Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/5044
Title: Multi-stage scenario-based stochastic programming for managing lot sizing and workforce scheduling at Vestel
Authors: Seyfi, S.A.
Yanıkoğlu, İ.
Yılmaz, Görkem
Keywords: Multi-stage decision making
Production planning
Stochastic programming
Workforce scheduling
Publisher: Springer
Abstract: This study proposes a multi-stage stochastic production planning approach for a joint lot sizing and workforce scheduling problem under demand uncertainty. Scenario trees are used to model uncertainty in demand, and a multi-stage scenario-based stochastic linear program is developed. This model allows for both here-and-now and wait-and-see decisions providing flexibility for decision-makers to adjust production quantities according to the realized portion of demand and improve the overall effectiveness of production planning by better managing the number of active lines, workforce, and inventory levels. A matheuristic is developed for large-sized instances, which yields near-optimal solutions in practicable computation times. The proposed methods are demonstrated over a real data set taken from a Turkish home and professional appliances company, Vestel. The results show significant improvements in cost and CPU time performances for benchmark approaches, verifying the effectiveness of the proposed method. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
URI: https://doi.org/10.1007/s10479-023-05741-4
https://hdl.handle.net/20.500.14365/5044
ISSN: 0254-5330
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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