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    A Hybrid Genetic Algorithm Approach to Multi-objective ERP Training Scheduling Problems

    Ching-Long, Su; Wu-Chen, Su

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    Master_research_result (244.1Kb)
    Date
    2007
    Citation:
    Ching-Long, S., & Wu-Chen, S. (2007). A Hybrid Genetic Algorithm Approach to Multi-objective ERP Training Scheduling Problems. conference contribution - oral presentation.
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/2099
    Abstract
    It mainly addresses a hybrid multiobjective training scheduling genetic algorithm in this research. We can get the schedule that almost matches the real decisive results to help enterprises proceed with training scheduling successfully by the algorithm. It can reduce from a week to twenty minutes in schedule. The enterprise can cost less to proceed with the schedule and have great elasticity to make decision. Finally, it lets employee in the enterprise help ERP (enterprise resource planning) system working successfully by joining the training though the establishment of enterprise decision support system and makes the enterprises’ operation cope with changeable businessenvironment with the most effective ways.
    Keywords:
    enterprise resource planning, training scheduling, competence sets, Pareto optimal solutions, multi-objective genetic algorithms
    ANZSRC Field of Research:
    080108 Neural, Evolutionary and Fuzzy Computation
    Copyright Holder:
    Su, Wu-Chen

    Copyright Notice:
    All rights reserved
    Available Online at:
    http://pdf.aminer.org/000/251/851/a_hybrid_fuzzy_evolutionary_algorithm_for_a_multi_objective_resource.pdf
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use. These documents or images may be used for research or private study purposes. Whether they can be used for any other purpose depends upon the Copyright Notice above. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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    • Computing Conference Papers [150]

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