Composition of module stock for final assembly using an enhanced genetic algorithm |
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Authors: | Bruno Agard Catherine da Cunha Bernard Cheung |
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Affiliation: | 1. CIRRELT, Département de Mathématiques et de Génie Industriel , école Polytechnique de Montréal , Montréal (Québec), Canada bruno.agard@polymtl.ca;3. Laboratoire IRCCyN, école Centrale de Nantes , Nantes Cedex 03, France;4. GERAD, école Polytechnique de Montréal , Montréal (Québec), Canada |
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Abstract: | The paper focuses on modelling and solving a design problem, namely the selection of a set of modules to be manufactured at one or more distant sites and shipped to a proximity site for final assembly subject to time constraints. The problem is modelled as a mathematical one, and solved by an appropriately designed genetic algorithm enhanced with a modified crossover operation, a uniform mutation with adaptive rate and a partial reshuffling procedure. The actual design problem is solved with 17 components. Larger problems may be solved without modifying the modelling steps, although they may require variation in terms of processing time, depending on the constraints that exist between the components. |
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Keywords: | product design productivity improvement data mining |
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