scholarly journals Multi-Objective Job Scheduling Model Based on NSGA-II for Grid Computing

2011 ◽  
Vol 16 (7) ◽  
pp. 13-23
Author(s):  
Sol-Ji Kim ◽  
Tae-Ho Kim ◽  
Hong-Chul Lee
Keyword(s):  
Grid Computing ◽  
Job Scheduling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Model Based ◽  
Scheduling Model

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.

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