The impact of criteria in system architecture selection: observation from industrial experiment

Decisions related to system architecture are difficult, because of fuzziness and lack of information combined with often conflicting objectives. We organised an industrial workshop with the objective to choose 5 out of 800 architectures. The first step, the identification of selection criteria, proved to be the greatest challenge. As a result, designers selected system architectures that did not satisfy them without being able to explain what went wrong in their selection process. The objective of this study is to investigate the impact of criteria in system architecture selection. The recordings of the workshop were transcribed and analysed in order to identify the difficulties related to the definition and the use of criteria. The analysis highlights two issues: the interdisciplinarity of system architecture makes criteria interdependent and the lack of information is making it impossible to define an exhaustive set of criteria. This questions the applicability of most of design selection methods that assume that criteria are well defined by designers. Finally, this study provides insights and recommendations for future selection support tools dedicated to system architecture design

Vers une méthode d aide à la décision pour l intégration d innovations dès la conception préliminaire des architectures de systèmes complexes

L objectif de ce travail de recherche est de proposer une méthode d aide à l intégration d innovations dès la conception préliminaire des systèmes complexes. Cette étape de la conception a en effet de forts impacts sur le reste de cycle de vie du produit. En se focalisant sur l aide à la génération d architectures de système complexe, cette méthode utilise un réseau Bayésien combiné à un problème de satisfaction de contraintes (CSP) pour générer et évaluer automatiquement des architectures de systèmes complexes. Le modèle de réseau Bayésien proposé est utilisé pour représenter le problème de conception de l architecture en termes de variables de décision, de contraintes et de performances. Un algorithme parcourt le graphe ainsi défini afin de générer les solutions d architecture qui sont considérées comme faisables et qui présentent un niveau de confiance acceptable. Ce niveau de confiance estime l incertitude associée à chaque architecture générée. Les performances des architectures sont aussi calculées grâce au réseau bayésien. Une fois les architectures générées, un modèle de problème de satisfaction de contraintes permet d optimiser le placement des composants au vu des contraintes de placement et des objectifs d optimisation préalablement définis par les concepteurs. Un logiciel a été développé pour faciliter la modélisation du problème et la visualisation des solutions. Deux cas industriels ont permis de tester la méthode et de nombreuses solutions d architecture ont été générées. Afin de tester la faisabilité de l étape de sélection d architectures dans un cadre industriel, un atelier de sélection d architectures a été organisé afin d être par la suite analysé. Il a impliqué quatre concepteurs de Thales et portait sur un des cas industriels précédents. Cette dernière étude a souligné des difficultés dans la définition des critères de sélection des architectures et propose des recommandations pour un futur support à la sélection d architecture système.The aim of this research work is to propose a method allowing innovation integration in early design stages and supporting architecture design of complex systems that have significant implications for the rest of overall system life-cycle. Focusing on system architectures generation support, this method proposes to use Bayesian networks combined with Constraint Satisfaction Problem (CSP) techniques in order to semi-automatically generate and evaluate complex systems architectures. Bayesian network model is used to represent the design problem in terms of decision variables, constraints and performances. Furthermore, an architecture generation algorithm is proposed to generate feasible solutions and to cluster them with regard to a given confidence level threshold. This confidence level is representing the estimation of the uncertainty on the overall system. Estimation of architecture performances are also calculated within the Bayesian network. Once the system architectures are generated, a CSP model optimises the component placement regarding placement constraints and optimisation objectives defined by designers. Software has been developed for the purpose of problem modelling and solutions visualisation. Two industrial implementations yielded in a generation of a high number of architecture solutions. In order to test the feasibility of architecture selection in an industrial environment, a study was conducted integrating four system designers. This study underlined the difficulties in defining architecture selection criteria and provides recommendations for the future system architecture selection support.CHATENAY MALABRY-Ecole centrale (920192301) / SudocSudocFranceF

Vers une méthode d’aide à la décision pour l’intégration d’innovations dès la conception préliminaire des architectures de systèmes complexes

