Rapid modeling and discovery of priority dispatching rules: An autonomous learning approach

    Authors

    C. D. Geiger; R. Uzsoy;H. Aytug

    Comments

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    Abbreviated Journal Title

    J. Sched.

    Keywords

    priority dispatching rules; single machine; rule discovery; genetic; programming; BATCH-PROCESSING MACHINE; INCOMPATIBLE JOB FAMILIES; GENETIC ALGORITHM; MANUFACTURING SYSTEMS; SHOP PROBLEM; METHODOLOGY; ENVIRONMENT; TARDINESS; Engineering, Manufacturing; Operations Research & Management Science

    Abstract

    Priority-dispatching rules have been studied for many decades, and they form the backbone of much industrial scheduling practice. Developing new dispatching rules for a given environment, however, is usually a tedious process involving implementing different rules in a simulation model of the facility under study and evaluating the rule through extensive simulation experiments. In this research, an innovative approach is presented, which is capable of automatically discovering effective dispatching rules. This is a significant step beyond current applications of artificial intelligence to production scheduling, which are mainly based on learning to select a given rule from among a number of candidates rather than identifying new and potentially more effective rules. The proposed approach is evaluated in a variety of single machine environments, and discovers rules that are competitive with those in the literature, which are the results of decades of research.

    Journal Title

    Journal of Scheduling

    Volume

    9

    Issue/Number

    1

    Publication Date

    1-1-2006

    Document Type

    Article

    Language

    English

    First Page

    7

    Last Page

    34

    WOS Identifier

    WOS:000234552900002

    ISSN

    1094-6136

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