System simulation by SEMoLa

SEMoLa is a platform, developed at DISA since 1992, for system knowledge integration and modelling. It allows to create computer models for dynamic systems and to manage different types of information. It is formed by several parts, each dealing with different forms of knowledge, in an integrated way: a graphical user interface (GUI), a declarative language for modelling, a set of commands with a procedural scripting language, a specific editor with code highlighting (SemEdit), a visual modelling application (SemDraw), a data base management system (SemData), plotting data capabilities (SemPlot), a raster maps management system (SemGrid), a large library of random number generators for uncertainty analysis, support for fuzzy logic expert systems, a neural networks builder and various statistical tools (basic statistics, multiple and non-linear regression, moving statistics, etc.). The core part of the platform is the declarative modelling language (SEMoLa; simple, easy to use, modelling language). It relies on System Dynamics principles and uses an integrated view to represent dynamic systems through different modelling approaches (state/individual-based, continuous/discrete, deterministic/stochastic) without requiring specific programming skills. SEMoLa language is based on a ontology closer to human reasoning rather than computer logic and constitutes also a paradigm for knowledge management. SEMoLa platform permits to simplify the routinely tasks of creating, debugging, evaluating and deploying computer simulation models but also to create user libraries of script commands. It is able to communicate with other frameworks exchanging - with standard formats - data, modules and model components

Development, test and comparison of two Multiple Criteria Decision Analysis(MCDA) models: A case of healthcare infrastructure location

When planning a new development, location decisions have always been a major issue. This paper examines and compares two modelling methods used to inform a healthcare infrastructure location decision. Two Multiple Criteria Decision Analysis (MCDA) models were developed to support the optimisation of this decision-making process, within a National Health Service (NHS) organisation, in the UK. The proposed model structure is based on seven criteria (environment and safety, size, total cost, accessibility, design, risks and population profile) and 28 sub-criteria. First, Evidential Reasoning (ER) was used to solve the model, then, the processes and results were compared with the Analytical Hierarchy Process (AHP). It was established that using ER or AHP led to the same solutions. However, the scores between the alternatives were significantly different; which impacted the stakeholders‟ decision-making. As the processes differ according to the model selected, ER or AHP, it is relevant to establish the practical and managerial implications for selecting one model or the other and providing evidence of which models best fit this specific environment. To achieve an optimum operational decision it is argued, in this study, that the most transparent and robust framework is achieved by merging ER process with the pair-wise comparison, an element of AHP. This paper makes a defined contribution by developing and examining the use of MCDA models, to rationalise new healthcare infrastructure location, with the proposed model to be used for future decision. Moreover, very few studies comparing different MCDA techniques were found, this study results enable practitioners to consider even further the modelling characteristics to ensure the development of a reliable framework, even if this means applying a hybrid approach

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version