The aim of this research work is to propose a method allowing innovation integration in early design stages and supporting architecture design of complex systems that have significant implications for the rest of overall system life-cycle. Focusing on system architectures generation support, this method proposes to use Bayesian networks combined with Constraint Satisfaction Problem (CSP) techniques in order to semi-automatically generate and evaluate complex systems architectures. Bayesian network model is used to represent the design problem in terms of decision variables, constraints and performances. Furthermore, an architecture generation algorithm is proposed to generate feasible solutions and to cluster them with regard to a given confidence level threshold. This confidence level is representing the estimation of the uncertainty on the overall system. Estimation of architecture performances are also calculated within the Bayesian network. Once the system architectures are generated, a CSP model optimises the component placement regarding placement constraints and optimisation objectives defined by designers. Software has been developed for the purpose of problem modelling and solutions visualisation. Two industrial implementations yielded in a generation of a high number of architecture solutions. In order to test the feasibility of architecture selection in an industrial environment, a study was conducted integrating four system designers. This study underlined the difficulties in defining architecture selection criteria and provides recommendations for the future system architecture selection support.L’objectif de ce travail de recherche est de proposer une méthode d’aide à l’intégration d’innovations dès la conception préliminaire des systèmes complexes. Cette étape de la conception a en effet de forts impacts sur le reste de cycle de vie du produit. En se focalisant sur l’aide à la génération d’architectures de système complexe, cette méthode utilise un réseau Bayésien combiné à un problème de satisfaction de contraintes (CSP) pour générer et évaluer automatiquement des architectures de systèmes complexes. Le modèle de réseau Bayésien proposé est utilisé pour représenter le problème de conception de l’architecture en termes de variables de décision, de contraintes et de performances. Un algorithme parcourt le graphe ainsi défini afin de générer les solutions d’architecture qui sont considérées comme faisables et qui présentent un niveau de confiance acceptable. Ce niveau de confiance estime l’incertitude associée à chaque architecture générée. Les performances des architectures sont aussi calculées grâce au réseau bayésien. Une fois les architectures générées, un modèle de problème de satisfaction de contraintes permet d’optimiser le placement des composants au vu des contraintes de placement et des objectifs d’optimisation préalablement définis par les concepteurs. Un logiciel a été développé pour faciliter la modélisation du problème et la visualisation des solutions. Deux cas industriels ont permis de tester la méthode et de nombreuses solutions d’architecture ont été générées. Afin de tester la faisabilité de l’étape de sélection d’architectures dans un cadre industriel, un atelier de sélection d’architectures a été organisé afin d’être par la suite analysé. Il a impliqué quatre concepteurs de Thales et portait sur un des cas industriels précédents. Cette dernière étude a souligné des difficultés dans la définition des critères de sélection des architectures et propose des recommandations pour un futur support à la sélection d’architecture système

Eigenvector Rotation as an Estimation of Architectural Change

International audienceMany important technical innovations occur through changes to existing system architectures. To manage the balance between performance gains by the innovation and the risk of change, companies estimate the degree of architectural change an innovation option could cause due to change propagation throughout the entire system. To do so, they must evaluate the innovation options for their integration cost given the present system architecture. This article presents a new algorithm and metrics based upon eigenvector rotations of the architectural connectivity matrix to assess the sensitivity of a system architecture to introduced innovations, modelled as perturbations on the system. The article presents studies of the impact of changes on synthetic system architectures to validate the method. The results show that there is no single architecture that is the most amenable to introduced innovation. Properties such as the density of existing connections and the number of changes that modify intra- or inter-module connections can introduce global effects that are not known in advance. Hierarchical modular system architectures tend to be relatively stable to introduced innovations and distributed changes to any architecture tends to cause the largest eigenvector rotations

Proposition of Combined Approach for Architecture Generation Integrating Component Placement Optimization

International audienc

Toward System Architecture Generation and Performances Assessment Under Uncertainty Using Bayesian Networks

International audienceBackground: Architecture generation and evaluation are critical points in complex system design. Uncertainties concerning component characteristics and their impact onto overall system performance are often not taken into account in early design stages. In this paper, we propose a Bayesian Network approach for system architecture generation and evaluation.Approach: A method relying on Bayesian network templates is proposed in order to represent an architecture design problem integrating uncertainties concerning component characteristics and component compatibility. These templates aim at modeling designers’ knowledge concerning system architecture. We also propose an algorithm for architecture generation and evaluation related to the Bayesian network model with the objective of generating all possible architectures and filtering them in view to a defined confidence threshold. Within this algorithm, expert estimations on component compatibilities are used to estimate overall architecture uncertainty as a confidence level. Results: The proposed approach is tested and illustrated on a case study of bicycle design. This first case shows how uncertainties concerning component compatibilities and components characteristics impact bicycle architecture generation. The method is, additionally, tested and implemented in the case of a radar antenna cooling system design in industry. Results highlight the relevance of the proposed approach in view to the generated solutions as well as other benefits such as reduced time for architecture generation, and a better overall understanding of the design problem. However, some limitations have been observed and call for enhancements like integration of designer’s preferences and identification of possible trade-offs within the architecture. Conclusions: This method enables generation and evaluation of complex system architecture taking into account initial system requirements and designer’s knowledge. Its usability and added-value have been verified on a large-scale system implemented in industry