Knowledge-based environment for investigating multicomputer architectures

Multicomputers for massively parallel processing will eventually employ billions of processing elements, each of which will be capable of communicating with every other processing element. A knowledge-based modelling and simulation environment (KBMSE) for investigating such multicomputer architecture at a discrete-event system level is described. The KBMSE implements the discrete-event system specification (DEVS) formalism in an object-oriented programming system of Scheme (a dialect), which supports building models in a hierarchical, modular manner, a systems-oriented approach not possible in conventional simulation languages. The paper presents a framework for knowledge-based modelling and simulation by exemplifying modelling a hypercube multicomputer architecture in the KBMSE. The KBMSE has been tested on a variety of domains characterized by complex, hierarchical structures such as advanced multicomputer architectures, local area computer networks, intelligent multi-robot organizations, and biologically based life-support systems.     

The pragmatic quality of Resources‐ Events‐Agents diagrams: an experimental evaluation

The Resources‐Events‐Agents (REA) model is a semantic data model for the development of enterprise information systems. Although this model has been proposed as a benchmark for enterprise information modelling, only few studies have attempted to empirically validate the claimed benefits of REA modelling. Moreover, these studies focused on the evaluation of REA‐based system implementations rather than directly assessing the REA‐modelled conceptual schemas that these systems are based on. This paper presents a laboratory experiment that measured the user understanding of diagrammatic conceptual schemas developed using the REA model. The theoretical foundation for the hypotheses are cognitive theories that explain pattern recognition phenomena and the resulting reduction in cognitive effort for understanding conceptual schemas. The results of the experiment indicate a more accurate understanding of the business processes and policies modelled when users recognize the REA model’s core pattern of enterprise information in the diagram. The implication for modelling practice is that the use of the REA model improves the requirements engineering process by facilitating the user validation of conceptual schemas produced by analysts, and thus helps ensuring the quality of the enterprise information system that is developed or implemented.     

Discrete-event modelling of fire spreading

We deal here with the application of discrete-event System Specification (DEVS) formalism to implement a semi-physical fire spread model. Currently, models from physics finely representing forest fires are not efficient and still under development. If current softwares are devoted to the simulation of simple models of fire spread, nowadays there is no environment allowing us to model and simulate complex physical models of fire spread. Simulation models of such a type of models require being easily designed, modified and efficient in terms of execution time. DEVS formalism can be used to deal with these problems. This formalism enables the association of object-oriented hierarchical modelling with discrete-event techniques. Object-oriented hierarchical programming facilitates construction, maintenance and reusability of the simulation model. Discrete-events reduce the calculation domain to the active cells of the propagation domain (the heated ones).     

Industrial cost modelling and multi-objective optimisation for decision support in production systems development

Recent developments in cost modelling, simulation-based multi-objective optimisation, and post-optimality analysis have enabled the integration of costing data and cost estimation into a new methodology for supporting economically sound decision-making in manufacturing enterprises. Within this methodology, the combination of production engineering and financial data with multi-objective optimisation and post-optimality analysis has been proven to provide the essential information to facilitate knowledge-driven decision-making in real-world production systems development. The focus of this paper is to present the incremental cost modelling technique specifically designed for the integration with discrete-event simulation models and multi-objective optimisation within this methodology. A complete example, using the simulation model and data modified from a previous real-world case study, is provided in this paper to illustrate how the methodology and cost modelling are applied for the optimal investment decision support.     

Pseudo-fuzzy discrete-event simulation for on-line production control

In this paper we propose an alternative use of commercial object-oriented discrete-event simulators. We attempt to bridge the intrinsic inaccuracy of simulators in modelling real systems affected by fuzziness. The strategy adopted, called pseudo-fuzzy discrete event simulation, models fuzziness through a set of several classic simulation runs to trace an output fuzzy performance function. The idea behind the approach proposed is to use the simulator as a fuzzy operator, which embeds some stochastic functions.A benchmark industrial setting has been used to build a reference simulation model and perform evaluations of the simulation strategy proposed for a specific working case.     

Agent-Based Simulation of Animal Behaviour

In the biological literature on animal behaviour, in addition to real experiments and field studies, also simulation experiments are a useful source of progress. Often specific mathematical modelling techniques are adopted and directly implemented in a programming language. Modelling more complex agent behaviours is less adequate using the usually adopted mathematical modelling techniques. The literature on AI and Agent Technology offers more specific methods to design and implement (also more complex) intelligent agents and agent societies on a conceptual level. One of these methods is the compositional multi-agent system design method DESIRE. In this paper it is shown how (depending on the complexity of the required behaviour) a simulation model for animal behaviour can be designed at a conceptual level in an agent-based manner. Different models are shown for different types of behaviour, varying from purely reactive behaviour to pro-active, social and adaptive behaviour. The compositional design method for multi-agent systems DESIRE and its software environment supports the conceptual and detailed design, and execution of these models. A number of experiments reported in the literature on animal behaviour have been simulated for different agent models.     

