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Paul Pitiot

Associate Professor
Present : YES
Bureau 0A07
Keywords : 
Research Axes : 
  • Axis 1 : ORKID


  • Concurrent product configuration and process planning: Some optimization experimental results

    References :
    Paul Pitiot, Michel Aldanondo, and Élise Vareilles. « Concurrent product configuration and process planning: Some optimization experimental results ». In: Computers in Industry 65.4 (May 2014). pp. 610--621. ISSN: 0166-3615. DOI: 10.1016/j.compind.2014.01.012.
    In nowadays industrial competition, optimizing concurrently the configured product and the planning of its production process becomes a key issue in order to achieve mass customization development. However, if many studies have addressed these two problems separately, very few have considered them concurrently. We therefore consider in this article a multi-criteria optimization problem that follows an interactive configuration and planning process. The configuration and planning problems are considered as constraint satisfaction problems (CSPs). After some recalls about this two-step approach, we propose to evaluate a recent evolutionary optimization algorithm called CFB-EA (for constraint filtering based evolutionary algorithm). CFB-EA, specially designed to handle constrained problems, is compared with an exact branch and bound approach on small problem instances and with another evolutionary approach carefully selected for larger instances. Various experiments, with solutions spaces up to 1017, permit us to conclude that CFB-EA sounds very promising for the concurrent optimization of a configured product and its production process. (C) 2014 Elsevier B.V. All rights reserved.
    Keywords: algorithms, Constrained optimization, constraint filtering, Design, differential evolution, evolutionary algorithm, process planning, product configuration, satisfaction
  • Concurrent product configuration and process planning, towards an approach combining interactivity and optimality

    References :
    Paul Pitiot, Michel Aldanondo, Élise Vareilles, Paul Gaborit, Meriem Djefel, and Sabine Carbonnel. « Concurrent product configuration and process planning, towards an approach combining interactivity and optimality ». In: International Journal of Production Research 51.2 (2013). pp. 524--541. ISSN: 0020-7543. DOI: 10.1080/00207543.2011.653449.
    In mass customisation, defining concurrently the configured product and the planning of the associated production process is a key issue in the customer/supplier relationship. Nevertheless, few studies propose supporting the decision-maker during the resolution of this significant problem. After studying the decision-maker's needs and problem characterisation (modelling and scale aspects), we propose in this paper a two-step approach with the aid of some tools. The first step allows the customer or internal requirements to be captured interactively with a constraint-based approach. However, this step does not lead to one single solution, e. g. there are many uninstantiated remaining decision variables. In this paper, we suggest adding an original optimisation step to complete this task. Thus, the contribution of the study is twofold: first, methodologically to define a new two-step approach that meets industrial needs; and second, to provide adapted tools especially for the optimisation step. The optimisation step, using a multi-criteria constrained evolutionary algorithm, allows the user to select their own cost/cycle time compromise among a set of Pareto optimised solutions. A conventional evolutionary algorithm is adapted and modified, with the inclusion of filtering processing, in order to avoid generating invalid solutions. Experimentations are described, and a comparison is made with a branch-and-bound approach that outlines the interest in the propositions.
    Keywords: constraint filtering, constraint satisfaction, evolutionary algorithm, evolutionary algorithms, framework, optimisation, optimization, process planning, product configuration, requirements
  • Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context

    References :
    Paul Pitiot, Thierry Coudert, Laurent Geneste, and Claude Baron. « Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context ». In: Engineering Applications of Artificial Intelligence 23.5 (Aug 2010). pp. 830--843. ISSN: 0952-1976. DOI: 10.1016/j.engappai.2010.01.019.
    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. (C) 2010 Elsevier Ltd. All rights reserved.
    Keywords: Bayesian network, evolutionary algorithm, Experience feedback, Learning, manufacturing systems, Product preliminary design, Project management