An object-oriented application framework for building water resource information and planning tools applied to the design of a flood analysis system

An object-oriented framework for building computer based modelling tools for water resource planning is presented. The focus is on building a program for flood calculations in river systems with several reservoirs and water transfer structures. The foundation for the flood model is a general application framework for building hydrological modelling tools. The general framework provides the user with tools for describing the structural components of the hydrological system, their relation in the system topology and controlling the behaviour of the system during simulation. Hydrological models are often data intensive, and the framework is equipped with tools to handle both time series and spatially-distributed data efficiently. During the development, effort has been put into supporting future changes and extensions to the model system, as well as creating sound reusable components that will benefit future development and maintenance. A flood modelling application in the Norwegian river Gudbrandsdalslågen is described to illustrate the use of the toolkit.     

Tarsier and ICMS: two approaches to framework development

Modelling frameworks provide models with support components that handle tasks such as visualisation, data management and model integration. Within these broad requirements different approaches to framework development are possible. Tarsier is a modelling framework that supports the development of models in a high-level language, such as C++. This approach allows Tarsier model developers to craft object oriented solutions to large modelling problems. ICMS is a software system that supports the development of models in a custom modelling language that allows modellers with little programming experience to develop, integrate and visualise catchment models. Both frameworks provide sophisticated tools for model linking, data management, and data analysis and visualisation. By focusing on different user groups, Tarsier and ICMS have evolved into quite different environments, yet both satisfy the definition of a modelling framework. This paper concentrates on the components within each framework and the strengths and weaknesses of the different approaches.     

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.     

A Framework for Integrating Non-Functional Requirements into Conceptual Models

The development of complex information systems calls for conceptual models that describe aspects beyond entities and activities. In particular, recent research has pointed out that conceptual models need to model goals, in order to capture the intentions which underlie complex situations within an organisational context. This paper focuses on one class of goals, namely non-functional requirements (NFR), which need to be captured and analysed from the very early phases of the software development process. The paper presents a framework for integrating NFRs into the ER and OO models. This framework has been validated by two case studies, one of which is very large. The results of the case studies suggest that goal modelling during early phases can lead to a more productive and complete modelling activity.       

Reusing knowledge in embedded systems modelling

Model‐based design is a promising technique to improve the quality of software and the efficiency of the software development process. We are investigating how to efficiently model embedded software and its environment to verify the requirements for the system controlled by the software. The software environment consists of mechanical, electrical and other parts; modelling it involves learning how these parts work, deciding what is relevant to model and how to model it. It is not possible to fully automate these steps. There are general guidelines, but given that every modelling problem differs, much is left to the modeller's own preference, background and experience. Still, when the next generation of a system is designed, the new system will have common elements with its previous version. Therefore, lessons learned from the current model could inform future models. We propose a framework for identifying the non‐formal elements of knowledge, insights and a model itself, which can support modelling of the next system generation. We will present the application of our framework on an action research case – modelling mechanical parts of a paper‐inserting machine.     

Systems dynamics modelling of a manufacturing supply chain system

Supply chains are multifaceted structures focusing on the integration of all the factors involved in the overall process of production and distribution of end products to the customers. Growing interest in supply chain systems has highlighted the need to adopt appropriate approaches that can ensure the efficient management of their complexity, enormity and broadness of scope. With the main aim of supply chain management being to optimise the performance of supply chains, attention is mainly drawn to the development of modelling frameworks that can be utilised to analyse and comprehend the dynamic behaviour of supply chains. While there have been only a few supply chain modelling attempts reported in the literature, this paper proposes a modelling framework that is used to simulate the operation of a supply chain network of moderate complexity. The proposed model comprises four echelons and is build around a central medium-sized manufacturing company operating as a typical Make-to-Order (MTO) system. The developed model was built using a systems dynamics (SD) approach. The operations performed within a supply chain are a function of a great number of key variables which often seem to have strong interrelationships. The ability of understanding the network as a whole, analysing the interactions between the various components of the integrated system and eventually supplying feedback without de-composing it make systems dynamics an ideal methodology for modelling supply chain networks. The objective of the paper is to model the operation of the supply chain network under study and obtain a true reflection of its behaviour. The modelling framework is also used to study the performance of the system under the initial conditions considered and compare it with that obtained by running the system under eight different scenarios concerning commonly addressed real-life operational conditions. The modelling effort has focused on measuring the supply chain system performance in terms of key metrics such as inventory, WIP levels, backlogged orders and customer satisfaction at all four echelons. The study concludes with the analysis of the obtained results and the conclusions drawn from contrasting the system’s performance under each investigated scenario to that of the benchmark model.     

Inference for grouped data with a truncated skew-Laplace distribution

The skew-Laplace distribution has been used for modelling particle size with point observations. In reality, the observations are truncated and grouped (rounded). This must be formally taken into account for accurate modelling, and it is shown how this leads to convenient closed-form expressions for the likelihood in this model. In a Bayesian framework, “noninformative” benchmark priors, which only require the choice of a single scalar prior hyperparameter, are specified. Conditions for the existence of the posterior distribution are derived when rounding and various forms of truncation are considered. The main application focus is on modelling microbiological data obtained with flow cytometry. However, the model is also applied to data often used to illustrate other skewed distributions, and it is shown that our modelling compares favourably with the popular skew-Student models. Further examples with simulated data illustrate the wide applicability of the model.     

