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

Traffic Multiresolution Modeling and Consistency Analysis of Urban Expressway Based on Asynchronous Integration Strategy

The paper studies multiresolution traffic flow simulation model of urban expressway. Firstly, compared with two-level hybrid model, three-level multiresolution hybrid model has been chosen. Then, multiresolution simulation framework and integration strategies are introduced. Thirdly, the paper proposes an urban expressway multiresolution traffic simulation model by asynchronous integration strategy based on Set Theory, which includes three submodels: macromodel, mesomodel, and micromodel. After that, the applicable conditions and derivation process of the three submodels are discussed in detail. In addition, in order to simulate and evaluate the multiresolution model, “simple simulation scenario” of North-South Elevated Expressway in Shanghai has been established. The simulation results showed the following. (1) Volume-density relationships of three submodels are unanimous with detector data. (2) When traffic density is high, macromodel has a high precision and smaller error and the dispersion of results is smaller. Compared with macromodel, simulation accuracies of micromodel and mesomodel are lower but errors are bigger. (3) Multiresolution model can simulate characteristics of traffic flow, capture traffic wave, and keep the consistency of traffic state transition. Finally, the results showed that the novel multiresolution model can have higher simulation accuracy and it is feasible and effective in the real traffic simulation scenario

OOPM/RT: A Multimodeling Methodology for Real-Time Simulation

ion Tree) which organizes the multimodels based on the abstraction relationship to facilitate the optimal model selection process, and 3) Selection of the optimal model which guarantees to deliver simulation results by the given amount of time. A more detailed model (low abstraction model) is selected when we have enough time to simulate, while a less detailed model (high abstraction model) is selected when the deadline is immediate. The basic idea of selection is to trade structural information for a faster runtime while minimizing the loss of behavioral information. We propose two possible approaches for the selection: integer programming based-approach and search-based approach. By systematically handling simulation deadlines while minimizing the modeler's interventions, OOPM/RT provides an efficient modeling environment for real-time systems. Categories and Subject Descriptors: I.6.5 [Simulation and Modeling]: Model Development General Terms: Modeling Methodology, Real-Time Simulat..

Methodology to develop hybrid simulation/emulation model.

Trends towards reduced life-time of products and globalised competition has increased pressure on manufacturing industries to be more responsive to changing needs of product markets. Consequently, the use of simulation to describe short term future performance of manufacturing system has become more significant than ever. An application of simulation that has attracted attention is for testing of control logic before commissioning on site by using a detailed simulation model called emulation model. However, though the success of using emulation particularly in improving cost-effectiveness of automated material handling system delivery has been acknowledged by industries and simulation model developers, the uptake for this technology is still low. The major inhibitors are the high costs of its model building as well as simulation and emulation models are perceived to be non convertible.The main objective, of this research is to establish a methodology to develop simulation model that can be converted into emulation model with ease, thus making emulation technology more affordable. The product of this research called the methodology to build Hybrid Simulation Emulation Model (HSEM) is a new approach of building emulation model comprising of three phases namely (1) development of base simulation model, (2) development of detail emulation model, and (3) integration of controller with the emulation model. Important requirements for HSEM are flexibility of adding details to the simulation model and inter process communication between model and real control system. To facilitate implementation of the methodology, it is essential that the simulation software package provide functionalities for modular model development, access and adding of codes, integration with other application and real time (RT) modelling.The methodology developed offers a more affordable emulation modelling and an opening for further research into the comprehensive support for the implementation of real time control system testing using emulation

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains