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.
This communication presents the first ideas relevant to the possibility of coupling together, thanks to constraints modelling, product configuration tools with process planning tools in an interactive and simultaneous way, in order to pass decisions made from one to the other. The first section introduces the problem and the general ideas of the proposed solution. Two constraints based models, relevant to product configuration and process planning, are presented. Then first investigations for coupling these two models and associated problems are discussed. An example illustrates our proposal through out the paper. Reference:Dr. Michel Aldanondo has been a a professor at the ecole des mines d'Albi Carmaux, France, since 1998. He had his PhD in 1992 after a degree in mecanical field in 1983. He worked for some industries abroad in the 80ies. He conentrates his researches on the development of interactive aiding design tools based on experts'knwoledge and applies this approach to industrial problems (design of products, heat treatment operations, of supply chains, …). Dr. EliseVareilles has been an assistant professor at the ecole des mines d'AlbiCarmaux, France since 2005. She had her PhD in 2005 and won the price of the best PhD Thesis of INP Toulouse in the same year. She works on the development of interactive aiding design tools based on knowledge and is part of the development and the improvement of CoFiADe. Meriem Djefel is a PhD student at the ecole des mines d'AlbiCarmaux and the university of Toulouse, France. She has reached the team in 2007. She works in the adaptation of CoFiade on the particular problem of ATLAS.
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 multicriteria 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.Keywordsproduct configuration, process planning, constraint satisfaction problem, evolutionary algorithm 978-1-4244-8503-1/10/$26.00 ©2010 IEEE Proceedings of the 2010 IEEE IEEM
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