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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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.
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.
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
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
Oprea.H and Nicoara.S (2005) Artificial intelligence, Petroleum-gas University of Ploiesti.
Seda, M. (2008) Mathematical models of flow shop and job shop scheduling problems. International Journal of Applied Mathematics and Computer Sciences 4, (4).
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
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)