Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context

A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA

Coupling system design and project planning: discussion on a bijective link between system and project structures

This article discuss the architecture of an integrated model able to support the coupling between a system design process and a project planning process. The project planning process is in charge of defining, planning and controlling the system design project. A benchmarking analysis carried out with fifteen companies belonging to the world competitiveness cluster, Aerospace Valley, has highlighted a lack of models, processes and tools for aiding the interactions between the two environments. We define the coupling as the establishment of links between entities of the two domains while preserving their original semantic, thus allowing information to be collected. The proposed coupling is recursive. It enables systems to be decomposed into subsystems when designers consider complexity to be too high, and can also decompose projects into sub-projects. The coupling enables systematically links to be drawn between project entities and system entities. In this paper, we discuss the different possibilities of linking system and project structures during the design and the planning processes. Firstly, after presenting the results of the industrial analysis, the different entities are defined and the various coupling modes are discussed

Technic and Collaboration Breakdown Structures: Drivers of collaborative problem solving approaches in a supply chain context

Problem Solving Methodologies have been par excellence a cornerstone element of the firms’ strategy on achieving effective continuous improvement. But the enterprise evolution towards an extended environment characterized by network-based organization has radically changed the problem solving paradigms. This paper aims to propose a generic and collaborative methodology addressing more complex and distributed problems, dealing with Supply Chain issues and having a key role as a driver for building global competitive advantages and create superior performances at a Supply Chain level

Proposition d’une architecture composée de multiples processus de retour d’expérience coopérants

Cet article présente les premiers résultats d’une étude réalisée en partenariat avec l’entreprise Turbomeca traitant des problèmes engendrés par l’implémentation de processus de retour d’expérience dans une entreprise étendue. La première partie de l’article est dédiée à la définition et à la description des processus de retour d’expérience et des approches les plus avancées pour faciliter leur implémentation. Dans une seconde partie, nous montrons que dans une entreprise étendue, il est nécessaire de définir de multiples processus de retour d’expérience pour que l’approche soit adaptée aux différents niveaux de décisions et aux différents produits/technologies utilisés dans l’entreprise. Nous proposons une trame générale pour intégrer ces différents aspects et nous présentons une illustration d’un cas concret. Finalement, nous concluons sur l’originalité de notre proposition ses avantages et les perspectives de notre travail

How to take into account general and contextual knowledge for interactive aiding design: Towards the coupling of CSP and CBR approaches

The goal of this paper is to show how it is possible to support design decisions with two different tools relying on two kinds of knowledge: case-based reasoning operating with contextual knowledge embodied in past cases and constraint filtering that operates with general knowledge formalized using constraints. Our goals are, firstly to make an overview of existing works that analyses the various ways to associate these two kinds of aiding tools essentially in a sequential way. Secondly, we propose an approach that allows us to use them simultaneously in order to assist design decisions with these two kinds of knowledge. The paper is organized as follows. In the first section, we define the goal of the paper and recall the background of case-based reasoning and constraint filtering. In the second section, the industrial problem which led us to consider these two kinds of knowledge is presented. In the third section, an overview of the various possibilities of using these two aiding decision tools in a sequential way is drawn up. In the fourth section, we propose an approach that allows us to use both aiding decision tools in a simultaneous and iterative way according to the availability of knowledge. An example dealing with helicopter maintenance illustrates our proposals

Improvement of retrieval in Case-Based Reasoning for system design

The problematic addressed in this article is dealing with the improvement of retrieval in Case-Based Reasoning for system design. The retrieval activity is based on the evaluation of similarities between requirements (target) and the solutions (sources). However, similarities between features is often a subjective kind of knowledge difficult to formalize within companies. Based on an ontology of domain, the approach permits to retrieve compatible solutions rather than similar ones using a model of designer preferences. The requirements are modeled by means of constraints. When constraints are confronted to solutions in order to evaluate a compatibility measure, missing information within solutions with regard to requirements are taken into account using semantic similarities between concepts. A case study validates the proposals

Configuration knowledge modeling: How to extend configuration from assemble/make to order towards engineer to order for the bidding process

