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The use of Agent-Based Models (ABMs) to make predictions in real-time is hindered by their high computation cost and the lack of detailed individual data. This paper proposes a new framework to enable the use of emulators, also referred to as surrogate models or meta-models, coupled with ABMs, to allow for real-time predictions of the behaviour of a complex system. The case study is that of pedestrian movements through an environment. We evaluate two different types of emulators: a regression emulator based on a Random Forest and a time-series emulator using a Long Short-Term Memory neural network. Both emulators perform well, but the time-series emulator proves to generalise better to cases where the number of agents in the system is not known a priori. The results have implications for the real-time modelling of human crowds, suggesting that emulation is a feasible approach to modelling crowds in real-time, where computational complexity prohibits the use of an ABM directly. [ABSTRACT FROM AUTHOR]

This paper looks at the tension between the desire to claim predictive ability for Agent-Based Models (ABMs) and its extreme difficulty for social and ecological systems, suggesting that this is the main cause for the continuance of a rhetoric of prediction that is at odds with what is achievable. Following others, it recommends that it is better to avoid giving the impression of predictive ability until this has been iteratively and independently verified, due to the danger of suggesting more than is empirically warranted, especially in non-modellers. It notes that there is a restricted and technical context where prediction is useful, that of meta-modelling – when we are trying to explain and understand our own simulation models. If one is going to claim prediction, then a lot more care needs to be taken, implying minimal standards in practice and transparent honesty about the empirical track record – the over-enthusiastic claiming of prediction in casual ways needs to cease. [ABSTRACT FROM AUTHOR]

During the last 30 years, enterprise modelling has been recognised as an efficient tool to externalise the knowledge of companies in order to understand their operations, to analyse their running and to design new systems from several points of view: functions, processes, decisions, resources and information technology. This paper aims at describing the long evolution of enterprise modelling techniques as well as one of the future challenges of these techniques: the transformation of enterprise models. So, in a first part, the paper describes the evolution of enterprise modelling techniques from the divergence era to the convergence period. In a second time, the paper focuses on the recent advances in the use of enterprise models through model-driven approaches, interoperability problem-solving and simulation, all these advances having the same characteristic to use the transformation of enterprise models. [ABSTRACT FROM AUTHOR]

Experimental optimization of physical and biological processes is a difficult task. To address this, sequential surrogate models combined with search algorithms have been employed to solve nonlinear high-dimensional design problems with expensive objective function evaluations. In this article, a hybrid surrogate framework was built to learn the optimal parameters of a diverse set of simulated design problems meant to represent real-world physical and biological processes in both dimensionality and nonlinearity. The framework uses a hybrid radial basis function/genetic algorithm with dynamic coordinate search response, utilizing the strengths of both algorithms. The new hybrid method performs at least as well as its constituent algorithms in 19 of 20 high-dimensional test functions, making it a very practical surrogate framework for a wide variety of optimization design problems. Experiments also show that the hybrid framework can be improved even more when optimizing processes with simulated noise. [ABSTRACT FROM AUTHOR]

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature. [ABSTRACT FROM AUTHOR]

Despite significant advances in Clinical Decision Support Systems, they have not been extensively used in nursing practice to date. One key problem is the failure of these systems to fully support actionable nursing practices that guide nurse decision-making. In addition, current workflow-related systems have failed to consider the specific workflow challenges associated with acute-care nursing. In response to these challenges, we describe a novel three-stage approach that builds and evaluates a meta-model that addresses key requirements of multi-level guideline-based clinical nursing-specific decision support. This research-in-progress presents the first two stages of this approach, highlighting the importance of meta-modelling as a tool to identify the essential system-centric information that underpins acute-care nursing practice. [ABSTRACT FROM PUBLISHER]

In this paper, crushing characteristics of small-sized conical tubes called miniature frusta under axial loading have been studied. Finite-element model is developed using non-linear explicit code LS_DYNA to investigate the non-symmetrical fold patterns as well as material and geometrical non-linearity of frusta. Numerical simulation is first validated by confirming the results using experimental test data. Effects of shell thickness, semi-epical angle of cone and frusta's length on energy absorption characteristics are then studied by carrying out a parametric study. Based on the crushing parameters of numerous examples, a simplified analytical model as a meta-model for the mean crushing force of miniature frusta is presented using a Genetic Algorithm optimization for finding the meta-model coefficients. Miniature frusta show promising behaviour in lightweight design and crash analysis as their response results in low peak force and efficient-specific energy absorption. Obtained results from the developed meta-model showed good concurrence with finite elements method (FEM) model. [ABSTRACT FROM AUTHOR]

As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations. [ABSTRACT FROM PUBLISHER]

Global optimization techniques have been used extensively due to their capability in handling complex engineering problems. In addition to a number of well known global optimization techniques, many new methods have been introduced recently for various optimal design applications. In this work, a number of representative, well known and recently introduced global optimization techniques are closely examined and compared. The historical development, special features and trends on the development of global optimization algorithms are reviewed. Special attention is devoted to the recent developments of multidisciplinary design optimization algorithms based on effective metamodelling techniques. Commonly used benchmark optimization problems are used as test examples to reveal the pros and cons of these global optimization methods. A new meta-model based global optimization search method, introduced and improved recently by the authors, is also included in the tests and comparison. [ABSTRACT FROM AUTHOR]

Fierce competition in today's economy forces companies to fully optimize their processes in order to supply customers with high-quality products on time with lowest possible cost. Designing optimal production lines is a major step ahead in satisfying customer needs. Owing to the stochastic and highly nonlinear nature of the production lines, their optimal design is not easy and requires usage of advanced tools and techniques. In the present paper one of the new generation soft computing technique that is known as gene expression programming (GEP) is used to develop a meta-model from extensive simulation experiments for the multiple objective design of a production line. The developed meta-model is used to optimize production line design with multiple objective tabu search algorithm (MOTS). It is found out that GEP and MOTS can be effectively used to model and solve production line design problems. [ABSTRACT FROM AUTHOR]