Claude Baron

SysPEM: a SysML and SPEM based process modelling language for systems engineering

The evolution of industry through a competitive environment resulted in a need for optimising the manufacturing processes. Many studies focalised on this topic, especially in the case of software engineering processes, which led to the definition of a standard for development processes (SPEM) by the OMG (Object Management Group) and a race to implement it through different languages and meta-models. Even if there is a huge similarity between software engineering and systems engineering, the standard and its different implementations are still insufficient to fulfil the requirements expressed through systems engineering standards such as EIA 632. In this paper a meta-model keeps the fruitful approach of the Software Process Engineering Meta-model (SPEM) combined with some useful concepts from the System Modelling Language (SysML). This model was validated through some predefined criteria, the specification of the EIA 632 standard and a sample process for systems engineering.

How to interconnect product design and project management including experience feedback and reusability requirements

Nowadays, the development of high quality and high performance products, with short time to market, represents a challenge for manufacturers. So, they must take into account, in the early steps of the product development, constraints that usually appear during the project management process. In this article, we define a process, based on the reuse of technical and nontechnical knowledge and experience feedback, that improves the communication between the design and the project management teams, in order to facilitate the emergence of collective solutions. After a brief presentation of the research objectives, this paper tackles the problem to closely connect design and management and suggests a methodology to associate the project tasks with the functionalities that the product has to implement.

Establishing Profiles for Systems Engineering Standards: A Great Help for Companies to Manage Their Processes

In this paper, we discuss how to establish profiles of Systems Engineering standards for companies.In order todefine anappropriate system engineering standard for a company, this paper presents a detailed comparison between the current releases of the main Systems Engineering standards(ANSI/EIA-632, ISO/IEC-15288 and IEEE-1220), and explains how to choose the most adapted one according to the company practices. When no standard completely corresponds, the paper suggests and illustrates how to elaborate a tailor-made standard on the basis of specific required characteristics of the companyor of theproject, followinga multi-standard approach, leading to to extend and adapt a standard by importing some elements from another.

System Engineering Method for System Design

The purpose of this chapter is to present some educational materials, the process and the outcomes to teach an engineering approach applied to a practical development case. The starting point is the requirements of an application of remote supervision of a room with several parameters: light, temperature and movement (intrusion into the room or movement of an object within the room). This application is based on wireless terminal nodes composed of a sensor, a microcontroller and a telecommunication module. Several rooms can be interconnected, so it must be possible to use the sensors of each room of a given site simultaneously. Various issues can be raised during teaching on wireless sensor networks (Kotzl & Essien, 2005): electronic design, risks to humans (Brownsell et al., 1999), energy management, telecommunication technologies, etc.

A FRAMEWORK FOR THE IMPROVEMENT OF COMBINATORIAL OPTIMIZATION: AN EXPERIENCE FEEDBACK APPROCH

Abstract The problem of selection of project scenarios in a multi-objective context is difficult to solve in an optimal manner. The idea proposed in this paper consists in using previous experiences in order to accelerate the search process for solutions and to limit a too important combinatorial explosion. A framework which allows re-using knowledge resulting from experience feedback is proposed in this paper. This knowledge is capitalized from experiences about previously planned projects in order to guide and to refine the search for new solutions. The method for selection of scenarios is based on an evolutionary algorithm. This algorithm is modified in order to allow the reuse of capitalized knowledge. The knowledge is generated and updated starting from the results of the evaluation of the scenarios by the evolutionary algorithm and/or from the analysis of the projects previously carried out. They are gathered in an influence diagram (extension of the Bayesian networks for the decision-making) allowing their reuse by the algorithm.

Interests of genetic algorithms to select and optimize scenarios in a system design process

This paper explores the interest and the possibility to join system design and project management methods and tools. Our motivation is to prevent the obvious incompatibilities between technical objectives and socio-economical requirements in the enterprise. What we recommend is to work on a generic unique model based on the classical top down design steps, to which costs models and non-functional requirements are associated. Project management thus appears as an activity of diagnosis and optimisation, allowing to choose certain realisations between the different possible scenarios and to optimise the management by an allocation of tolerances, which is calculated for each supplier on the base of a global objective. This analysis concludes on the interest of two complementary tools; the evolutionary algorithms to arbitrate the scenarios, and the Monte-Carlo methods for the allocation of tolerances.

Genetic algorithms for the identification of the generalised Erlang laws parameters used in systems dependability studies

In systems dependability modelling, the absence of a fine knowledge on the failure dynamics for certain systems and on the multiple interactions which exist between the various subsystems, and also the difficulty to validly use some simplifying assumptions require to resort with the exploitation of experience feedback. In addition, one has approximate models and, the problem is then to find the parameters of these models which satisfy “as well as possible” the observed feedback data, according to the principle of maximum of probability or minimum of least squares (it depends on the nature of the obtained data). Certain identification heuristics were hitherto used, but they showed their limits when, for instance, the relief of the function to be optimised presents many local valleys. These difficulties led us to consider an approach totally different where the transition rules can allow to avoid local cavities. For that, we studied a certain number of operational research techniques and finally chose a resolution by genetic algorithms. Their major advantage is that they operate the search of an optimum starting from a population and not from only one single point, allowing thus a parallel search, effective on the whole solutions space. After a thorough presentation of the considered applicability and the obtained results in this study, we underline in this communication the observed advantages, difficulties and limits compared to more traditional techniques for the parametric identification.