The bidding process is one of the most important phases for system contractors. A successful bid implies defining and implementing attractive and realistic systems solutions that fulfil customer expectations. An additional challenge arises with the increase in systems diversity resulting from growing customization needs. As a result, for standard customizing offers, bidders find good quality support with configuration software for assemble/make-to-order situations. But when requirements exceed the standard offers, bidders need extended support to fulfil Engineering-to-Order requirements. In this context, this article shows how configuration knowledge models, which support configuration in assemble/make-to-order situations (AMTO), can be extended and used in engineer-to-order situations (ETO). Modeling is achieved assuming that the configuration problem is considered as a constraint satisfaction problem. Six key requirements that differentiate ETO from AMTO are identified and modeling extensions are proposed and discussed. An example illustrates all the contributions

Readiness, feasibility and confidence: how to help bidders to better develop and assess their offers

In a bidding process, the bidder must define and evaluate potential offers in order to propose the most suitable one to the potential customer. Proposing attractive but also realistic offers to various potential customers is a key factor for the bidder to stay competitive. In order to achieve this, the bidder needs to be very sure about the technical specifications and the constructability of the proposal. However, performing a detailed design is resource and time-consuming. This article proposes the foundation of a new framework which can help bidders to define the right offer: (i) in the context of a non-routine design process, while avoiding a detailed design and (ii) taking into account two new indicators that reflect the bidder’s confidence that they can meet the commitments once the offer is accepted. The first indicator (OCS) characterises the Overall Confidence in the technical System, while the second one (OCP) gives the Overall Confidence in the delivery Process. Both OCS and OCP are based firstly on two factual objective indicators, Technology Readiness Level (TRL) for OCS and Activity Feasibility Level (AFL) for OCP, and secondly on two human-based subjective indicators, Confidence In System (CIS) for the OCS and Confidence In Process for the OCP. An illustrative application shows how this framework can really help bidders define an offer, while avoiding detailed design and enable them to evaluate the confidence level in each potential offe

Collaborative methodology for supply chain quality management: framework and integration with strategic decision processes in product development

The new generation of network-based organizations has triggered the emergence of distributed and more complex contexts for the analysis of firms’ strategies. This gradual change in the way we understand enterprises has induced radical evolutions on the Quality Management domain. As a consequence, the Problem Solving Methodologies (PSM)widely used in industry and positioned up to now as one of the key elements for achieving continuous improvement efforts within local scopes are now insufficient to deal with major and distributed problems and requirements in this new environment. The definition of a generic and collaborative PSM well-adapted to supply chain contexts is one of the purposes of this paper. Additional requirements linked to specificities carried out by the introduction of a networked context within the methodology scope, the relational aspects of the supply chains, complexity and distribution of information, distributed decision-making processes and knowledge management challenges are some of the aspects being addressed by the proposed methodology. A special focus is made on benefits obtained through the integration of those elements across all problem-solving phases and particularly a proposal for multi-level root-cause analysis articulating both horizontal and vertical decision processes of supply chains is presented. In addition to laying out the expected benefits of such a methodology in the Quality Management area, the article studies the reuse of all the quality-related evidence capitalized in series phase as a driver for improving upstream phases of product development projects. This paper addresses this link between series and development activities in light of the proposed PSM and intends to encourage discussion on the definition of new approaches for Quality Management throughout the whole product life cycle. Some enabling elements in the decision-making processes linked to both the problem-solving in series phase and the roll-out of new products are introduced

Hyperbolicity Computation through Dominating Sets

International audienceHyperbolicity is a graph parameter related to how much a graph resembles a tree with respect to distances. Its computation is challenging as the main approaches consist in scanning all quadruples of the graph or using fast matrix multiplication as building block, both are not practical for large graphs. In this paper, we propose and evaluate an approach that uses a hierarchy of distance-k dominating sets to reduce the search space. This technique, compared to the previous best practical algorithms, enables us to compute the hyperbolicity of graphs with unprecedented size (up to a million nodes) and speeds up the computation of previously attainable graphs by up to 3 orders of magnitude while reducing the memory consumption by up to more than a factor of 23