Reinforcement Learning Based Job Shop Scheduling with Machine Choice

2011 ◽  
Vol 314-316 ◽  
pp. 2172-2176
Author(s):  
Chao Wang ◽  
Hong Bin Zhang ◽  
Jing Guo ◽  
Ling Chen
Keyword(s):  
Reinforcement Learning ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Dynamic Environment ◽  
Core Competitiveness ◽  
Key Technology ◽  
Slow Convergence ◽  
Shop Scheduling ◽  
Modern Manufacturing ◽  
Dynamic Job Shop

Job shop scheduling is a key technology in modern manufacturing. Scheduling performance will decide the enterprises’ core competitiveness. In this paper, improved reinforcement learning with cohesion is used in dynamic job shop environment, and it eased the contradiction of precocious and slow convergence. Also the machine choice is considered. So the dual scheduling which included job and machine is achieved in this system. And it obtains better results through the experiments. The utilization of equipments and the emergency handling capacity can be improved in the dynamic environment.

scholarly journals A dynamic job-shop scheduling model based on deep learning

2021 ◽  
Vol 16 (1) ◽  
pp. 23-36
Author(s):  
W. Tian ◽  
H.P. Zhang
Keyword(s):  
Deep Learning ◽  
Job Shop ◽  
Short Term Memory ◽  
Job Shop Scheduling ◽  
Core Competitiveness ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Model Based ◽  
Scheduling Model ◽  
Dynamic Job Shop

Ideally, the solution to job-shop scheduling problem (JSP) should effectively reduce the cost of manpower and materials, thereby enhancing the core competitiveness of the manufacturer. Deep learning (DL) neural networks have certain advantages in handling complex dynamic JSPs with a massive amount of historical data. Therefore, this paper proposes a dynamic job-shop scheduling model based on DL. Firstly, a data prediction model was established for dynamic job-shop scheduling, with long short-term memory network (LSTM) as the basis; the Dropout technology and adaptive moment estimation (ADAM) were introduced to enhance the generalization ability and prediction effect of the model. Next, the dynamic JSP was described in details, and three objective functions, namely, maximum makespan, total device load, and key device load, were chosen for optimization. Finally, the multi-objective problem of dynamic JSP scheduling was solved by the improved multi-objective genetic algorithm (MOGA). The effectiveness of the algorithm was proved experimentally.

Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning

2021 ◽  
Vol 190 ◽  
pp. 107969
Author(s):  
Libing Wang ◽  
Xin Hu ◽  
Yin Wang ◽  
Sujie Xu ◽  
Shijun Ma ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Smart Manufacturing ◽  
Shop Scheduling ◽  
Dynamic Job Shop

scholarly journals Genetic Programming with Delayed Routing for Multi-Objective Dynamic Flexible Job Shop Scheduling

2020 ◽  
Author(s):  
Binzi Xu ◽  
Yi Mei ◽  
Yan Wang ◽  
Zhicheng Ji ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Dynamic Environment ◽  
Accurate Information ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Conflicting Objectives

Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e. the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this paper, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and more accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multi-objective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

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