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A genetic algorithm for permutation flowshop scheduling under practical make-to-order production system

Published online by Cambridge University Press:  06 June 2016

Humyun Fuad Rahman ,
Ruhul Sarker  and
Daryl Essam
Humyun Fuad Rahman*
Affiliation:
School of Engineering and Information Technology, University of New South Wales, Canberra, Australia
Ruhul Sarker
Affiliation:
School of Engineering and Information Technology, University of New South Wales, Canberra, Australia
Daryl Essam
Affiliation:
School of Engineering and Information Technology, University of New South Wales, Canberra, Australia
*
Reprint requests to: Humyun Fuad Rahman, School of Engineering and Information Technology, University of New South Wales, Canberra ACT 2600, Australia. E-mail: m.rahman4@adfa.edu.au
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Abstract

The aim of this work is to bridge the gap between the theory and actual practice of production scheduling by studying a problem from a real-life production environment. This paper considers a practical Sanitaryware production system as a number of make-to-order permutation flowshop problems. Due to the wide range of variation in its products, real-time arrival of customer orders, dynamic batch adjustments, and time for machine setup, Sanitaryware production system is complex and also time sensitive. In practice, many such companies run with suboptimal solutions. To tackle this problem, in this paper, a memetic algorithm based real-time approach has been proposed. Numerical experiments based on real data are also been presented in this paper.

Keywords

Flowshop SchedulingGenetic AlgorithmMemetic AlgorithmOrder AcceptancePractical Make-to-Order System
Type
Regular Articles
Information
AI EDAM , Volume 31 , Issue 1 , February 2017 , pp. 87 - 103
Copyright
Copyright © Cambridge University Press 2016 

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