Self-adapting WIP parameter setting using deep reinforcement learning

DOI10.1016/J.COR.2022.105854OpenAlexW4224299265MaRDI QIDQ2147128

Américo Azevedo, Manuel Tomé De Andrade e. Silva

Publication date: 22 June 2022

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2022.105854

zbMATH Keywords

CONWIPdeep reinforcement learningWIP reduction

Mathematics Subject Classification ID

Operations research and management science (90Bxx)

Related Items (1)

Robustness evaluation of trust and reputation systems using a deep reinforcement learning approach

Uses Software

• IMPALA

Cites Work

• Unnamed Item
• A Mathematical Theory of Communication
• IMPALA
• Monotonicity of the Throughput of a Closed Queueing Network in the Number of Jobs
• A dynamic programming model for the kanban assignment problem in a multistage multiperiod production system
• Push and Pull Production Systems: Issues and Comparisons
• Setting WIP levels with statistical throughput control (STC) in CONWIP production lines
• An adaptive approach to controlling kanban systems

This page was built for publication: Self-adapting WIP parameter setting using deep reinforcement learning