An Integrated Modelling Methodology for Simulation of Large and Complex Systems

Smart Transportation Systems (SmTS) will exceed complex systems with their classification as complex, sociotechnical, and AI‐based systems. Possessing a toolkit that helps systemically understand, analyze, and assess these systems will be advantageous in the early stages of the systems engineering (SE) lifecycle, and Agent Based Models (ABMs) could provide this alleviation. This paper proposes how lessons learned from a novice ABM developer can help create a novel framework that uncovers complex system behaviors and bolsters simulation modeling learning mechanisms for engineers. A knowledge base of StarLogo and NetLogo was developed using SmTS as a system of interest (SoI). A comparative assessment of StarLogo and NetLogo was performed, creating an expandable toolkit called the Simulation Modeling Pipeline Framework (SMPF). The SmTS was then utilized as a case study for applying the SMPF as a learning tool for simulation model prototyping and development. Results showed StarLogo and NetLogo are exceptional ABMs for novice ABM developers, however each comes with their strengths and weaknesses. StarLogo programming language is conducive for rapid code generation but lacks lower modeling fidelity. NetLogo consummates StarLogo's weaknesses, however its proprietary programming language is cumbersome for beginners. These complementary characteristics helped form the SMPF by connecting model abstractions of disparate ABMs, facilitating systematic‐based learning, prototyping, and development of simulation models. By using two ABMs; a toolkit for simulation modeling of emerging and/or nonexistent complex systems can be developed and utilized in learning ABM development, while cultivating a deeper comprehension of system mechanisms at various levels for a SoI.

The value of modeling and simulation for education, training, and testing in information security has been documented in several studies. In this paper, we suggest that it is important not only to include the general use of simulation in various courses of the security curriculum, but also to include the theory and development of simulation models. We describe briefly the general features of simulation models and tools for model development that we are using in computing education.A collection of educational simulation tools have been created in the OOPsim project, for developing discrete-event simulation models. The principal goal of this project is to develop newer simulation tools and approaches for education in computing. The Object Oriented Simulation Language, OOSimL, was recently developed with partial support from an NSF CPATH grant.Two object-oriented simulation models are discussed as typical examples discussed in a simulation course on security: a model of a distributed denial of service (DDoS) and a model of simple firewall system. These models were developed with educational simulation tools created in OOPsim project. We have also developed a course that emphasizes an approach to early introduction to object-oriented discrete-event simulation.The DDoS simulation model is implemented using the OOSimL simulation language. The Firewall simulation model was implemented in Java with the PsimJ2 object oriented simulation package; other models have been implemented in C++ using the Psim3 object oriented simulation package. The simulation tools and model development are very useful for educating and training students and professionals in information security, computer science, software engineering, information technology, and in other related disciplines.

<i>Introduction to the Modeling and Analysis of Complex Systems.</i> H. Sayama (Ed.). (2015, Open SUNY Textbooks). Free open access PDF, 498 pp. ISBN 978-1-942341-06-2 (deluxe color edition). ISBN 978-1-942341-08-6 (print edition). ISBN 978-1-942341-09-3 (ebook).

The development of simulation models can be time consuming and highly dependant on system data being widely available. When using simulation modeling to analyze future systems, system data may not be available for the system under study and simulation results are often needed within a short time frame to support early system design efforts. This paper presents a parametric estimation/generic simulation integrated environment developed to facilitate the rapid development of valid simulation models for the Orbital Space Vehicle ground processing operations.

The development of simulation models can be time consuming and highly dependant on system data being widely available. When using simulation modeling to analyze future systems, system data may not be available for the system under study and simulation results are often needed within a short time frame to support early system design efforts. This paper presents a parametric estimation/generic simulation integrated environment developed to facilitate the rapid development of valid simulation models for the Orbital Space Vehicle ground processing operations.

