Smart Warehouse Management using Hybrid Architecture of Neural Network with Barcode Reader 1D / 2D Vision Technology

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Mbida Mohamed 1,*

1. Dept Mathematics& informatics Emerging Technologies Laboratory (LAVETE), Faculty of Sciences and Technology Hassan 1st University, Settat, Morocco

* Corresponding author.


Received: 1 Feb. 2019 / Revised: 19 May 2019 / Accepted: 12 Aug. 2019 / Published: 8 Nov. 2019

Index Terms

Neural, network, smart, wharehouse, hybride, management


Manually, to manage stocks amounts spending the every day in the rays to count for each product the number which it remains in stores, or to record by a scanner head barcode information dependent of each product. However, the mission become increasingly difficult if several warehouses are found, that involves much time to pass from a product to another, moreover that requires agents to carry out these spots. In this article we use a network architecture neuron combined with the readers bar code of technology vision, this method allows to know in real time information concerning each product in stock. It will allow besides introducing the concept of real stocks rather than physical. However The basic classical use of data and to feed it will be completely changed by the spheres of knowledge which generates the NN (Neural Network) to store information on the quantity at a given time (Dynamic inventory), the entries(delivery of suppliers ) and the outputs ( delivery or sale with the customers and use of manufacturing pieces or repair ).

Cite This Paper

Mbida Mohamed, "Smart Warehouse Management using Hybrid Architecture of Neural Network with Barcode Reader 1D / 2D Vision Technology", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.11, pp.16-24, 2019. DOI:10.5815/ijisa.2019.11.02


[1]N. Guo, X. Wang, Z. Wang, and J. Zhu, “GBVS Based 1D and 2D Barcodes Localization in Complex Scene,” in 2015 International Conference on Computational Intelligence and Communication Networks (CICN), Jabalpur, India, 2015, pp. 352–356
[2]Y. Yang, C. Gu, Y. Li, R. Gale, and C. Li, “Doppler Radar Motion Sensor with CMOS Digital DC-Tuning VGA and Inverter-Based Sigma-Delta Modulator,” IEEE Trans. Instrum. Meas., vol. 63, no. 11, pp. 2666–2674, Nov. 2014.
[3]K. Gharehbaghi, “Artificial Neural Network for Transportation Infrastructure Systems,” MATEC Web Conf., vol. 81, p. 05001, 2016.
[4]LECUN, Yann, BENGIO, Yoshua, et HINTON, Geoffrey. Deep learning. nature, 2015, vol. 521, no 7553, p. 436 .
[5]Y.-S. Lin, Y. Zhang, I.-C. Lin, and C.-J. Chang, “Predicting logistics delivery demand with deep neural networks,” in 2018 7th International Conference on Industrial Technology and Management (ICITM), Oxford, United Kingdom, 2018, pp. 294–297
[6]F.-M. Tsai and L. J. W. Huang, “Using artificial neural networks to predict container flows between the major ports of Asia,” Int. J. Prod. Res., vol. 55, no. 17, pp. 5001–5010, Sep. 2017.
[7]MA, Xiaolei, TAO, Zhimin, WANG, Yinhai, et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 2015, vol. 54, p. 187-197.
[8]GRAVES, Alex, MOHAMED, Abdel-rahman, et HINTON, Geoffrey. Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013. p. 6645-6649.
[9]O. Yorulmaz, O. Oguz, E. Akhan, D. Tuncel, R. C. Atalay, and A. E. Cetin, “Multi-resolution super-pixels and their applications on fluorescent mesenchymal stem cells images using 1-D SIFT merging,” in 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, 2015, pp. 2495–2499.
[10]TOH, Peng Seng. Image acquisition system for machine vision applications. U.S. Patent No 6,346,966, . 2002.
[11]Cabada, R. Z., Estrada, M. L. B., & García, C. A. R. (2011). EDUCA: A web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network. Expert Systems with Applications, 38(8), 9522-9529.
[12]Rossi, F., Manenti, F., & Reklaitis, G. (2015). A general modular framework for the integrated optimal management of an industrial gases supply-chain and its production systems. Computers & Chemical Engineering, 82, 84-104.
[13]Antonov, A., & Neikova, K. (2016). OVERVIEW OF A SOFTWARE PRODUCT" ANYLOGIC" USED IN TRAINING OF STUDENTS IN GENERAL ENGINEERING. Journal Scientific & Applied Research, 9.
[14]R. Thakur and L. Workman, “Customer portfolio management (CPM) for improved customer relationship management (CRM): Are your customers platinum, gold, silver, or bronze?,” J. Bus. Res., vol. 69, no. 10, pp. 4095–4102, Oct. 2016.