Iterchanging Discrete Event Simulationprocess Interaction Modelsusing The Web Ontology Language - Owl

Discrete event simulation development requires significant investments in time and resources. Descriptions of discrete event simulation models are associated with world views, including the process interaction orientation. Historically, these models have been encoded using high-level programming languages or special purpose, typically vendor-specific, simulation languages. These approaches complicate simulation model reuse and interchange. The current document-centric World Wide Web is evolving into a Semantic Web that communicates information using ontologies. The Web Ontology Language OWL, was used to encode a Process Interaction Modeling Ontology for Discrete Event Simulations (PIMODES). The PIMODES ontology was developed using ontology engineering processes. Software was developed to demonstrate the feasibility of interchanging models from commercial simulation packages using PIMODES as an intermediate representation. The purpose of PIMODES is to provide a vendor-neutral open representation to support model interchange. Model interchange enables reuse and provides an opportunity to improve simulation quality, reduce development costs, and reduce development times

Discrete-Event Simulation Data Transformation: A Model-Driven Data Integration Approach

Achieving a smooth production system is a complex process that requires the use of commercial discrete event simulation (DES) tools to provide a high flexibility production process, for instance the use of simulation modelling to model a production system. These tools require high levels of cooperation to work together because they are not designed to be integrated and hardly share their data. This research aims to integrate DES tools applied by different manufacturing systems in order to enable them to share their data. This thesis presents data integration from a simulation model point of view because it views data integration between different DES tools models as key steps towards system integration. A new approach has been developed which is called a Model-Driven Data Integration Approach (MDDI), so named because the integration involves the combination of data from different DES tools model sources. The effectiveness of this data integration approach has been demonstrated in a case study undertaken for DES design of a phone production line in the manufacturing industry. However, the application of the MDDI is not limited to this case study: it can also be used for other system and applications. The MDDI approach was tested and evaluated on the basis of this case study. These test cases simulated how the data integration based on different DES tools’ models react to the process of data sharing as they occur in the manufacturing production line. The result is that the MDDI approach best maintains data consistency and integrity and can be adopted by different industries

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

Design of adaptable simulation models.

In today's world, with ever increasing competition, modelling and simulation proves to be a very helpful tool. Many methodologies exist to help build a simulation model from scratch. In terms of adaptability, most current attempts focus on either the operational side, ie the automated integration of data into a model, or the creation of new software. However, very few attempts are being made to improve the adaptability of shelved models built in existing simulation software. As a result, there is a certain reluctance, in some areas, to use simulation to its full potential.Based on these facts, it is obvious that anything, which makes reuse of simulation models easier, can help improve the use and spread of simulation as a valuable tool to maintain a company's competitiveness. In order to find such a solution, the following issues are looked at in this thesis: The changes to a simulation model that constitute the biggest problem, ways to minimise those changes, and possibilities to simplify the implementation of those changes. Those factors are evaluated, first by investigating current practices of building adaptable simulation models via a literature review, then the most difficult changes to implement in a simulation model, and the most frequent types of simulation software, are identified by means of interviews and questionnaire surveys. Next, parameters describing the adaptability of a simulation model are defined. In a further step, two of the most widely used simulation packages are benchmarked against a variety of tasks, reflecting the changes most frequent to models. The benchmarking study also serves to define and test certain elements regarding their suitability for adaptable models. Based on all those steps, model building guidelines for the creation of adaptable simulation models are developed and then validated by means of interviews and a framed field experiment. The interviews and questionnaire reveal that deleting is the easiest task and modifying the most complicated, while handling devices are the most difficult element to modify. The results also show that simulators (eg Arena) are the most widespread type of simulation software. The benchmarking showed that Arena is overall more adaptable than Simul8, and confirms the findings from the user survey. Also, it shows that sequencing is very helpful for modifying models, while the use of sub-models decrease the adaptability. Finally, the validation proves that the model building guidelines substantially increase the adaptability of models