International Journal of Modeling, Simulation, and Scientific Computing

Discrete event simulation (DES) is a well-established decision support tool in modeling work flows in manufacturing industry. But, there are an amount of practical and financial obstacles that deter the employment of this technology in industry. One of the main weaknesses of operating DES is the costs spent on collecting and mapping input data from different enterprise data resources into a DES model. Another issue is the cost of integrating simulation applications with other manufacturing applications. These barriers hinder the automated input of data into DES models and as a result deter use of real-time DES in manufacturing. This review presents the existing research studies in the literature that address the above issues, demonstrating in parallel the already implemented concepts. The scope of this review is to provide an overview of the input data phase, focusing on its automation and motivating researchers to re-examine this phase by highlighting future research directions.

A software framework is an infrastructure or architecture intended to enable the integration and interoperation of a set of software components. A specialized type of software frameworks are those specifically designed to support the composition of models or other components within a simulation system. Such frameworks are intended to simplify the process of assembling a complex model or simulation system from simpler component models as well as to promote the reuse of the component models. Several different types of software frameworks for model composition have been designed and implemented; the various framework types have different component types, processes for composing models, and intended applications. The different framework types and the implemented examples of them vary widely in terms of features and capabilities. Comparing alternative frameworks so as to assess their likely utility for a specific application has heretofore been largely subjective and qualitative, and consequently of uncertain reliability. To address this issue an assessment methodology specifically designed for comparing model composition frameworks is developed and explained. The methodology employs a quantitative metric based on a set of well-defined criteria relating to the features and capabilities of simulation frameworks and is intended to be objective and quantitative. The methodology is illustrated and demonstrated by applying it to a set of existing model composition frameworks. The assessment results suggest that using the methodology can, at a minimum, improve the objectivity and reliability of framework selection decisions. The assessment criteria may also be useful as guidelines when designing and developing a framework.

Model calibration is the procedure that adjusts the unknown parameters in order to fit the model to experimental data and improve predictive capability. However, it is difficult to implement the procedure because of the aleatory uncertainty. In this paper, a new method of model calibration based on uncertainty propagation is investigated. The calibration process is described as an optimization problem. A two-stage nested uncertainty propagation method is proposed to resolve this problem. Monte Carlo Simulation method is applied for the inner loop to propagate the aleatory uncertainty. Optimization method is applied for the outer loop to propagate the epistemic uncertainty. The optimization objective function is the consistency between the result of the inner loop and the experimental data. Thus, different consistency measurement methods for unary output and multivariate outputs are proposed as the optimization objective function. Finally, the thermal challenge problem is given to validate the reasonableness and effectiveness of the proposed method.

Model-Based Development has become an industry wide standard paradigm. As an open source alternative, Scilab/Xcos is being widely employed as a hybrid dynamic systems modeling tool. With the increasing efficiency in implementation using graphical model development and code generation, the modeling and simulation community is struggling with assuring quality as well as maintainability and extendibility. Refactoring is defined as an evolutionary modernization activity where, most of the time, the structure of the artifact is changed to alter its quality characteristics, while keeping its behavior unchanged. It has been widely established as a technique for textual programming languages to improve the code structure and quality. While refactoring is also regarded as one of the key practices of model engineering, the methodologies and approaches for model refactoring are still under development. Architecture-Driven Modernization (ADM) has been introduced by the software engineering community as a model-based approach to software modernization, in which the implicit information that lies in software artifacts is extracted to models and model transformations are applied for modernization tasks. Regarding refactoring as a low level modernization task, the practices from ADM are adaptable. Accordingly, this paper proposes a model-based approach for model refactoring in order to come up with more efficient and effective model refactoring methodology that is accessible and extendable by modelers. Like other graphical modeling tools, Scilab/Xcos also possesses a formalized model specification conforming to its implicit metamodel. Rather than proposing another metamodel for knowledge extraction, this pragmatic approach proposes to conduct in place model-to-model transformations for refactoring employing the Scilab/Xcos model specification. To construct a structured model-based approach, the implicit Scilab/Xcos metamodel is explicitly presented utilizing ECORE as a meta-metamodel. Then a practical model transformation approach is established based on Scilab scripting. A Scilab toolset is provided to the modeler for in-place model-to-model transformations. Using a sample case study, it is demonstrated that proposed model transformation functions in Scilab provide a valuable refactoring tool.

