Dynamic Scheduling of the Dual Stocker System Using Reinforcement Learning - Advances in Production Management Systems Access content directly
Conference Papers Year : 2018

Dynamic Scheduling of the Dual Stocker System Using Reinforcement Learning

Abstract

The stocker system is the most widely used material handling system in LCD and flat panel fabrication facilities (FABs). The stocker mainly consists of one or two cranes moving along a single track to transport lots, or cassettes, containing 10 to 30 thin glass substrates between processing machines. Because the stocker system is the primary material handling system in the FABs, its performance directly affects the overall performance. In this study, we investigate the scheduling of a dual stocker system operating with two cranes simultaneously on a single track and propose a learning-based scheduling algorithm for the system. We report some of the results of our long-term efforts to dynamically optimize the dual-crane stocker. We fisrt show the modeling and algorithm to minimize the make-span of the jobs. We incorporate the model to dynamically allocate jobs. In particular, we use a reinforcement learning method in the scheduling algorithm. The model is validated in an extensive simulation study based on actual data.
Fichier principal
Vignette du fichier
472850_1_En_59_Chapter.pdf (460.9 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02164880 , version 1 (25-06-2019)

Licence

Attribution

Identifiers

Cite

Seol Hwang, Sang Pyo Hong, Young Jae Jang. Dynamic Scheduling of the Dual Stocker System Using Reinforcement Learning. IFIP International Conference on Advances in Production Management Systems (APMS), Aug 2018, Seoul, South Korea. pp.482-489, ⟨10.1007/978-3-319-99704-9_59⟩. ⟨hal-02164880⟩
135 View
215 Download

Altmetric

Share

Gmail Facebook X LinkedIn More