A Multi-Agent Learning Approach for the Job Shop Scheduling Problem

    Onderzoeksoutput: Editorial

    Samenvatting

    In this paper, a Multi-agent learning approach for the JSSP is proposed, a Q-Learning algorithm is implemented assigning an agent per machine, which will decide which job will be processed next out of a set of currently waiting jobs at the corresponding resource. The behavior of the algorithm is studied using three different cost functions, and on different benchmarks problems from the OR-Library. The approach is compared with other heuristic approaches such as ACO. The results show that our approach yields solutions as good as better than the ACO approach.
    Originele taal-2English
    Pagina's (van-tot)259
    Aantal pagina's1
    TijdschriftBook of Abstracts of the 23rd European Conference on Operational Research
    StatusPublished - 2009

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