Decision support tool for multi-objective job shop scheduling problems with linguistically quantified decision functions

Dobrila Petrovic, Alejandra Duenas, Sanja Petrovic

    Research output: Contribution to journalArticle

    28 Citations (Scopus)

    Abstract

    This paper presents a new tool for multi-objective job shop scheduling problems. The tool encompasses an interactive fuzzy multi-objective genetic algorithm (GA) which considers aspiration levels set by the decision maker (DM) for all the objectives. The GA's decision (fitness) function is defined as a measure of truth of a linguistically quantified statement, imprecisely specified by the DM using linguistic quantifiers such as most, few, etc., that refer to acceptable distances between the achieved objective values and the aspiration levels. The linguistic quantifiers are modelled using fuzzy sets. The developed tool is used to analyse and solve a real-world problem defined in collaboration with a pottery company. The tool provides a valuable support in performing various what-if analyses, for example, how changes of batch sizes, aspiration levels, linguistic quantifiers and the measure of acceptable distances affect the final schedule.
    Original languageEnglish
    Pages (from-to)1527–1538
    JournalDecision Support Systems
    Volume43
    Issue number4
    DOIs
    Publication statusPublished - Aug 2007

    Bibliographical note

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    Keywords

    • Job shop scheduling
    • Fuzzy sets
    • Linguistic quantifiers
    • Multi-objective optimisation
    • Genetic algorithms

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