농산물의 거래처별 가격과 수요를 예측하고 물동량을 분배하는 딥러닝 방법론 연구 = A study of deep learning methods for predicting the price and demand of agricultural items for each consumer and distributing the quantity of supplies
'
농산물의 거래처별 가격과 수요를 예측하고 물동량을 분배하는 딥러닝 방법론 연구 = A study of deep learning methods for predicting the price and demand of agricultural items for each consumer and distributing the quantity of supplies' 의 주제별 논문영향력
논문영향력 요약
주제
일반 경영
가격 예측
거래처
농산물
딥 러닝
물동량 분배
방법론
수요예측
동일주제 총논문수
논문피인용 총횟수
주제별 논문영향력의 평균
2,517
0
0.0%
주제별 논문영향력
논문영향력
주제
주제별 논문수
주제별 피인용횟수
주제별 논문영향력
주제분류(KDC/DDC)
일반 경영
185
0
0.0%
주제어
가격 예측
18
0
0.0%
거래처
1
0
0.0%
농산물
72
0
0.0%
딥 러닝
1,935
0
0.0%
물동량 분배
1
0
0.0%
방법론
174
0
0.0%
수요예측
131
0
0.0%
계
2,517
0
0.0%
* 다른 주제어 보유 논문에서 피인용된 횟수
0
'
농산물의 거래처별 가격과 수요를 예측하고 물동량을 분배하는 딥러닝 방법론 연구 = A study of deep learning methods for predicting the price and demand of agricultural items for each consumer and distributing the quantity of supplies' 의 참고문헌
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[2019]
Wang, Z. H., Lu, C. Y., Pu, B., Li, G. W., & Guo, Z. J. (2017). Short-term forecast model of vehicles volume based on ARIMA seasonal model and holt-winters. In ITM Web of Conferences (Vol. 12, p. 04028). EDP Sciences.
[2017]
Trisna, T., Marimin, M., Arkeman, Y., & Sunarti, T. (2016). Multi-objective optimization for supply chain management problem: A literature review. Decision Science Letters, 5(2), 283-316.
[2016]
Ma, S., Fildes, R., & Huang, T. (2016). Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra-and inter-category promotional information. European Journal of Operational Research, 249(1), 245-257.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
[2018]
Lezoche, M., Hernandez, J. E., Díaz, M. D. M. E. A., Panetto, H., & Kacprzyk, J. (2020). Agri-food 4.0: a survey of the supply chains and technologies for the future agriculture. Computers in Industry, 117, 103187.
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[2014]
Kim, C., Im, C., & Youm, S. (2020). Auxiliary system for prediction of volume using tomata big data and data mining methodology. JP Journal of Heat and Mass Transfer.
[2020]
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37.
[2017]
Kamariotou, M., Kitsios, F., Madas, M. A., Manthou, V., & Vlachopoulou, M. (2017, September). Strategic Decision Support Systems for Logistics in the Agrifood Industry. In HAICTA (pp. 781-794).
[2017]
Im, C., Kim, C., & Youm, S. (2019). Tomato Volume Prediction System Using Nonghyup Tomato Big Data. Advanced Engineeirng and ICT-Convergence Proceedings. 2.
[2019]
Holzworth, D. P., Snow, V., Janssen, S., Athanasiadis, I. N., Donatelli, M., Hoogenboom, G., ... & Thorburn, P. (2015). Agricultural production systems modelling and software: current status and future prospects. Environmental Modelling & Software, 72, 276-286.
[2015]
Gavirneni, S., & Tayur, S. (2001). An efficient procedure for non-stationary inventory control. Iie Transactions, 33(2), 83-89.
[2001]
Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & Service Operations Management, 18(1), 69-88.
[2016]
Cho, J. H., Wang, Y., Chen, R., Chan, K. S., & Swami, A. (2017). A survey on modeling and optimizing multi-objective systems. IEEE Communications Surveys & Tutorials, 19(3), 1867-1901.
[2017]
Castro, C. A. D. O., Resende, R. T., Kuki, K. N., Carneiro, V. Q., Marcatti, G. E., Cruz, C. D., & Motoike, S. Y. (2017). High-performance prediction of macauba fruit biomass for agricultural and industrial purposes using Artificial Neural Networks. Industrial Crops and Products, 108, 806-813.
Bortolini, M., Faccio, M., Ferrari, E., Gamberi, M., & Pilati, F. (2016). Fresh food sustainable distribution: cost, delivery time and carbon footprint three-objective optimization. Journal of Food Engineering, 174, 56-67.
[2016]
Borodin, V., Bourtembourg, J., Hnaien, F., & Labadie, N. (2016). Handling uncertainty in agricultural supply chain management: A state of the art. European Journal of Operational Research, 254(2), 348-359.
[2016]
Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165-4176.
[2013]
Ahumada, O., & Villalobos, J. R. (2011). Operational model for planning the harvest and distribution of perishable agricultural products. International Journal of Production Economics, 133(2), 677-687.
[2011]
'
농산물의 거래처별 가격과 수요를 예측하고 물동량을 분배하는 딥러닝 방법론 연구 = A study of deep learning methods for predicting the price and demand of agricultural items for each consumer and distributing the quantity of supplies'
의 유사주제(
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