Modeling and simulation is pervasive throughout many different disciplines. As computing technology has provided more capability, the systems being modeled and simulated have grown larger and more complex. Often times, these large systems are managed as interacting subsystems. When it is necessary for the simulation to allow disparate subsystems to maintain their independence, then a hybrid model of the subsystems should be used. Furthermore, to ease the burden of verification and validation of simulation results, a proven system theoretical modeling specification should be used. However, many communities have already adopted nonsystem theoretical software solutions and established a group of domain experts familiar with these tools. This paper provides two things: a formal approach to building a hybrid model, and a discussion of how to incorporate a nonsystem theoretical software implementation into a proven framework. The first is done through the implementation of a Knowledge Interchange Broker (KIB) as an Interaction Model (IM). The second is accomplished by exemplifying the use of the IM in an agent-environment hybrid model. In the hybrid model, the agent is implemented in the Discrete-event System (DEVS) specification and the environment is implemented in the Geographical Resources Analysis Support System (GRASS) using a Composable Cellular Automaton (CCA) specification. This concept has been successfully applied to both example models and an interdisciplinary research project where the interactions between human activities and landscape processes are studied.

In this paper, we propose an approach to formally verify and rigorously validate a simulation system against the specification of the real system. We implement the approach in a verification and validation calculator tool that takes as input a set of statements that capture the requirements, internal conditions of the system and expected outputs of the real system and produces as output whether the simulation satisfies the requirements, faithfully represents the internal conditions of the system and produces the expected outputs. We provide a use case to show how subject matter experts can apply the tool.

In recent years, Cloud Computing has become popular to facilitate the use of Modeling and Simulation (M&S) resources. Nevertheless, there are still various issues to solve, including the structure constrain of chosen web service frameworks, the sharing of varied resources in the Cloud, and the difficulties in reproducing experiments. We show a new architecture based on Cloud Computing and new modeling methods to deal with these issues. This layered architecture, called Cloud Architecture for Modeling and Simulation as a Service (CAMSaaS), simplifies the deployment of M&S resources as services in the Cloud. CAMSaaS supports hierarchical resource services, experimental frameworks, scalable infrastructure and makes everything as a service. We deploy varied M&S resources as services in the Cloud, and build a Modeling and Simulation as a Service (MSaaS) middleware called CloudRISE to manage a variety of M&S resources. We also use the experimental framework concept to simplify the management of experiment environments. We present a case study for crowd evacuation application using the architecture.

We present HiLLS (High Level Language for System Specification), a graphical formalism that allows to specify Discrete Event System (DES) models for analysis using methodologies like simulation, formal methods and enactment. HiLLS’ syntax is built from the integration of concepts from System Theory and Software Engineering aided by simple concrete notations to describe the structural and behavioral aspects of DESs. This paper provides the syntax of HiLLS and its simulation semantics which is based on the Discrete Event System Specification (DEVS) formalism. From DEVS-based Modeling and Simulation (M&S) perspective, HiLLS is a platform-independent visual language with generic expressions that can serve as a front-end for most existing DEVS-based simulation environments with the aid of Model-Driven Engineering (MDE) techniques. It also suggests ways to fill some gaps in existing DEVS-based visual formalisms that inhibit complete specification of the behavior of complex DESs. We provide a case study to illustrate the core features of the language.

Air command and security (ACS) process is a kind of typical complex operation process. Its modeling issue has become a hot spot and challenge in the research field of complex operation process. Combining the demand on the application of air command and support, this paper proposes a simulation-based modeling method of integrating air command and support process, emphatically describing such aspects as the meta-model of the concept of aviation support process, work flow service modeling and arrangement of multi-aircraft process, etc. Finally, based on actual cases of application, this paper unfolds simulating verification and assessment on the whole process of control and support operation process of a flight task involving multiple aircrafts.