Performance analysis of manufacturing systems : queueing approximations and algorithms

Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. This paper focuses on the integration of methodologies in the smart adaptive applications. Statistical methods and arti cial neural networks form a good basis for the data-driven analysis of interactions and fuzzy logic introduces solutions for knowledge-based understanding the system behaviour and the meaning of variable levels. Efficient normalisation, scaling and decomposition approaches are the key methodologies in developing large-scale applications. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are de ned separately for the scaling and the interactions. Introduction EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 2 SNE 24(1) – 4/2014 ON 1 Steady-State Modelling multiple input, multiple output MIMO response surface methodology RSM multiple input, single output MISO Fuzzy set theory Extension principle Type-2 fuzzy Figure 1: Methodologies for modelling of complex system. EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 3 O N Linguistic fuzzy models Takagi-Sugeno (TS) fuzzy models Singleton models Fuzzy relational models Figure 2: Combined fuzzy modelling. Artificial neural networks ANN Linear networks multilayer perceptron MLP backpropagation learning Neurofuzzy systems Figure 3: A fuzzy neuron. function expansion Approximate reasoning 2 Decomposition Methodologies EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 4 SNE 24(1) – 4/2014 ON Decomposition Figure 4: Decomposition for modelling. Hierarchical clustering Partitioning-based clustering algorithms fuzzy clustering Fuzzy c-means (FCM) Subtractive clustering Neural clustering Robust clustering number of clusters Composite local model Linear parameter varying (LPV) models Piecewise affine (PWA) systems Fuzzy models Multiple neural network systems mixed approach EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 5 O N 3 Adaptive Nonlinear Scaling Membership 3.1 Working point and feasible ranges Figure 5: Nonlinear scaling [28] EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 6 SNE 24(1) – 4/2014 ON 3.2 Membership definitions linguistic range linguistic values • the corner points (Figure 5) are good for visualisation; • the parameters suit for tuning; • the coefficients are used in the calculations. 3.3 Adaptation of nonlinear scaling EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 7 O N 4 Intelligent Systems 4.1 LE models 4.2 Hybrid LE systems EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 8 SNE 24(1) – 4/2014 ON extension principle linguistification

Development of dynamic models for complex systems often does not exploit the hierarchical structure of the system design. Rather, equations describing physical effects of interest are set up, manipulated, and combined at an early stage of the modeling process. In contrary, by applying a structured bond graph approach to an electrohydraulic servo valve as an example of a complex multi energy domain system it is shown how word bond graphs, known since bond graphs were invented by Paynter but seldom used, account for the hierarchical structure of a system design in a natural way and allow for systematic top-down development of a consistent simulation model on a graphical basis. Formulation of dynamic system equations is the last step in this modeling procedure and can be easily carried out by inspection of the final bond graph. By this way a state space model was prepared for the servo valve, coded as a FORTRAN subroutine of a general ODEs solver, and the system simulated on a workstation.

I believe we are leaving one cultural and technological age and entering another; that we are in the early stages of a change in our conception of the world, a change in our way of thinking about it, and a change in the technology with which we try to make it serve our purposes. These changes, I believe, are as fundamental and pervasive as were those associated with the Renaissance, the Age of the Machine that it introduced, and the Industrial Revolution that was its principal product. The socio-technical revolution we have entered may well come to be known as the Resurrection.

Simulation is a common method for studying the behavior of complex systems and revealing the mechanism of the system. However, complex systems have many parameters, non-linear interactions, and complex evolutionary dynamics. It is difficult to reveal the mechanism of complex systems. Especially complex system simulation experiments may produce a large amount of data. How to summarize the macroscopic mode of the system, identify key factors, and discover the relationship between input and output variables, still lacks an effective method. This paper proposes an integrated framework for simulation modeling and data mining, which combines data mining and simulation modeling to conduct iterative experimental exploration and analysis of complex systems. Data mining techniques were used in multiple stages of modeling and simulation, including: ETL on raw data, text mining and process mining to build conceptual models, uniform experimental design to generate simulation data, and clustering of simulation data to identify system macro patterns, use stepwise regression, neural network, etc. to build a meta-model of a complex system. The introduction of data mining can improve the ability and efficiency of complex system modeling and simulation.

