A prevalent and pervasive view of designing is that it can be modeled using variables and decisions made about what values should be taken by these variables. The activity of designing is carried out with the expectation that the designed artifact will operate in the natural world and the social world. These worlds impose constraints on the variables and their values; so, design could be described as a goal-oriented, constrained, decision- making activity. However, design distinguish- es itself from other similarly described activities not only by its domain but also by additional necessary features. Designing involves exploration, exploring what variables might be appropriate. The process of explo- ration involves both goal variables and deci- sion variables. In addition, designing involves learning: Part of the exploration activity is learning about emerging features as a design proceeds. Finally, design activity occurs within two contexts: the context within which the designer operates and the context produced by the developing design itself. The designer’s perception of what the context is affects the implication of the context on the design. The context shifts as the designer’s perceptions change. Design activity can be now characterized as a goal-oriented, con- strained, decision-making, exploration, and learning activity that operates within a con- text that depends on the designer’s percep- tion of the context.

Design Prototypes: A Knowledge Representation Schema for Design

Information and Communication Technologies in Tourism

Environment and Planning B: Planning and Design

The application of multi-attribute utility theory whose aggregation process is based on the Choquet integral requires the prior identification of a capacity. The main approaches to capacity identification proposed in the literature are reviewed and their advantages and inconveniences are discussed. All the reviewed methods have been implemented within the Kappalab R package. Their application is illustrated on a detailed example.

A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package

The Mechanical Design Process

In a bidding process, bidders have to define and estimate potential bid solutions relevant to the customer's requirements Afterward, based on several criteria (eg cost, due date), they have to select the most interesting solution to be sent as an offer to the customer However, the lack of complete and accurate information makes the estimation imprecise and uncertain In this paper, an approach is proposed to support the definition and the estimation of the potential Engineering To Order (ETO) technical bid solutions and the selection of the most interesting ones The definition and the estimation of the potential bid solutions is supported by a new knowledge-based configuration model whereas the selection of the most interesting solutions is supported by a new multi-criteria decision making approach that takes into account uncertainties and imprecisions

ETO bid solutions definition and selection using configuration models and a multi-criteria approach

This article proposes a model dealing with the synchronisation of system design process and project planning process This synchronisation is named synchronous coupling and is based on the assumption which states that a mapping exists between system Bill Of Materials and projects Work Breakdown Structure Synchronous coupling ensures a consistency in the monitoring and the control of the system design and project planning by imposing synchronous milestones during the project development The synchronous coupling is based on the definition of three states for each design and project entity following feasibility and verification criteria, and can thus manage the changes between states Some rules are defined (precedence and coupling rules) to forbid certain changes when they are inconsistent and to synchronize them

Synchronization of system design and project planning: Integrated model and rules

Coupling product configuration and process planning with constraints

This communication aims to associate the product configuration task with the planning of its production process in order to make consistent decisions while trying to minimize cost and cycle time. A two step approach is described with relevant aiding tools. During the first one, configuration and planning are considered as two constraint satisfaction problems and are interactively assisted by constraint propagation. The second one, thanks to a multi-criteria optimisation relying on a constrained evolutionary algorithm, proposes a set of solutions belonging to a Pareto front minimizing cost and cycle time to the user. After a problem introduction and a global description of the aiding system, the paper focuses on the optimisation process with interesting quantified results.

Using constraints filtering and evolutionary algorithms for interactive configuration and planning

The purpose of this paper is, on the one hand, to identify to define and classify customization requirements and, on the other hand, to evaluate how generic modeling and configuration assistance within the Constraint Satisfaction Problem (CSP) framework can fulfil the requirements. The aim is to provide commercial configurator knowledge base designers with constraint based generic modeling elements for customizable industrial product. A first part recalls the main trends of the configuration problem. In a second part divided in four sections corresponding with different requirement set; each section proposes a definition of the requirement set, some CSP based modeling elements and a discussion about adequacy of relevant configuration assistance techniques.

Michel Aldanondo

Papers

ETO bid solutions definition and selection using configuration models and a multi-criteria approach

Synchronization of system design and project planning: Integrated model and rules

Coupling product configuration and process planning with constraints

Using constraints filtering and evolutionary algorithms for interactive configuration and planning

Product generic modelling for configuration: requirement analysis and modelling elements