Concurrent product configuration and process planning: Some optimization experimental results

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 c...

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Bibliographic Details
Published in:Computers in industry Vol. 65; no. 4; pp. 610 - 621
Main Authors: Pitiot, Paul, Aldanondo, Michel, Vareilles, Elise
Format: Journal Article
Language:English
Published: Kidlington Elsevier B.V 01.05.2014
Elsevier
Elsevier Sequoia S.A
Subjects:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Computer Aided Engineering
Computer Science
Computer science; control theory; systems
Constrained optimization
Constraint filtering
Evolutionary algorithm
Exact sciences and technology
Mechanical engineering. Machine design
Multiple criteria decision making
Operations research
Optimization
Optimization algorithms
Process planning
Product configuration
Production planning
Studies
Theoretical computing
Product configuration
Process planning
Evolutionary algorithm
Constraint filtering
Constrained optimization
Competition
Mass production
Filtering
Multicriteria analysis
Customization
Product planning
Constraint satisfaction
Experimental study
Engineering design
Production management
Experimental result
Product structure
Production planning
Concurrency
Implicit enumeration method
Production process
Mathematical programming
ISSN:0166-3615, 1872-6194
Online Access:Get full text
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Summary: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.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0166-3615
1872-6194
DOI:10.1016/j.compind.2014.01.012