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

Jiseong Noh, Hyun-Ji Park, Jong Soo Kim and Seung-June Hwang
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Jiseong Noh: Institute of Knowledge Services, Hanyang University, Erica, Ansan 15588, Korea
Hyun-Ji Park: Graduate School of Management Consulting, Hanyang University, Erica, Ansan 15588, Korea
Jong Soo Kim: Department of Industrial and Management Engineering, Hanyang University, Erica, Ansan 15588, Korea
Seung-June Hwang: Institute of Knowledge Services, Hanyang University, Erica, Ansan 15588, Korea

Mathematics, 2020, vol. 8, issue 4, 1-14

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.

Keywords: demand forecasting; gated recurrent unit; genetic algorithm; hyperparameter; supply chain management (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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