Factory modelling: data guidance for analysing production, utility and building architecture systems

Work on energy and resource reduction in factories is dependent on the availability of data. Typically, available sources are incomplete or inappropriate for direct use and manipulation is required. Identifying new improvement opportunities through simulation across factory production, utility and building architecture domains requires analysis of model feasibility, particularly in terms of system data composition, input resolution and simulation result fidelity. This paper reviews literature on developing appropriate model data for assessing energy and material flows at factory level. Gaps are found in guidance for analysis and integration of resource-flows across system boundaries. The process for how data was prepared, input and iteratively developed alongside conceptual and simulation models is described. The case of a large-scale UK manufacturer is presented alongside discussions on challenges associated with factory level modelling, and the insights gained from understanding the effect of data clarity on system performance

Panel on future challenges in modeling methodology

This panel paper presents the views of six researchers and practitioners of simulation modeling. Collectively we attempt to address a range of key future challenges to modeling methodology. It is hoped that the views of this paper, and the presentations made by the panelists at the 2004 Winter Simulation Conference will raise awareness and stimulate further discussion on the future of modeling methodology in areas such as modeling problems in business applications, human factors and geographically dispersed networks; rapid model development and maintenance; legacy modeling approaches; markup languages; virtual interactive process design and simulation; standards; and Grid computing

An Extended Interoperability Framework for Joint Composability

Interoperation of systems is defined by the aspects of integratability, interoperability, and composability. It is therefore needed, to address all levels of interoperation - from conceptual models via implemented systems to the supported infrastructure - accordingly in an interoperation framework. Several candidates are available and provide valuable part solution. This paper evaluates the Base Object Models (BOMs), Discrete Event Simulation Specifications (DEVS), Unified Language Model (UML) artifacts as used within the Test and Training Enabling Architecture (TENA), the Object-Process Methodology (OPM), and Conceptual Graphs (CG) regarding their contribution. Using the Levels of Conceptual Interoperability Model (LCIM), an extended interoperability framework based on the contributions of BOM, DEVS, UML/TENA, OPM, and CG will be proposed and gaps in support of joint composability are indentified

Toward composing variable structure models and their interfaces: a case of intensional coupling definitions

In this thesis, we investigate a combination of traditional component-based and variable structure modeling. The focus is on a structural consistent specification of couplings in modular, hierarchical models with a variable structure. For this, we exploitintensional definitions, as known from logic, and introduce a novel intensional coupling definition, which allows a concise yet expressive specification of complex communication and interaction patterns in static as well as variable structure models, without the need to worryabout structural consistency.In der Arbeit untersuchen wir ein Zusammenbringen von klassischer komponenten-basierter und variabler Strukturmodellierung. Der Fokus liegt dabei auf der Spezifikation von strukturkonsistenten Kopplungen in modular-hierarchischen Modellen mit einer variablen Struktur. Dafür nutzen wir intensionale Definitionen, wie sie aus der Logik bekannt sind, und führen ein neuartiges Konzept von intensionalen Kopplungen ein, welches kompakte gleichzeitig ausdrucksstarke Spezifikationen von komplexen Kommunikations- und Interaktionsmuster in statischen und variablen Strukturmodellen erlaubt

Semantic web service architecture for simulation model reuse

COTS simulation packages (CSPs) have proved popular in an industrial setting with a number of software vendors. In contrast, options for re-using existing models seem more limited. Re-use of simulation component models by collaborating organizations is restricted by the same semantic issues however that restrict the inter-organization use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontology to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development of ontology, connector software and web service discovery architecture in order to understand how such ontology are created, maintained and subsequently used for simulation model reuse. The ontology is extracted from health service simulation - comprising hospitals and the National Blood Service. The ontology engineering framework and discovery architecture provide a novel approach to inter- organization simulation, uncovering domain semantics and adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community

Simulation Software as a Service and Service-Oriented Simulation Experiment

Simulation software is being increasingly used in various domains for system analysis and/or behavior prediction. Traditionally, researchers and field experts need to have access to the computers that host the simulation software to do simulation experiments. With recent advances in cloud computing and Software as a Service (SaaS), a new paradigm is emerging where simulation software is used as services that are composed with others and dynamically influence each other for service-oriented simulation experiment on the Internet. The new service-oriented paradigm brings new research challenges in composing multiple simulation services in a meaningful and correct way for simulation experiments. To systematically support simulation software as a service (SimSaaS) and service-oriented simulation experiment, we propose a layered framework that includes five layers: an infrastructure layer, a simulation execution engine layer, a simulation service layer, a simulation experiment layer and finally a graphical user interface layer. Within this layered framework, we provide a specification for both simulation experiment and the involved individual simulation services. Such a formal specification is useful in order to support systematic compositions of simulation services as well as automatic deployment of composed services for carrying out simulation experiments. Built on this specification, we identify the issue of mismatch of time granularity and event granularity in composing simulation services at the pragmatic level, and develop four types of granularity handling agents to be associated with the couplings between services. The ultimate goal is to achieve standard and automated approaches for simulation service composition in the emerging service-oriented computing environment. Finally, to achieve more efficient service-oriented simulation, we develop a profile-based partitioning method that exploits a system’s dynamic behavior and uses it as a profile to guide the spatial partitioning for more efficient parallel simulation. We develop the work in this dissertation within the application context of wildfire spread simulation, and demonstrate the effectiveness of our work based on this application

Models, Composability, and Validity

Composability is the capability to select and assemble simulation components in various combinations into simulation systems to satisfy specific user requirements. The defining characteristic of composability is the ability to combine and recombine components into different simulation systems for different purposes. The ability to compose simulation systems from repositories of reusable components has been a highly sought after goal among modeling and simulation developers. The expected benefits of robust, general composability include reduced simulation development cost and time, increased validity and reliability of simulation results, and increased involvement of simulation users in the process. Consequently, composability is an active research area, with both software engineering and theoretical approaches being developed. Composability exists in two forms, syntactic and semantic (also known as engineering and modeling). Syntactic composability is the implementation of components so that they can be connected. Semantic composability answers the question of whether the models implemented in the composition can be meaningfully composed

Towards Self-Adaptive Discrete Event Simulation (SADES)

Systems that benefit from the ongoing use of simulation, often require considerable input by the modeller(s) to update and maintain the models. This paper proposes automating the evolution of the modelling process for discrete event simulation (DES) and therefore limiting the majority of the human modeller’s input to the development of the model. This mode of practice could be named Self-Adaptive Discrete Event Simulation (SADES). The research is driven from ideas emerging from simulation model reuse, automations in the modelling process, real time simulation, dynamic data driven application systems, autonomic computing and self-adaptive software systems. This paper explores some of the areas that could inform the development of SADES and proposes a modified version of the MAPE-K feedback control loop as a potential process. The expected outcome from developing SADES would be a simulation environment that is self-managing and more responsive to the analytical needs of real systems