A framework for linking Soft Systems Methodology (SSM) and Jackson System Development (JSD)

Abstract. This paper describes a proposed framework for linking soft systems methodology (SSM), a problem structuring technique for use in messy, ill-defined problem situations, with Jackson System Development (JSD), an information system development methodology which has object-oriented characteristics. The approach taken has been to embed the modelling phase of JSD within SSM after the conceptual modelling stage, but before the debate on desirable and feasible change. The JSD modelling phase is carried out at a conceptual rather than a real world level. The framework comprises of a series of steps which enable JSD entity structure diagrams to be derived to a large extent from the activities in an SSM conceptual model. A data model may then be built up by exploding the activities in the conceptual model or by using 'conceptual hows' to identify more entities and actions. One of the main benefits expected to arise from the use of such a framework is an improved user requirement definition, which is important to successful systems implementation. The difficulties in actually achieving a link relate to the methodological and philosophical differences between SSM and JSD.     

Optimisation of construction site layout using a hybrid simulation-based system

The layout of temporary facilities for a construction project greatly impacts project performance as demonstrated in many prior studies. This paper demonstrates how the site layout process can be automated for specific types of construction. Utility tunnel construction was selected as the specialty area, for which a partially automated site layout application was built. This approach integrates general purpose simulation for modelling space, logistics and resource dynamics with genetic algorithms for optimizing the layout based on various constraints and rules. We achieve this by implementing a site layout optimisation system within a simulation environment which already models tunnel construction processes. The new modelling strategy provides a medium for seamless integration between the resulting site layout and the discrete event production simulation models. The paper describes the approach taken and the system developed, presenting a practical case study from a tunnelling project in Edmonton, Canada to illustrate the system’s performance and its validation.     

Data-driven modeling and simulation framework for material handling systems in coal mines

In coal mining industry, discrete-event simulation has been widely used to support decisions in material handling system (MHS) to achieve premiums on revenues. However, the conventional simulation modeling approach requires extensive expertise of simulation during the modeling phase and lacks flexibility when the MHS structure changes. In this paper, a data-driven modeling and simulation framework is developed for MHS of coal mines to automatically generate a discrete-event simulation model based on current MHS structural and operational data. To this end, a formal information model based on Unified Modeling Language (UML) is first developed to provide MHS structural information for simulation model generation, production information for simulation execution, and output requirement information for defining simulation outputs. Then, Petri net-based model generation procedures are designed and used to automatically generate a simulation model in Arena® based on the simulation inputs conforming to the constructed information model. The proposed framework is demonstrated for one of the largest open-pit coal mines in the USA, and it has been demonstrated that the framework can be used to effectively generate the simulation models that precisely represent MHS of coal mines, and then be used to support various decisions in coal mining such as equipment scheduling.     

CHEOPS: A tool-integration platform for chemical process modelling and simulation

A large number of modelling tools exist for the construction and solution of mathematical models of chemical processes. Each (chemical) process modelling tool provides its own model representation and model definition functions as well as its own solution algorithms, which are used for performing computer-aided studies for the process under consideration. However, in order to support reusability of existing models and to allow for the combined use of different modelling tools for the study of complex processes, model integration is needed. This paper presents a concept for an integration platform that allows for the integration of modelling tools, combining their models to build up a process model and performing computer-aided studies based on this integrated process model. In order to illustrate the concept without getting into complicated algorithmic issues, we focus on steady-state simulation using models comprising only algebraic equations. The concept is realized in the component-based integration platform CHEOPS, which focuses on integrating and solving existing models rather than providing its own modelling capabilities.     

某航空兵场站机载弹药保障仿真研究

航空兵场站机载弹药保障的要素和环节众多,且相互影响、相互制约,通常采用计算机仿真对这类系统效能进行定量评估。本文对某航空兵场站机载弹药保障的力量构成、保障过程和特点进行了分析,并将其抽象为一个离散事件系统,建立了涵盖任务下达与执行、保障对象、保障资源以及保障过程等要素的建制单位离散事件仿真模型,并在此基础上开发了仿真系统。应用表明,该仿真模型运行正确、可靠,为某航空兵场站机载弹药保障效能评估提供了有力的分析工具。     

