scholarly journals Reinforcement Learning With Composite Rewards for Production Scheduling in a Smart Factory

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 752-766
Author(s):  
Tong Zhou ◽  
Dunbing Tang ◽  
Haihua Zhu ◽  
Liping Wang
Keyword(s):  
Reinforcement Learning ◽  
Production Scheduling ◽  
Smart Factory

scholarly journals A deep reinforcement learning approach for chemical production scheduling

2020 ◽  
Vol 141 ◽  
pp. 106982 ◽  
Author(s):  
Christian D. Hubbs ◽  
Can Li ◽  
Nikolaos V. Sahinidis ◽  
Ignacio E. Grossmann ◽  
John M. Wassick
Keyword(s):  
Reinforcement Learning ◽  
Production Scheduling ◽  
Learning Approach ◽  
Chemical Production

scholarly journals Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

2020 ◽  
Vol 12 (20) ◽  
pp. 8718 ◽  
Author(s):  
Seunghoon Lee ◽  
Yongju Cho ◽  
Young Hoon Lee
Keyword(s):  
Reinforcement Learning ◽  
Production Scheduling ◽  
Dynamic Environment ◽  
Total Weighted Tardiness ◽  
Injection Mold ◽  
Weighted Tardiness ◽  
Scheduling Policy ◽  
Delivery Date ◽  
Process Framework ◽  
Markov Decision

In the injection mold industry, it is important for manufacturers to satisfy the delivery date for the products that customers order. The mold products are diverse, and each product has a different manufacturing process. Owing to the nature of mold, mold manufacturing is a complex and dynamic environment. To meet the delivery date of the customers, the scheduling of mold production is important and is required to be sustainable and intelligent even in the complicated system and dynamic situation. To address this, in this paper, deep reinforcement learning (RL) is proposed for injection mold production scheduling. Before presenting the RL algorithm, a mathematical model for the mold scheduling problem is presented, and a Markov decision process framework is proposed for RL. The deep Q-network, which is an algorithm for RL, is employed to find the scheduling policy to minimize the total weighted tardiness. The results of experiments demonstrate that the proposed deep RL method outperforms the dispatching rules that are presented for minimizing the total weighted tardiness.

Scenario co-evolution for reinforcement learning on a grid world smart factory domain

2019 ◽  
Author(s):  
Thomas Gabor ◽  
Andreas Sedlmeier ◽  
Marie Kiermeier ◽  
Thomy Phan ◽  
Marcel Henrich ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Smart Factory

scholarly journals Digital Twin and Reinforcement Learning-Based Resilient Production Control for Micro Smart Factory

2021 ◽  
Vol 11 (7) ◽  
pp. 2977
Author(s):  
Kyu Tae Park ◽  
Yoo Ho Son ◽  
Sang Wook Ko ◽  
Sang Do Noh
Keyword(s):  
Reinforcement Learning ◽  
Production Control ◽  
Control Method ◽  
Production Systems ◽  
Smart Factory ◽  
Production Cycle ◽  
Policy Network ◽  
Digital Twin ◽  
Event Logs ◽  
Architectural Framework

To achieve efficient personalized production at an affordable cost, a modular manufacturing system (MMS) can be utilized. MMS enables restructuring of its configuration to accommodate product changes and is thus an efficient solution to reduce the costs involved in personalized production. A micro smart factory (MSF) is an MMS with heterogeneous production processes to enable personalized production. Similar to MMS, MSF also enables the restructuring of production configuration; additionally, it comprises cyber-physical production systems (CPPSs) that help achieve resilience. However, MSFs need to overcome performance hurdles with respect to production control. Therefore, this paper proposes a digital twin (DT) and reinforcement learning (RL)-based production control method. This method replaces the existing dispatching rule in the type and instance phases of the MSF. In this method, the RL policy network is learned and evaluated by coordination between DT and RL. The DT provides virtual event logs that include states, actions, and rewards to support learning. These virtual event logs are returned based on vertical integration with the MSF. As a result, the proposed method provides a resilient solution to the CPPS architectural framework and achieves appropriate actions to the dynamic situation of MSF. Additionally, applying DT with RL helps decide what-next/where-next in the production cycle. Moreover, the proposed concept can be extended to various manufacturing domains because the priority rule concept is frequently applied.

Deep reinforcement learning for semiconductor production scheduling

2018 ◽  
Author(s):  
Bernd Waschneck ◽  
Andre Reichstaller ◽  
Lenz Belzner ◽  
Thomas Altenmuller ◽  
Thomas Bauernhansl ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Production Scheduling ◽  
Semiconductor Production

scholarly journals Optimization of global production scheduling with deep reinforcement learning

Procedia CIRP ◽  
2018 ◽  
Vol 72 ◽  
pp. 1264-1269 ◽  
Author(s):  
Bernd Waschneck ◽  
André Reichstaller ◽  
Lenz Belzner ◽  
Thomas Altenmüller ◽  
Thomas Bauernhansl ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Production Scheduling
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