Reusability of model components for environmental simulation – Case studies for integrated coastal zone management

Model constructs in environmental models are seldom reused beyond the project lifetime or in other modelling studies. A library of reusable model components could facilitate the maintenance of existing models and make the design of new models more efficient. Although component-based design is the common standard in software engineering and manufacturing few examples are yet found in environmental science. The multi-disciplinary project SPICOSA used a common, component-based simulation framework for environmental modelling, based on 18 case studies through Europe. The development of high-quality model components with potential for reuse turned out to be a challenge despite of the guidelines and tutorial examples provided. Well-designed components are of appropriate granularity, encapsulated, with a limited use of connectors and proper data handling. Ultimately, the success of a model library depends on a sufficient set of quality components with complementary functionalities, a framework for quality control, and support of the environmental modelling community.     

A Framework for Improving the Requirements Engineering Process Management

This paper presents a system dynamics model for improving the requirements engineering process management. The paper argues that improving RE process management improves the quality of the specification produced. It uses a simulation modelling approach to capture the complex and dynamic nature of quality and also the cost of resources and time needed to complete the process. Current claims by various researchers and empirical evidence has led to our proposition that “the earlier in the requirements engineering phase that system dynamics simulation modelling is used, the more effective the RE process management is and the better its product quality will be.” In developing such a model, the paper fills an important gap in the RE process management literature and has potential to provide requirement engineers, managers and software development organisations with a model-based process framework to aid quality assessment and improvement. The paper concludes by suggesting that the framework makes a useful contribution both in providing the foundations for theory building in RE process management and quality improvement by aiding shared understanding through learning and training situations. This revised version was published online in August 2006 with corrections to the Cover Date.     

Toward a methodological framework for agent-based modelling and simulation of supply chains in a mass customization context

In a dynamic customer-centric supply chain context, classic forecasting models turn out to have a limited applicability. In order to estimate the key performance indices of these Supply Chains and to facilitate their management, it is necessary to use more elaborate tools such as a simulation. However building simulation of customer-centric supply chains is no trivial matter. It requires the elaboration of a representative model and the execution of this model according to a set of hypotheses associated to scenarios. Due to their properties, Multi-Agent Systems seem particularly well suited for the modelling and the simulation of Supply Chains and more especially in a mass customization context. In this paper we propose an agent modelling framework for the modelling and simulation of such Supply Chains to facilitate their management. We show how this framework can be applied to a case of customer-centric Supply Chain from the golf club industry and we present an experiment plan associated.     

Modelling Off-the-Shelf Information Systems Requirements: An Ontological Approach

Requirements for choosing off-the-shelf information systems (OISR) differ from requirements for development of new information systems in that they do not necessarily provide complete specifications, thus allowing flexibility in matching an existing IS to the stated needs. We present a framework for OISR conceptual models that consists of four essential elements: business processes, business rules, information objects and required system services. We formalise the definitions of these concepts based on an ontological model. The ontology-based OISR model provides a framework to evaluate modelling languages on how appropriate they are for OISR requirements specifications. The evaluation framework is applied to the Object-Process Methodology, and its results are compared with a similar evaluation of ARIS. This comparison demonstrates the effectiveness of the ontological framework for evaluating modelling tools on how well they can guide selection, implementation and integration of purchased software packages.     

An ontological process modelling framework for stochastic systems

Most process modelling techniques exist without any firm theoretical foundation. This results in a lack of model validation, which can be in-terms of model consistency, feasibility and goal compliance. Moreover, these techniques are mostly deterministic in nature and not applicable to stochastic systems. In this paper, we propose an ontology-based stochastic process modelling framework that further provides a specialization to failure and reliability issues. The framework is notation independent, and is primarily rooted in Bunge’s ontology. The well-established theory of reliability constructs are also mapped to facilitate the modelling of failure prone systems.     

Epsilon Flock: a model migration language

Model-driven engineering introduces additional challenges for controlling and managing software evolution. Today, tools exist for generating model editors and for managing models with transformation, validation, merging and weaving. There is limited support, however, for model migration—a development activity in which instance models are updated in response to metamodel evolution. In this paper, we propose conservative copy—a style of model transformation that we believe is well-suited to model migration—and Epsilon Flock—a compact model-to-model transformation language tailored for model migration. The proposed structures are evaluated by comparing the conciseness of model migration strategies written in different styles of transformation language, using several examples of evolution taken from UML and the graphical modelling framework.     

Simplifying environmental model reuse

The environmental modelling community has developed many models with varying levels of complexity and functionality. Many of these have overlapping problem domains, have very similar ‘science’ and yet are not compatible with each other. The modelling community recognises the benefits to model exchange and reuse, but often it is perceived to be easier to (re)create a new model than to take an existing one and adapt it to new needs.Many of these third party models have been incorporated into the Agricultural Production Systems Simulator (APSIM), a farming systems modelling framework. Some of the issues encountered during this process were system boundary issues (the functional boundary between models and sub-models), mixed programming languages, differences in data semantics, intellectual property and ownership.This paper looks at these difficulties and how they were overcome. It explores some software development techniques that facilitated the process and discusses some guidelines that can not only make this process simpler but also move models towards framework independence.     

A geospatial framework to support integrated biogeochemical modelling in the United Kingdom

Anthropogenic impacts on the aquatic environment, especially in the context of nutrients, provide a major challenge for water resource management. The heterogeneous nature of policy relevant management units (e.g. catchments), in terms of environmental controls on nutrient source and transport, leads to the need for holistic management. However, current strategies are limited by current understanding and knowledge that is transferable between spatial scales and landscape typologies. This study presents a spatially-explicit framework to support the modelling of nutrients from land to water, encompassing environmental and spatial complexities. The framework recognises nine homogeneous landscape units, distinct in terms of sensitivity of nutrient losses to waterbodies. The functionality of the framework is demonstrated by supporting an exemplar nutrient model, applied within the Environmental Virtual Observatory pilot (EVOp) cloud cyber-infrastructure. We demonstrate scope for the use of the framework as a management decision support tool and for further development of integrated biogeochemical modelling.