2019
DOI: 10.1016/j.apm.2018.09.010
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Sliding mode control of inventory management systems with bounded batch size

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Cited by 14 publications
(6 citation statements)
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References 36 publications
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“…Beberapa penelitian telah mengembangkan sistem informasi manajemen inventaris/aset, yang mana sistem yang dikembangkan tersebut memuat menu-menu mengenai kegiatan perbekalan (Taleizadeh et al, 2020), (Bartoszewicz and Latosiński, 2019), (Cendani et al, 2019), (Dharma et al, 2019), (Rachmawati et al, 2018), (Tejesh and Neeraja, 2018), (Pambudi et al, 2017), (Atieh et al, 2016), (Mirzaei and Seifi, 2015), (Pertiwi et al, 2013). Akan tetapi, pengembangan sistem informasi manajemen inventaris/aset pada penelitian sebelumnya terbatas untuk diterapkan di instansi swasta baik perusahaan atau kantor-kantor tertentu.…”
Section: Pendahuluanunclassified
“…Beberapa penelitian telah mengembangkan sistem informasi manajemen inventaris/aset, yang mana sistem yang dikembangkan tersebut memuat menu-menu mengenai kegiatan perbekalan (Taleizadeh et al, 2020), (Bartoszewicz and Latosiński, 2019), (Cendani et al, 2019), (Dharma et al, 2019), (Rachmawati et al, 2018), (Tejesh and Neeraja, 2018), (Pambudi et al, 2017), (Atieh et al, 2016), (Mirzaei and Seifi, 2015), (Pertiwi et al, 2013). Akan tetapi, pengembangan sistem informasi manajemen inventaris/aset pada penelitian sebelumnya terbatas untuk diterapkan di instansi swasta baik perusahaan atau kantor-kantor tertentu.…”
Section: Pendahuluanunclassified
“…Vladimir et al [ 23 ] considered the optimization problem of a failure-prone manufacturing system with uncertainty in demand and inventory levels, and designed a controller based on adaptive control for the online estimation and optimal control of a supply-chain inventory system in the presence of unknown demand and inaccurate inventory. Bartoszewicz et al [ 24 ] used sliding mode control to optimize the management of manufacturers’ inventories with storage constraints for manufacturers’ inventory management under different demands. Achamrah et al [ 25 ] used the genetic algorithm and deep reinforcement learning to solve the inventory path problem with transit and substitution under dynamic and stochastic demand.…”
Section: Introductionmentioning
confidence: 99%
“…Stok yönetim sisteminde değişkenlerin belirlenmesi ve ardından gönderi boyutunun minimize edilmesi, depolama alanının kapasitesinin aşılmaması ve müşteri taleplerinin karşılanabilmesi amacıyla simüle edilmesi süreci üzerine çalışılmıştır (Bartoszewicz ve Latosinski, 2019).…”
Section: Introductionunclassified