M
Michel Aldanondo
Researcher at University of Toulouse
Publications - 105
Citations - 913
Michel Aldanondo is an academic researcher from University of Toulouse. The author has contributed to research in topics: Constraint satisfaction problem & Constraint (information theory). The author has an hindex of 14, co-authored 101 publications receiving 840 citations. Previous affiliations of Michel Aldanondo include École nationale d'ingénieurs de Tarbes & École Normale Supérieure.
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An optimization model for selecting a product family and designing its supply chain
TL;DR: A mixed integer linear programming model is investigated that optimizes the operating cost of the resulting supply chain while choosing the product variants.
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Configuration for mass customization: how to extend product configuration towards requirements and process configuration
Michel Aldanondo,Élise Vareilles +1 more
TL;DR: The aim of this paper is to show how Product Configuration, when considered as a constraint satisfaction problem, can be extended upstream towards Requirements Configuration and downstream towards Process Configuration.
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Mass customization and configuration: Requirement analysis and constraint based modeling propositions
TL;DR: The purpose of this paper is to identify to define and classify customization requirements and evaluate how generic modeling and configuration assistance within the CSP framework can fulfil the requirements.
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Expert configurator for concurrent engineering: Cameleon software and model
TL;DR: This paper identifies cases in which the use of expert configurator software is a significant improvement for concurrent engineering achievement and presents a model that allows to specify the industrial problem before implementation.
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Concurrent product configuration and process planning: Some optimization experimental results
TL;DR: A recent evolutionary optimization algorithm called 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.