This article discusses a multi-item planning and scheduling problem in a job-shop system with consideration of energy consumption. Planning is considered by a set of periods, each one is characterized by a demand, energy, and length. Scheduling is determined by the sequences of jobs on available resources. A Mixed-Integer Linear Programming (MILP) problem is formulated to integrate planning and scheduling, it is considered as an NP-difficult problem. A Genetic Algorithm (GA) is then developed to solve the MILP, and then a hybridized approach of simulated annealing with genetic algorithm (HGASA) is presented to optimize the results. Finally, numerical results are presented and analyzed to evaluate the effectiveness of the proposed algorithms.


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    Titel :

    A Comparative Analysis of Metaheuristic Approaches (Genetic Algorithm/Hybridization of Genetic Algorithms and Simulated Annealing) for Planning and Scheduling Problem with Energy Aspect


    Weitere Titelangaben:

    Sae Int. J. Mater. Manf
    Sae International Journal of Materials and Manufacturing




    Erscheinungsdatum :

    2021-05-20


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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