Fuzzy job shop scheduling with lot-sizing

Sanja Petrovic, Carole Fayad, Dobrila Petrovic, Edmund Burke, Graham Kendall

    Research output: Contribution to journalArticle

    47 Citations (Scopus)

    Abstract

    This paper deals with a problem of determining lot-sizes of jobs in a real-world job shop-scheduling in the presence of uncertainty. The main issue discussed in this paper is lot-sizing of jobs. A fuzzy rule-based system is developed which determines the size of lots using the following premise variables: size of the job, the static slack of the job, workload on the shop floor, and the priority of the job. Both premise and conclusion variables are modelled as linguistic variables represented by using fuzzy sets (apart from the priority of the job which is a crisp value). The determined lots’ sizes are input to a fuzzy multi-objective genetic algorithm for job shop scheduling. Imprecise jobs’ processing times and due dates are modelled by using fuzzy sets. The objectives that are used to measure the quality of the generated schedules are average weighted tardiness of jobs, the number of tardy jobs, the total setup time, the total idle time of machines and the total flow time of jobs. The developed algorithm is analysed on real-world data obtained from a printing company.
    Original languageEnglish
    Pages (from-to)275-292
    JournalAnnals of Operations Research
    Volume159
    Issue number1
    DOIs
    Publication statusPublished - Mar 2008

    Bibliographical note

    This paper is not available on the repository.

    Keywords

    • Job shop scheduling
    • Fuzzy rule-based system
    • Lot-sizing
    • Batching
    • Fuzzy multi-objective genetic algorithm
    • Real-world application
    • Dispatching rules

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