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2018 | OriginalPaper | Buchkapitel

A Novel Hybrid GA for the Assignment of Jobs to Machines in a Complex Hybrid Flow Shop Problem

verfasst von : Houda Harbaoui, Soulef Khalfallah, Odile Bellenguez-Morineau

Erschienen in: Intelligent Systems Design and Applications

Verlag: Springer International Publishing

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Abstract

This paper, investigates a complex manufacturing production system encountered in the food industry. We consider a two stage hybrid flow shop with two dedicated machines at stage1, and several identical parallel machines at stage 2. We consider two simultaneous constraints: the sequence dependent family setup times and time lags. The optimization criterion considered is the minimization of makespan. Given the complexity of problem, an hybrid genetic algorithms (HGA) based on an improving heuristic is presented. We experimented a new heuristic to assign jobs on the second stage. The proposed HGA is compared against a lower bound (LB), and against a mixed integer programming model (MIP). The results indicate that the proposed hybrid GA is effective and can produce near-optimal solutions in a reasonable amount of time.

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Vorheriges Kapitel HMI Fuzzy Assessment of Complex Systems Usability

Nächstes Kapitel Selecting Relevant Educational Attributes for Predicting Students’ Academic Performance

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Titel: A Novel Hybrid GA for the Assignment of Jobs to Machines in a Complex Hybrid Flow Shop Problem
verfasst von: Houda Harbaoui
Soulef Khalfallah
Odile Bellenguez-Morineau
Verlag: Springer International Publishing
Buch: Intelligent Systems Design and Applications
Print ISBN: 978-3-319-76347-7

Electronic ISBN: 978-3-319-76348-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-76348-4_62

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