In this paper, a genetic algorithm for the robotic assembly line balancing problem (RALBP) is developed that supports multimodal stochastic processing times and multiple parallel-working robots per workstation. It has the objective to minimize the amount of workstations at a given production rate and probability limit for violating the cycle time (PL). The algorithm is evaluated on the BARTHOLD data set in a range of 1 % to 50 % for PL using an experimentally determined and a normal distribution for the task times. The increase of PL results in a shift of tasks from rear to front stations, because more tasks can be assigned to each station. The shift using normal distributed task times is stronger. This demonstrates the importance of realistic stochastic distribution assumptions. For practical applicability, more constraint types have to be included in the future.


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    Titel :

    Robotic Assembly Line Balancing with Multimodal Stochastic Processing Times


    Weitere Titelangaben:

    ARENA2036


    Beteiligte:
    Kiefl, Niklas (Herausgeber:in) / Wulle, Frederik (Herausgeber:in) / Ackermann, Clemens (Herausgeber:in) / Holder, Daniel (Herausgeber:in) / Stade, Dawid (Autor:in) / Manns, Martin (Autor:in)

    Kongress:

    Stuttgart Conference on Automotive Production ; 2022 ; Stuttgart, Germany November 16, 2022 - November 18, 2022



    Erscheinungsdatum :

    05.06.2023


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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