An open framework for agent based modelling of agricultural land use change

There is growing interest in creating empirically grounded agent based models (ABMs) to simulate land use change at a variety of spatio-temporal scales. The development of land use change models is challenging, as there is a need to connect representations of human behavioural processes to simulations of the biophysical environment. This paper presents a new agent-based modelling framework (Aporia) that has the goal of reducing the complexity and difficulty of constructing high-fidelity land use models. Building on earlier conceptual developments for modelling land use change and the provision of ecosystem services, Aporia was designed to be modular, flexible and open, using a declarative, compositional approach to create complex models from subcomponents. The framework can be tightly or loosely coupled with multiple vegetation models, it can be set up to evaluate a range of ecosystem service indicators, and it can be calibrated for a range of different landscape-scale case studies and modelling styles. The framework is released under an Open Source licence, and can be freely re-used and modified to form the basis of new models. We illustrate this with two case studies implemented using Aporia, exploring different socio-economic scenarios and behavioural characteristics on the land use decisions of Swiss and Scottish farmers. We also discuss the benefits of frameworks in terms of their flexibility, expandability, verification and transparency.     

Language-measure-theoretic optimal control of probabilistic finite-state systems

Supervisory control theory for discrete event systems, introduced by Ramadge and Wonham, is based on a non-probabilistic formal language framework. However, models for physical processes inherently involve modelling errors and noise-corrupted observations, implying that any practical finite-state approximation would require consideration of event occurrence probabilities. Building on the concept of signed real measure of regular languages, this paper formulates a comprehensive theory for optimal control of finite-state probabilistic processes. It is shown that the resulting discrete-event supervisor is optimal in the sense of elementwise maximizing the renormalized langauge measure vector for the controlled plant behaviour and is efficiently computable. The theoretical results are validated through several examples including the simulation of an engineering problem.     

Best practices for conceptual modelling in environmental planning and management

Conceptual modelling is used in many fields with a varying degree of formality. In environmental applications, conceptual models are used to express relationships, explore and test ideas, check inference and causality, identify knowledge and data gaps, synchronize mental models and build consensus, and to highlight key or dominant processes. Due to their sometimes apparent simplicity, development and use of a conceptual model is often an attractive option when tackling an environmental problem situation. However, we have experienced many examples where conceptual modelling has failed to effectively assist in the resolution of environmental problems. This paper explores development and application of conceptual modelling to environmental problems, and identifies a range of best practices for environmental scientists and managers that include considerations of stakeholder participation and trust, model development and representation, integration of different and disparate conceptual models, model maturation, testing, and transition to application within the problem situation.     

Discrete-event models of quantized systems for diagnosis

The paper concerns the model-based diagnosis of continuous-variable systems whose state can only be measured through a quantizer. The diagnosis is based on the investigation whether the observed discrete input and output sequences are consistent with a discrete-event model of the quantized system. The paper describes a necessary and sufficient condition for the discrete-event model to be suitable for diagnosis. This condition is independent of the specific model form and of the diagnostic algorithm applied to the model. Within a hierarchy of models with increasing accuracy all of which satisfy this modelling requirement the quality of the diagnostic result increases. The results are illustrated by an example.     

A Discrete-Event Neural Network Simulator for General Neuron Models

Efficient simulation techniques for a discrete-event pulsed neural network simulator are developed. In a discrete-event simulation framework, simulation of complex neural behaviours, such as phase precession and phase arbitration, demands the prediction of delayed firing times. The new technique, the incremental partitioning method, uses linear envelopes of the state variable of a neuron to partition the simulated time so that the delayed-firing time is reliably calculated by applying the bisection-combined Newton-Raphson method to every partition. The quick filtering technique is also proposed for reducing calculation cost of linear envelopes. The simulator developed, Punnets, has achieved efficiency and precision, but is still capable of simulating a complex behaviour of large-scale neural network models.     

Phenomenologial foundations of conceptual product modelling in architecture,engineering and construction

The ultimate goal of conceptual modelling in architecture, engineering and construction (AEC) has been to define the data structures that could be used to describe the entire built environment through all its life cycle stages — from inception and design to demolition. In spite of the magnitude and complexity of this task, the theoretical foundations of modelling received little attention. In this paper, the theoretical foundations of the traditional modelling approaches are questioned using phenomenology and hermeneutics as philosophical base. The author exposes the difference between the remodelling of some existing models, the modelling of physical objects and the modelling of psychical, intentional objects. The author concludes that AEC or building product and process models do not model objective reality but the modeller's partial understanding of that reality. Therefore, several correct but different models may and should exist. Future software architectures in AEC should not be built on a unified, centralized model but, on a combination of models, which may not be standardised but whose schemas are encoded in a standard manner.