Smart Short Term Capacity Planning: A Reinforcement Learning Approach - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Smart Short Term Capacity Planning: A Reinforcement Learning Approach

Résumé

Capacity planning is an important production control function that significantly influences firm performance. Especially, in the short term, we face a dynamically changing system which calls for an adaptive capacity planning system that reacts based on the current state of the shop floor. Thus, this paper analyzes the performance of a reinforcement learning (RL) algorithm for overtime planning for a make-to-order job shop. We compare the performance of the RL algorithm to mechanisms that set overtime-hours statically or randomly over time. Performance is measured in total costs which consist of overtime, holding and backorder costs. The results show that our tested benchmarks can be outperformed by the RL algorithm, where the major savings were achieved due to less needed overtime.
Fichier principal
Vignette du fichier
509923_1_En_27_Chapter.pdf (425.84 Ko) Télécharger le fichier
Origine : Accord explicite pour ce dépôt

Dates et versions

hal-04030409 , version 1 (16-03-2023)

Licence

Paternité

Identifiants

Citer

Manuel Schneckenreither, Sebastian Windmueller, Stefan Haeussler. Smart Short Term Capacity Planning: A Reinforcement Learning Approach. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.258-266, ⟨10.1007/978-3-030-85874-2_27⟩. ⟨hal-04030409⟩
19 Consultations
3 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More