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Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns

Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems which have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real life industries, a machine can be unavailable for many reasons, such as unanticipated breakdowns, i.e., stochastic unavailability, or due to a scheduled preventive maintenance where the periods of unavailability are known in advance, i.e., deterministic unavailability. This paper deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times, commonly known as the SDST, and machines which suffer stochastic breakdown to optimize objectives based on expected makespan. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. The genetic algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes how we can incorporate simulation into genetic algorithm approach to the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdown. An overview of the hybrid flow shops and scheduling under stochastic unavailability of machines are presented. Subsequently, the details of incorporated simulation into genetic algorithm approach are described and implemented. Consequently, the results obtained are analyzed with Taguchi experimental design. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns

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References (32)

Publisher
Springer Journals
Copyright
Copyright © 2008 by Springer-Verlag London Limited
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
DOI
10.1007/s00170-008-1577-3
Publisher site
See Article on Publisher Site

Abstract

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems which have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real life industries, a machine can be unavailable for many reasons, such as unanticipated breakdowns, i.e., stochastic unavailability, or due to a scheduled preventive maintenance where the periods of unavailability are known in advance, i.e., deterministic unavailability. This paper deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times, commonly known as the SDST, and machines which suffer stochastic breakdown to optimize objectives based on expected makespan. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. The genetic algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes how we can incorporate simulation into genetic algorithm approach to the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdown. An overview of the hybrid flow shops and scheduling under stochastic unavailability of machines are presented. Subsequently, the details of incorporated simulation into genetic algorithm approach are described and implemented. Consequently, the results obtained are analyzed with Taguchi experimental design.

Journal

The International Journal of Advanced Manufacturing Technology – Springer Journals

Published: Jun 12, 2008

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