A tutorial on simulation conceptual modeling

© 2017 IEEE. Conceptual modeling is the abstraction of a simulation model from the part of the real world it is representing; in other words, choosing what to model, and what not to model. This is generally agreed to be the most difficult, least understood, but probably the most important activity to be carried out in a simulation study. In this tutorial we explore the definition, requirements and approach to conceptual modeling. First we ask 'where is the model?' We go on to define the term 'conceptual model', to identify the artefacts of conceptual modeling, and to discuss the purpose and benefits of a conceptual model. In so doing we identify the role of conceptual modeling in the simulation project life-cycle. The discussion then focuses on the requirements of a conceptual model, the approaches for documenting a conceptual model, and frameworks for guiding the conceptual modeling activity. One specific framework is described and illustrated in more detail. The tutorial concludes with a discussion on the level of abstraction

Hybrid agent-based and social force simulation for modelling human evacuation egress

Simulation has become one of the popular techniques to model evacuation scenarios. Simulation is used as an instrumental for examining human movement during both normal and emergencies such as evacuation. During an evacuation, people will be in a panic situation and egress behaviour that will find the way out from a dangerous place to a safe place. Two well-known techniques in simulation that can incorporate human behaviour inside the simulation models are Agent-Based Simulation and Social Force Simulation. ABS is using the concept of a multi-agent system that consists of decentralized agents which can be autonomous, responsive and proactive. Meanwhile, SFS is a physical force to drive humans dynamically to perform egress actions and human self-organised behaviour in a group. However, the main issue in modelling both ABS or SFS alone is due to their characteristic as ABS have difficulty in modelling the force element and collective behaviours while SFS does not focus on free movements during the evacuation. This behaviour was due to the structure of humans (agents) inside ABS is decentralized which resulting collision among agents and the desired formation of evacuation was not achieved. On the other hand, in a single SFS model, the human was not proactive in finding the way out which was not reflecting the actual behaviour of humans during the evacuation. Both ABS and SFS are potential techniques to be combined due to their characteristics of self-learning and free movement in ABS and self organization in SFS. The research methodology based on modelling and simulation (M&S) life-cycle has been utilized for this work; consists of three main phases, namely preliminary study, model development and validation and verification and finally the experimentation and the results analysis. The M&S life-cycle was utilized aligned with the research aim which is to investigate the performance of the combined ABS and SFS in modelling the egress behaviour during evacuation. To achieve the aim, five evacuation factors have been chosen namely obstacles, the number of exits, exit width, triggered alarm time, and the number of people that have been the most chosen factors in the literature review. Next, three simulation models (using techniques: SFS, ABS and hybrid ABS/SFS) have been developed, verified, and validated based on the real case study data. Various simulation scenarios that will influence the human evacuation movement based on the evacuation factors were modelled and analysed. The simulation results were compared based on the chosen performance measurement parameters (PMP): evacuation time, velocity, flow rate, density and simulation time (model execution time). The simulation results analysis revealed that SFS, ABS, and hybrid ABS/ SFS were found suitable to model evacuation egress (EE) based on the reported PMP. The smallest standard error (SSE) values reported 66% for hybrid ABS/ SFS, 17% for ABS and 17% for SFS where the highest percentage of SSE indicated the most accurate. Based on the experiment results, the hybrid ABS/ SFS revealed a better performance with high effectiveness and accuracy in the simulation model behaviour when modelling various evacuation egress scenarios compared to single ABS and SFS. Thus, hybrid ABS/ SFS was found the most appropriate technique for modelling EE as agents in the hybrid technique were communicating to each other by forming a decentralised control for smooth and safe EE movement. In addition, the impactful factors that affected the result accuracy were exits, the exit width (size), the obstacle and the number of people. Conclusively, this thesis contributed the hybrid ABS/ SFS model for modelling human behaviour during evacuation in a closed area such as an office building to the body of knowledge. Hence, this research was found significant to assist the practitioners and researchers to study the closer representation of human EE behaviour by considering the hybrid ABS/SFS model and the impactful factors of evacuation

Data Technology in Materials Modelling

This open access book discusses advances in semantic interoperability for materials modelling, aiming at integrating data obtained from different methods and sources into common frameworks, and facilitating the development of platforms where simulation services in computational molecular engineering can be provided as well as coupled and linked to each other in a standardized and reliable way. The Virtual Materials Marketplace (VIMMP), which is open to all service providers and clients, provides a framework for offering and accessing such services, assisting the uptake of novel modelling and simulation approaches by SMEs, consultants, and industrial R&D end users. Semantic assets presented include the EngMeta metadata schema for research data infrastructures in simulation-based engineering and the collection of ontologies from VIMMP, including the ontology for simulation, modelling, and optimization (OSMO) and the VIMMP software ontology (VISO)

Design, development and usability evaluation of social system interface and development of computational model

In recent times, methods of computational intelligence (CI) that aim to solve real-life problems are developed by computer science researchers in collaboration with domain experts. There has also been an increased emphasis on the usability aspect of these algorithms by developing easy-to-use web interfaces. The graphical user interfaces (GUIs) designed for these algorithms are often designed solely to connect the web interfaces to the algorithm’s functionality. While this is effective from researchers’ perspective, the needs of new users (such as policymakers) in relation to software use are often neglected. The lack of consideration of new users’ experience when developing GUIs often establishes usability issues for the technology and as a result expands the gap between the advances made in the computer science field and other fields, most notably the social sciences. This thesis investigates the various design, development, and evaluation methods for social simulation software and provides valuable insights for researchers and user interface designers who seek to create an effective GUI. Additionally, this thesis provides a case study of how computational models can be effectively applied for approaching complex social problems such as homelessness. In chapter 3 the development and testing process of the Homelessness Visualization (HOMVIZ) platform is discussed. The HOMVIZ platform uses a deep learning algorithm in order to predict potential trends in homeless populations in a particular area of interest. Various aspects of the user interface (UI) design were analyzed and a 14 participant usability testing session was conducted in order to discern the perceived usability of the platform. The UI evaluation session in this chapter involved software testing, focus groups, and questionnaires. These sessions provided our research with valuable qualitative and quantitative data. Chapter 4 explores moderated and unmoderated usability testing sessions and compares them in terms of efficiency, reliability, and flexibility. The research for this chapter was approved by the Lakehead University’s Research Ethics Board. The usability testing was conducted with a sample size of 72 participants. The research presented in this chapter provides valuable insight regarding different usability testing session methods and the impact of a known phenomenon called careless responding (CR) on data quality. Chapter 5 provides an example of how computational models can help mitigate a more complex social problem such as homelessness. The research presented in this chapter focuses on the operation of homeless shelters within Canada and introduces eight computation models that have the potential to improve the quality of life of people experiencing homelessness