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

  • Humyun Fuad Rahman (a1), Ruhul Sarker (a1) and Daryl Essam (a1)
Abstract
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.

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Corresponding author
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
References
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AI EDAM
  • ISSN: 0890-0604
  • EISSN: 1469-1760
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