Study on Multi-objective Dynamic Job Shop Scheduling

2011 ◽  
pp. 648-655
Author(s):  
Lixin Qi ◽  
Xiaoxia Liu
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Dynamic Job Shop

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.

scholarly journals Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hongjing Wei ◽  
Shaobo Li ◽  
Huafeng Quan ◽  
Dacheng Liu ◽  
Shu Rao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficient ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm
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