Production scheduling represents part of the complex flow of information and decision-making in manufacturing planning and control system. In recent years, production scheduling has become an essential activity for achieving competitive service levels, lower lead times, reduction costs as well as customers satisfaction. Depending on facilities layout assessment (e.g., flow shop, job shop, cellular manufacturing, etc), many approaches have been introduced to optimize the scheduling and many meta-heuristic techniques have been developed. The present work deals with the problem of defining the monthly production schedule in a flow shop production line. The exploration and exploitation of possible solutions have been reached by using a population based formulation (i.e. Genetic Algorithm) in robust perspective and Variance analysis (i.e., ANOVA) was used to identify the critical parameters in the genetic model. The main advantage of such an algorithm over the other approaches is represented by the possibility to evaluate and control the optimization process as a natural evolution. The overall equipment efficiency, the warehousing costs with constraints of finite capacity, the production orders, the production rate are the main parameters accounted. Results will be presented in terms of reduction of overall costs in real flow shop manufacturing environment.

A Genetic Procedure for Production Scheduling Automatic Design

LAMBIASE, FRANCESCO;
2009-01-01

Abstract

Production scheduling represents part of the complex flow of information and decision-making in manufacturing planning and control system. In recent years, production scheduling has become an essential activity for achieving competitive service levels, lower lead times, reduction costs as well as customers satisfaction. Depending on facilities layout assessment (e.g., flow shop, job shop, cellular manufacturing, etc), many approaches have been introduced to optimize the scheduling and many meta-heuristic techniques have been developed. The present work deals with the problem of defining the monthly production schedule in a flow shop production line. The exploration and exploitation of possible solutions have been reached by using a population based formulation (i.e. Genetic Algorithm) in robust perspective and Variance analysis (i.e., ANOVA) was used to identify the critical parameters in the genetic model. The main advantage of such an algorithm over the other approaches is represented by the possibility to evaluate and control the optimization process as a natural evolution. The overall equipment efficiency, the warehousing costs with constraints of finite capacity, the production orders, the production rate are the main parameters accounted. Results will be presented in terms of reduction of overall costs in real flow shop manufacturing environment.
978-88-95028-38-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/32184
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