e.g. taking money from the GDP (as an indicator of the aspect ``macroeconomic performance''). The value of the arc from that building block towards climate change must then be increased by a factor of 10. The choice of independent and representative indicators or aspects shall be validated and double-checked for their significance with the help of multi-criteria mixed-integer programming optimisation methods. T3 - ZIB-Report - 12-28 KW - Multi-Criteria Optimisation KW - Sustainability Indicator KW - System Dynamics KW - Value Creation Process Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-15703 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 -