This paper considers the use of multi-objective genetic algorithms for solving a typical production chain problem, in which two consecutive production stages have to schedule their internal work while taking into account each other’s requirements. We focus on a multi-objective genetic algorithm recently proposed in the related literature, i.e. IGA (Intelligent Genetic Algorithm), comparing the solutions it yields with those obtained by two state-of-the-art genetic optimizers. A set of preliminary computational tests on the mentioned case study using industrial data indicate that IGA is a promising multi objective optimizer for typical supply chain planning and scheduling problems.

Genetic Algorithms for Setup Coordination in Consecutive Stages of a Supply Chain

DOTOLI, Mariagrazia;FANTI, Maria Pia;MELONI, Carlo
2006

Abstract

This paper considers the use of multi-objective genetic algorithms for solving a typical production chain problem, in which two consecutive production stages have to schedule their internal work while taking into account each other’s requirements. We focus on a multi-objective genetic algorithm recently proposed in the related literature, i.e. IGA (Intelligent Genetic Algorithm), comparing the solutions it yields with those obtained by two state-of-the-art genetic optimizers. A set of preliminary computational tests on the mentioned case study using industrial data indicate that IGA is a promising multi objective optimizer for typical supply chain planning and scheduling problems.
2006 International Workshop on Logistics & Transportation
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/20843
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