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Modeling and Simulation
Published in B K Bala, Energy Systems Modeling and Policy Analysis, 2022
Modeling has been an integral part of national energy planning since the 1970’s energy crisis. Energy systems are highly complex, containing technological, socio-economic, environmental and political components, and the modeling of such complex systems is a formidable challenge. The twin goals of understanding and managing highly complex energy systems are to provide the desired goal of energy systems, and in order to manage an energy system effectively, the energy system under consideration must be modeled and simulated and well understood. System dynamics, a methodology of computer modeling of highly complex systems containing technological, socio-economic, environmental and political components, is the most appropriate technique to analyze and design policy options for national energy systems. System dynamics methodology is based on non-linear, time-lagged feedback concepts of control theory and systems thinking. Modeling using system dynamics can assist in policy planning and decision-making on a rational basis.
Models and Approaches for Sustainable Performance Measurement
Published in R. Ganesh Narayanan, Jay S. Gunasekera, Sustainable Material Forming and Joining, 2019
S. Vinodh, K. J. Manjunatheshwara
System Dynamics is a modeling approach focused on modeling system with complex characteristics and evaluates its performance over time period. It is quite different from other approaches as it utilizes feedback concept, and evaluate dynamic behavior in loops. System Dynamics modeling is vital for sustainable manufacturing as it focuses on analysis of the system behavior and reaction to trends (Sterman, 2001). The casual feedback loop diagram forms the fundamental for system dynamics modeling and allows the users to simulate the system variables. The casual feedback loop diagram allows the users to define the system pattern, key variables, causal relationship, and the direction of system variables. After the causal feedback loop diagram is configured, the system is modeled using simulation software.
System Dynamics and Synthetic Worlds
Published in C.A.P. Smith, Kenneth W. Kisiel, Jeffrey G. Morrison, Working Through Synthetic Worlds, 2009
System dynamics is a methodology for studying and managing complex feedback systems such as the environment, e.g., weather, natural disaster, man-made pollution, and population growth. Synthetic worlds, an integration of the real world and virtual worlds, offer the opportunity to globally integrate environmental data, models and public policies to address the complexities of the environment as a whole. Together synthetic worlds and system dynamics provide a venue and methodology for global environmental analysis in contrast to many limited national stovepipe approaches currently at hand. Using the methodologies and tools of system dynamics in synthetic worlds will facilitate a holistic learning environment for the analysis of environmental policy (Forrester 1961). Working in synthetic worlds, system dynamicists will be able to evaluate environmental policy in real time to show potential benefits and possible negative, unintended consequences. Predictive analysis of possible environmental events and planned policies that will potentially mitigate them will be available for professional and public scrutiny, as well as forensic analysis.
Designing a dynamic model to evaluate lot-sizing policies in different scenarios of demand and lead times in order to reduce the nervousness of the MRP system.
Published in Journal of Industrial and Production Engineering, 2021
Alireza Pooya, Nadiye Fakhlaei, Ali Alizadeh-Zoeram
To simulate the relationship between MRP variables including demand, lead time, amount of re-order, inventory levels and safety stock, a system dynamics approach is used. System dynamics is a method which can increase the level of learning in complex systems and it can also create simulated computer models to help understand the dynamic complexities. Vensim software which was used for simulation, is a powerful tool for modeling, simulating, model testing, and sensitivity analysis of system dynamics. To validate the model, various methods such as; limit test and compatibility test were used along with the experts’ opinion. After building the model, it can be used to design and evaluate scenarios and policies. In this study, three scenarios were chosen based on demand and lead time under various modes, including stochastic demand – stochastic lead time (Scenario 1: S1), stochastic demand – deterministic lead time (Scenario 2: S2), and the suggestion of the company (Scenario 3: S3), in which the company determines the values of demand, and lead time (Table 1).
A policy knowledge- and reasoning-based method for data-analytic city policymaking
Published in Building Research & Information, 2021
Sun-Young Ihm, Hye-Jin Lee, Eun-Ji Lee, Young-Ho Park
System dynamics is used to study and manage complex feedback systems such as those of businesses or societies. System dynamics modelling depicts the conceptual model structure in the form of a causal loop diagram based on the designer’s understanding of the system (Park et al., 2005). Figure 14 shows a diagram of the correlations between influential variables in the Boston housing data that were found through the analysis techniques described in fourth section, along with other additional variables that have been used in other related studies. The highly influential variables that were found through the experiments in fourth section are shown as ovals. The green ovals in Figure 14 are the variables found through k-means clustering and Shapley values. In addition, the red ovals represent the variables found through Shapley values and a random forest analysis. The purple ovals are variables found through a regression analysis. In the feedback loop in Figure 14(a), MEDV increases as RAD and DIS increase, and TAX increases as MEDV increases. In the feedback loop in Figure 14(b), LSTAT increases as MEDV increases, PTRATIO increases as LSTAT increases, CRIM increases as PTRATIO increases, and finally, MEDV decreases again.
Systems Dynamics-Based Modeling of Data Warehouse Quality
Published in Journal of Computer Information Systems, 2019
Girish H. Subramanian, Kai Wang
As a mathematical modeling technique, system dynamics (SD) can not only help to model an entire system but also provide the logic of research in a non-linear way. [11] However, system dynamics does not have detailed protocols to describe the use of qualitative data or quantitative research methods in the modeling process. [29] Considering this research is initially inductive approach-based grounded theory can be used to provide a systematic way of collecting the information about the factors that may influence data warehouse components. Therefore, a mixed use of Grounded Theory and System Dynamics methodology – Grounded System Modeling – is ideal as a hybridization methodology to provide explanatory or maybe even predictive descriptions with enough quantitative data for the research objective. The primary data is collected through five well-known online databases storing electronic journals, conference proceedings and case study (e.g., IEEE Xplore, ACM digital library, and Google Scholar). These searches only include the material published from the years 1992 to 2016 since data warehouse was firstly officially defined in 1992. Therefore, as a research strategy, grounded theory [11, 30–32, 74] is suitable enough to be employed to provide a systematic method of exploring collective primary data.