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Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management

Title
Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management
Author
김종수
Keywords
demand forecasting; gated recurrent unit; genetic algorithm; hyperparameter; supply chain management
Issue Date
2020-04
Publisher
MDPI
Citation
MATHEMATICS, v. 8, Issue. 4, Article no. 565, 14pp
Abstract
Product demand forecasting plays a vital role in supply chain management since it is directly related to the profit of the company. According to companies' concerns regarding product demand forecasting, many researchers have developed various forecasting models in order to improve accuracy. We propose a hybrid forecasting model called GA-GRU, which combines Genetic Algorithm (GA) with Gated Recurrent Unit (GRU). Because many hyperparameters of GRU affect its performance, we utilize GA that finds five kinds of hyperparameters of GRU including window size, number of neurons in the hidden state, batch size, epoch size, and initial learning rate. To validate the effectiveness of GA-GRU, this paper includes three experiments: comparing GA-GRU with other forecasting models, k-fold cross-validation, and sensitive analysis of the GA parameters. During each experiment, we use root mean square error and mean absolute error for calculating the accuracy of the forecasting models. The result shows that GA-GRU obtains better percent deviations than other forecasting models, suggesting setting the mutation factor of 0.015 and the crossover probability of 0.70. In short, we observe that GA-GRU can optimally set five types of hyperparameters and obtain the highest forecasting accuracy.
URI
https://www.mdpi.com/2227-7390/8/4/565https://repository.hanyang.ac.kr/handle/20.500.11754/164515
ISSN
2227-7390
DOI
10.3390/math8040565
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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