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Optimised Job-Shop Scheduling via Genetic Algorithm for a Manufacturing Production System

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Progress in Systems Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 366))

Abstract

The present work aims to develop a genetic algorithm (GA)-based approach to optimise the job-shop scheduling problem in a micro-brewery to minimise the production time and costs. In a production system, orders are placed randomly to form a queue. The problem is how to optimally schedule the tasks through the production process given the constraints on capacity and the customer satisfaction/service level. The work concentrates on formulating a mathematical model and to modify the scheduling problem based on a GA approach.

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References

  1. Chen.H, Ihblow.J, and Lehmann.C (1999) A genetic algorithm for flexible job-shop scheduling. IEEE International Conference on Robotics and Automation, Detroit, p1120–5.

    Google Scholar 

  2. Garey, E.L., Johnson, D.S and Sethi, R. (1976) The complexity of flow shop and job shop scheduling. Mathematics of Operations Research, p117–129.

    Google Scholar 

  3. Ho.N and Tay.J (2004) An efficient cultural algorithm for solving the flexible job-shop problem. IEEE International conference on robotics and automation, 1759–66

    Google Scholar 

  4. Jia.H, Nee.A, Futh.J and Zhang (2003) A modified genetic algorithm for distributed scheduling problems. International Journal of Intelligent Manufacturing,v14:351–62

    Google Scholar 

  5. Oprea.H and Nicoara.S (2005) Artificial intelligence, Petroleum-gas University of Ploiesti.

    Google Scholar 

  6. Seda, M. (2008) Mathematical models of flow shop and job shop scheduling problems. International Journal of Applied Mathematics and Computer Sciences 4, (4).

    Google Scholar 

  7. Shen.Z, Burnham.J.K, Samlov.L, and Amin.S (2014) Formulating scheduling problem for a manufacturing production system. Conference Proceeding of International Conference on Computer, Network Security and Communication Engineering, DEStech Pubilications.

    Google Scholar 

  8. Sun.L, Cheng.X and Liang.Y (2010) Solving job shop scheduling problem using genetic algorithm with penalty function. International Journal of Intelligent Information Processing, volume1, No.2.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Control Theory and Applications Centre, Coventry University, Coventry, UK, CV1 5FB

    Zhonghua Shen, Keith J. Burnham & Leonid Smalov

Authors
  1. Zhonghua Shen
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  2. Keith J. Burnham
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  3. Leonid Smalov
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Corresponding author

Correspondence to Keith J. Burnham .

Editor information

Editors and Affiliations

  1. University of Nevada at Las Vegas, Las Vegas, Nevada, USA

    Henry Selvaraj

  2. Department of Electrical Engineering, Idaho State University, Pocatello, Idaho, USA

    Dawid Zydek

  3. University of Nevada at Las Vegas, Las Vegas, Nevada, USA

    Grzegorz Chmaj

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© 2015 Springer International Publishing Switzerland

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Shen, Z., Burnham, K.J., Smalov, L. (2015). Optimised Job-Shop Scheduling via Genetic Algorithm for a Manufacturing Production System. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-08422-0_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08421-3

  • Online ISBN: 978-3-319-08422-0

  • eBook Packages: EngineeringEngineering (R0)

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