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

    5 Citations (Scopus)


    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
    ISBN (Print)978-3-319-08421-3
    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


    • genetic algorithm
    • mathematical model
    • production process
    • scheduling


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