Multi-objective genetic algorithm for single machine scheduling problem under fuzziness

A. Duenas, Dobrila Petrovic

    Research output: Contribution to journalArticle

    17 Citations (Scopus)
    76 Downloads (Pure)

    Abstract

    This paper presents a new multi-objective approach to a single machine scheduling problem in the presence of uncertainty. The uncertain parameters under consideration are due dates of jobs. They are modelled by fuzzy sets where membership degrees represent decision maker’s satisfaction grade with respect to the jobs’ completion times. The two objectives defined are to minimise the maximum and the average tardiness of the jobs. Due to fuzziness in the due dates, the two objectives become fuzzy too. In order to find a job schedule that maximises the aggregated satisfaction grade of the objectives, a hybrid algorithm that combines a multi-objective genetic algorithm with local search is developed. The algorithm is applied to solve a real-life problem of a manufacturing pottery company.
    Original languageEnglish
    Pages (from-to)87-104
    JournalFuzzy Optimization and Decision Making
    Volume7
    Issue number1
    DOIs
    Publication statusPublished - 2008

    Fingerprint

    Multi-objective Genetic Algorithm
    Single Machine Scheduling
    Due Dates
    Fuzziness
    Scheduling Problem
    Genetic algorithms
    Scheduling
    Tardiness
    Uncertain Parameters
    Completion Time
    Hybrid Algorithm
    Fuzzy sets
    Local Search
    Fuzzy Sets
    Schedule
    Manufacturing
    Maximise
    Minimise
    Uncertainty
    Industry

    Bibliographical note

    The final publication is available at www.springerlink.com.

    Keywords

    • single machine scheduling
    • fuzzy sets
    • multi-objective optimisation
    • genetic algorithms
    • local search

    Cite this

    Multi-objective genetic algorithm for single machine scheduling problem under fuzziness. / Duenas, A.; Petrovic, Dobrila.

    In: Fuzzy Optimization and Decision Making, Vol. 7, No. 1, 2008, p. 87-104.

    Research output: Contribution to journalArticle

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