Patrice Leclaire

Modelling and Simulation of the Doctors’ Availability in Emergency Department with SIMIO Software. Case of Study: Bichat-Claude Bernard Hospital

Emergency Departments (ED) require an appropriate allocation of human and material resources in order to increase their effectiveness and efficiency and reduce as much as possible the patients’ waiting time. This chapter describes step by step the patient’s stay process at the ED. The SIMIO software was used for the modelling and simulation of this process. This chapter also presents a novel method of modelling doctors’ availability in the emergency department taking into account their number and availability in trauma and medicine areas. The findings from the different simulated scenarios show that modifying the doctors’ number can have a strong effect on the patients’ length of stay and the number of exited patients from the service. This work was based on the ED at Bichat Claude Bernard Hospital in Paris, France.

Change propagation prediction: A formal model for two-dimensional geometrical models of products

A product is redesigned by integrating structural or functional modifications to satisfy new needs or requirements. Iterations are also necessary to fine-tune product definition during the initial design process. In both cases, engineering changes are the means by which the product evolves. However, changes are propagated from component to components and from function to functions, leading to nonvalue development activities with extra costs and delays. These engineering changes may have harmful impacts on industrialization and manufacturing; they can drastically hinder efforts to ensure faster time-to-market. Research works currently seek to model, predict, analyze, and assess the impacts of engineering changes and their propagations. This article focuses on changes in a two-dimensional geometrical product model and proposes a new change prediction approach based on two steps. First, the dependencies between the parameters of structural components are identified through experiments. These dependencies are characterized qualitatively, quantitatively, and by formal equations. The changes are then propagated through the network of dependencies, and their consequences are computed. A geometric two-dimensional model of a bicycle is used to illustrate the approach. The results are discussed, and the main contributions of the approach are highlighted while further research paths are presented at the end of this article.

Capitalizing Data, Information and Knowledge on Mechanical Experiments through Ontologies

Experiments in a laboratory or a company, either physical or virtual, generate a large amount of data and information (raw data, consolidated results, experimental conditions, etc.) that has to be managed in order to preserve and enhance the knowledge and the expertise of the organization, especially when turnover happens. If simulation data management has been especially explored and standardized (STEP AP 209 for instance), experimental data did not create the same interests and only standard data management tools are proposed. In this paper, we propose an ontology to capitalize such information and knowledge, with an efficient reuse objective. This ontology, for which specific taxonomies have been defined according to the relevant literature of the domain, is illustrated on a set of data on previous experimental campaigns, corresponding to two kinds of mechanical experiments: tribology tests and material characterization tests.

Structuring systems and related research challenges

Abstract Structuring systems are the infrastructures that provide services to customers and end-users during a very long period of time, often several generations. They include water supply, telecommunication, transport and energy, highly interdependent and intertwined. This paper aims at giving the main characteristics of these systems. We give a precise definition of these systems, suggest a threefold process of their lifecycle, extract some of their principles and propose a general framework for their study. Finally, we discuss the niche research areas where the existing methods and tools still fail to attack.

Engineering Change Management (ECM) Methods: Classification According to Their Dependency Models

Engineering Change Management methods lead to assess impact of engineering changes and evaluate their propagation effects. In this paper, we propose a novel classification of Engineering Change Management methods according to their dependency model: (i) theoretical dependency model and (ii) a posteriori dependency model. A dependency model is needed to assess the change effect on one parameter after changing the value of another one. Three reference methods are presented here in details to explain the differences between these two types of dependency models and their usability according to designer decisions. The comparison shows that a theoretical model is generally a deterministic model that request a lot of knowledge and expertise in different areas. Besides an a posteriori model is based on probabilistic data and uses simple techniques and tools.

Complex systems renewal: positioning, concepts and architectural issues

We look at understanding the main mechanisms behind the renewal of complex systems. These systems, such as trains or airplanes, have a long operation or service duration. They should be renewed and their renewal projects are often partial for economic reasons. A renewal act is performed through integration of engineering changes. Any functional or structural change could have cascading effects on consecutive subsystems. The system architecture can be used as the main driver of renewal acts definition and planning. We position the renewal problem and define some key concepts usable for complex systems renewal. They allow to define the renewal possibilities as scenarios that should be qualified for ultimate selection. This paper explores these mechanisms and suggests a first set of propagation mechanisms. Throughout the paper, we illustrate our purposes by using examples from a train renewal project.

Challenges in Knowledge Management for Structuring Systems

Structuring Systems can be defined as systems, designed, installed, used and maintained within their operational environment for an extremely long duration, answering to the fundamental needs of the society. Examples of such systems are water systems, transport or electricity network. Due to their nature, these systems are characterized to be highly complex, according to the general system theory. In this context, the classical Knowledge Management (KM) approaches are not suited, due to the nature and origins of some data, information and knowledge, the complexity of partner networks that are interacting around the systems, the evolution history of such systems, etc. In this article, we present the main challenges for KM in Structuring Systems, especially in terms of knowledge traceability and heritage from one hand, and knowledge preservation at another one.

A Co-evolution Oriented Change Analysis Framework in Product Development Project

Nowadays, companies are facing more and more challenges during Product Development (PD) project. On one hand, the PD project should design and develop the product to satisfy the requirements as far as possible under time, cost and quality constraints. On the other hand, the PD project is expected to deal with any possible modification in an agile and flexible way. Therefore, how to manage changes under various restrictions becomes an essential issue during a PD project. The aim of this paper is to provide some exploratory results concerning modeling change propagations during PD projects to support companies to improve their change management performance. We first propose a Co-evolution Oriented Change Analysis (COCA) framework. The framework enables us to simultaneously model product management, project management, and partnership management knowledge areas as well as the interrelations between them. In the framework, we propose a product evolution model manifesting itself with a serial of states reflecting how the information/data is aggregated along the project process. Relying on the product evolution model as well as the aggregation of information/data from the multiple knowledge areas, we suggest a method of building up a change propagation network. Within the network, we identify and summarize a set of change propagation patterns. Through analyzing the characteristics of the patterns, we suggest some guidelines as a theoretical contribution to change management.