Using constraints filtering and evolutionary algorithms for interactive configuration and planning

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.

[1]  Marc Zolghadri,et al.  Meta-heuristics for System Design Engineering , 2009, Foundations of Computational Intelligence.

[2]  Rina Dechter,et al.  Temporal Constraint Networks , 1989, Artif. Intell..

[3]  C. Baron,et al.  Product and process configuration: A constraint based approach , 2008, 2008 IEEE International Conference on Industrial Engineering and Engineering Management.

[4]  Reijo Sulonen,et al.  Towards a general ontology of configuration , 1998, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[5]  Bin Li,et al.  Product configuration optimization using a multiobjective genetic algorithm , 2006 .

[6]  Francesca Rossi,et al.  Constraint satisfaction techniques in planning and scheduling , 2010, J. Intell. Manuf..

[7]  Sancho Salcedo-Sanz,et al.  A survey of repair methods used as constraint handling techniques in evolutionary algorithms , 2009, Comput. Sci. Rev..

[8]  R. Kowalczyk,et al.  Constraint consistent genetic algorithms , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[9]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[10]  John Duchi,et al.  Temporal Constraint Satisfaction Problems An Evaluation of Search Strategies , 2022 .

[11]  William E. Hart,et al.  A Filter-Based Evolutionary Algorithm for Constrained Optimization , 2004, Evolutionary Computation.

[12]  Olivier Lhomme,et al.  Consistency Techniques for Numeric CSPs , 1993, IJCAI.

[13]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[14]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .