Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Selecting the Optimal Loading Location through Prediction of Required Amount for Goods based on Bi-LSTM

Bi-LSTM 기반 물품 소요량 예측을 통한 최적의 적재 위치 선정

  • Sein Jang (Graduate School of Consulting, Kumoh National Institute of Technology) ;
  • Yeojin Kim (Department of Industrial Engineering, Kumoh National Institute of Technology) ;
  • Geuntae Kim (Department of Industrial Engineering, Kumoh National Institute of Technology) ;
  • Jonghwan Lee (Department of Industrial Engineering, Kumoh National Institute of Technology)
  • 장세인 (금오공과대학교 컨설팅대학원) ;
  • 김여진 (금오공과대학교 산업공학과) ;
  • 김근태 (금오공과대학교 산업공학과) ;
  • 이종환 (금오공과대학교 산업공학과)
  • Received : 2023.07.31
  • Accepted : 2023.09.11
  • Published : 2023.09.30
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Abstract

Currently, the method of loading items in the warehouse, the worker directly decides the loading location, and the most used method is to load the product at the location closest to the entrance. This can be effective when there is no difference in the required amount for goods, but when there is a difference in the required amount for goods, it is inefficient because items with a small required amount are loaded near the entrance and occupy the corresponding space for a long time. Therefore, in order to minimize the release time of goods, it is essential to select an appropriate location when loading goods. In this study, a method for determining the loading location by predicting the required amount of goods was studied to select the optimal loading location. Deep learning based bidirectional long-term memory networks (Bi-LSTM) was used to predict the required amount for goods. This study compares and analyzes the release time of goods in the conventional method of loading close to the entrance and in the loading method using the required amount for goods using the Bi-LSTM model.

Keywords

Acknowledgement

이 논문은 2021년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임(No. 202101350001, 딥러닝 기반 스마트 자동물류 적재창고 기술개발).

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