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YILMAZ, Görkem

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Görkem

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    Conference paperPublicationOpen Access
    A hierarchical approach for solving simultaneous lot sizing and scheduling problem with secondary resources
    (Elsevier, 2019) Şafak, C. U.; Yılmaz, Görkem; Albey, Erinç; Industrial Engineering; ALBEY, Erinç; YILMAZ, Görkem
    This study represents a decomposition heuristic approach for simultaneous lot sizing and scheduling problem for multiple product, multiple parallel machines with secondary resources. The motivation of the study comes from the real-world instance of a plastic injection plant at Vestel Electronics. The plastic injection plant requires plastic injection molds at the planner's disposal, in order to produce variations of products, by the compatible plastic injection machines. The variations on the molds and the mold changes on the machines bring out sequence dependent major and minor setups. Since each machine requires an operator, we have extended the formulation with workforce and shift planning Results show that proposed heuristic yields comparable solutions to that of exact model for small and medium size instances; and provides schedules for the large size instances, for which exact model cannot find a feasible solution in the allotted time.
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    Conference paperPublicationMetadata only
    Capacitated stochastic lot-sizing and production planning problem under demand uncertainty
    (Elsevier, 2022) Seyfishishavan, Seyed Amin; Yılmaz, Görkem; Yanıkoğlu, İhsan; Industrial Engineering; YILMAZ, Görkem; YANIKOĞLU, Ihsan; Seyfishishavan, Seyed Amin
    This paper proposes two multi-period, multi-item capacitated stochastic lot-sizing problems under demand uncertainty. We model uncertainty via a scenario tree. The first model considers production, inventory, backlogging, line status, and worker group assignment decisions, where inventory and backlogging decisions have wait-and-see structure. The second model converts line status and worker group assignment decisions to the wait-and-see structure. Also, the second model enables us to take corrective extra-ordering decisions using scenario-based wait-and-see decisions. Numerical results compare the optimality and CPU time performances of two models and solution approaches using a data set inspired by a real-life electronics company.