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

Zhonghua Shen, Keith J. Burnham, Leonid Smalov

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

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
Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing: Proceedings of the Twenty-Third International Conference on Systems Engineering
EditorsHenry Selvaraj, Dawid Zydek, Grzegorz Chmaj
PublisherSpringer Verlag
Pages89-92
Volume1089
ISBN (Print)978-3-319-08421-3
DOIs
Publication statusPublished - 2015

Bibliographical note

This paper is not available on the repository. The paper was given at the 23rd International Conference on Systems Engineering, ICSEng 2014; Las Vegas, NV; United States; 19 August 2014 through 21 August 2014

Keywords

  • genetic algorithm
  • mathematical model
  • production process
  • scheduling

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  • Cite this

    Shen, Z., Burnham, K. J., & Smalov, L. (2015). Optimised Job-Shop Scheduling via Genetic Algorithm for a Manufacturing Production System. In H. Selvaraj, D. Zydek, & G. Chmaj (Eds.), Advances in Intelligent Systems and Computing: Proceedings of the Twenty-Third International Conference on Systems Engineering (Vol. 1089, pp. 89-92). Springer Verlag. https://doi.org/10.1007/978-3-319-08422-0_13