Nonlinear problem solving and complex system simulation have become critical issues in many fields of science. The development of novel computational methods is crucial to understanding these complex systems. In this abstract, we explore the dynamic landscape of computational techniques, focusing on their uses in simulating complex systems and tackling nonlinear challenges. Creating complex algorithms that can deal with nonlinearity, chaos, and emergent behaviours is where it's at. Tools for modelling and comprehending such complex systems are few, but machine learning, artificial intelligence, and evolutionary computation are at the forefront. The way problems are solved has been completely rethought because of their nonlinearity-tolerance and ability to operate in high-dimensional domains. In addition, novel opportunities have arisen due to the combination of classical mathematical models with computer methods. The behaviour and emergent features of complex systems are best understood by hybrid approaches that combine differential equations, agent-based modelling, and cellular automata. These techniques provide a fine-grained comprehension of component interactions, illuminating emergent events. Moreover, the advent of high-performance computing has substantially expanded the breadth and resolution of simulations. Scientists are now able to probe increasingly complex systems, shedding light on their dynamics and behaviours. Computational capacities have been vastly improved by parallel computing, distributed systems, and cloud computing infrastructures, allowing for the study of systems that were once thought to be intractable. Nonlinear issues and complex system simulations can benefit greatly from the combination of cutting-edge computational approaches with domain-specific expertise. This abstract is a testament to the expanding significance and potential of these computational approaches in understanding complex systems and opening up new frontiers for research and solving problems.

In popular dialogues, describing a system as "complex" is often the point of resignation, inferring that the system cannot be sufficiently described, predicted nor managed. Transport networks, management infrastructure and supply chain logistics are all often described in this way. Academic dialogues have begun to explore the collective behaviors of complex systems to define a complex system specifically as an adaptive one; i.e. a system that demonstrates 'self organising' principles and 'emergent' properties. Based upon the key principles of interaction and emergence in relation to adaptive and self organising systems in cultural artifacts and processes, this paper will argue that complex systems are cultural systems. By introducing generic principles of complex systems, and looking at the exploration of such principles in art, design and media research, this paper argues that a science of cultural systems as part of complex systems theory is the post modern science for the digital age. Furthermore, that such a science was predicated by post structuralism and has been manifest in art, design and media practice since the late 1960s.

Utility of object-oriented programming in complex system modeling

Several key issues relevant to the development, validation, and applications of simulation models for soil-crop systems are discussed. Development of models with modular components providing multiple options for conceptual representation of the system components is recommended. Combining simulation models with the concepts of artificial intelligence will facilitate the development of user-interactive interfaces which permit the user to customize the model, based on expert guidance, for a specific application. Most models do not account for the spatial and temporal variabilities in input parameters. Uncertainty in model predictions resulting from such variations in the input parameters needs to be accounted for. Minimum data sets required for model development and validation as well as objective criteria for assessing model performance need to be identified. The application of crop-soil simulation models to estimate the probable success of a specific crop production management recommendation (i.e., risk analysis) and evaluating the regional variations in crop performance using spatial modelling techniques are discussed.

Complexity science offers many theories such as chaos theory and coevolutionary theory. These theories illustrate a large set of real life systems and help decipher their nonlinear and unpredictable behaviours. Categorizing an observed Complex System among these theories depends on the aspect that we intend to study, and it can help better understand the phenomena that occur within the system. This article aims to give an overview on Complex Systems and their modelling. Therefore, we compare these theories based on their main behavioural characteristics, e.g. emergence, adaptability, and dynamism. Then we compare the methods used in the literature to model and simulate Complex Systems, and we propose and discuss simple guidelines to help understand one's Complex System and choose the most adequate model to simulate it.