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A Genetic Algorithm with Priority Rules for Solving Job-Shop Scheduling Problems

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Natural Intelligence for Scheduling, Planning and Packing Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 250))

Abstract

The Job-Shop Scheduling Problem (JSSP) is one of the most difficult NP-hard combinatorial optimization problems. In this chapter, we consider JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. First, we develop a genetic algorithm (GA) based approach for solving JSSPs. We then introduce a number of priority rules to improve the performance of GA, such as partial re-ordering, gap reduction, and restricted swapping. The addition of these rules results in a new hybrid GA algorithm that is clearly superior to other well-known algorithms appearing in the literature. Results show that this new algorithm obtained optimal solutions for 27 out of 40 benchmark problems. It thus makes a significantly new contribution to the research into solving JSSPs.

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Authors and Affiliations

  1. School of IT&EE, University of New South Wales at the Australian Defence Force Academy, Northcott Drive, Canberra, ACT, 2600, Australia

    S. M. Kamrul Hasan, Ruhul Sarker & Daryl Essam

  2. Division of Energy Technology, Commonwealth Scientific and Industrial Research Organization, Murray Dwyer Circuit, Mayfield West, NSW, 2304, Australia

    David Cornforth

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  1. S. M. Kamrul Hasan
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  2. Ruhul Sarker
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  3. Daryl Essam
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  4. David Cornforth
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Editors and Affiliations

  1. School of Computing & Design, Swinburne University of Technology (Sarawak Campus), Jalan Simpang Tiga, 93350, Kuching, Sarawak, Malaysia

    Raymond Chiong  & Sandeep Dhakal  & 

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Hasan, S.M.K., Sarker, R., Essam, D., Cornforth, D. (2009). A Genetic Algorithm with Priority Rules for Solving Job-Shop Scheduling Problems. In: Chiong, R., Dhakal, S. (eds) Natural Intelligence for Scheduling, Planning and Packing Problems. Studies in Computational Intelligence, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04039-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-04039-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04038-2

  • Online ISBN: 978-3-642-04039-9

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