Optimization Algorithm of Intelligent Warehouse Management System Based on Reinforcement Learning

Main Article Content

Jianjun Zhou


An Intelligent Warehouse Management System (IWMS) represents a technological leap forward in the realm of logistics and supply chain management. This sophisticated system integrates a suite of cutting-edge technologies, including artificial intelligence, machine learning, and the Internet of Things, to revolutionize the way warehouses operate. The primary focus is on the construction and performance evaluation of a robust big data prediction model within a cloud computing environment. The advent of big data and cloud computing has revolutionized the field of Logistics, offering immense potential for advanced data analysis and prediction. This research presents the development and evaluation of a robust prediction model for IWMS in Logistics applications. The proposed model incorporation of Reliable Discrete Variable Topology (RDVT) into the prediction model. RDVT introduces a topological data structure that enhances data reliability and ensures the integrity of Logistics information. The construction and training of the prediction model are meticulously detailed, encompassing data preprocessing, feature extraction, clustering, classification, and model evaluation. Additionally, the integration of fuzzy clustering with a reinforcement learning algorithm enhances the model's ability to handle uncertainty and imprecision in logistics management data. The advancement of Logistics in warehouses introduces the Reliable Discrete Variable Topology (RDVT) and a big data prediction model based on fuzzy clustering with a reinforcement learning algorithm in a cloud computing environment. The model's performance is rigorously assessed through extensive experimentation, including accuracy, precision, recall, and F1-score measurements.

Article Details

Author Biography

Jianjun Zhou

1Jianjun Zhou

1School of Finance and Business, Chengdu Vocational & Technical College of Industry, Chengdu 610218, China

*Corresponding author's e-mail: cdivtczhou@163.com

Copyright © JES 2024 on-line : journal.esrgroups.org


C. Medjahed, A.Rahmoun, C.Charrier, F.Mezzoudj, “A deep learning-based multimodal biometric system using score fusion,” IAES Int. J. Artif. Intell, vol.11, no.1, pp.65, 2022.

G.Muhammad et al., “A comprehensive survey on multimodal medical signals fusion for smart healthcare systems,” Information Fusion, vol.76, pp.355-375, 2021.

S.A.Wagan et al., “A fuzzy-based duo-secure multi-modal framework for IoMT anomaly detection,” Journal of King Saud University-Computer and Information Sciences, vol.35, no.1, pp.131-144, 2023.

A. G.Sreedevi, T. N.Harshitha, V.Sugumaran, P.Shankar, “Application of cognitive computing in healthcare, cybersecurity, big data and IoT: A literature review,” Information Processing & Management, vol.59, no.2, pp.102888, 2022.

N.Tenali, G.R.M.Babu, “A systematic literature review and future perspectives for handling big data analytics in COVID-19 diagnosis,” New Generation Computing, vol.41, no.2, pp.243-280, 2023.

J.Ehiabhi, H.Wang, “A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals,” BioMedInformatics, vol.3, no.1, pp.193-219, 2023.

A.Garg, V.Mago, “Role of machine learning in medical research: A survey,” Computer science review, vol.40, pp.100370, 2021.

M.Avanzo et al.,’ Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy,’ Physica Medica, vol.83, pp. 221-241, 2021.

M. H.Kashani et al., “A systematic review of IoT in healthcare: Applications, techniques, and trends,” Journal of Network and Computer Applications, vol.192, pp.103164, 2021.

G.Liu et al., “An enhanced intrusion detection model based on improved kNN in WSNs,” Sensors, vol.22, no.4, pp.1407, 2022.

X.Jiang, J.Ma, G.Xiao, Z.Shao, X.Guo, “A review of multimodal image matching: Methods and applications,” Information Fusion, vol.73, pp.22-71, 2021.

S.M.Nagarajan, G.G. Deverajan, P.Chatterjee, W.Alnumay, U.Ghosh, “Effective task scheduling algorithm with deep learning for Internet of Health Things (IoHT) in sustainable smart cities,” Sustainable Cities and Society, vol.71, pp.102945, 2021.

C.SaiTeja, J.B.Seventline, “A hybrid learning framework for multi-modal facial prediction and recognition using improvised non-linear SVM classifier,” AIP Advances, vol.13, no.2, 2023.

J. B.Awotunde, S.Oluwabukonla, C.Chakraborty, A. K.Bhoi, G. J. Ajamu, “Application of artificial intelligence and big data for fighting COVID-19 pandemic,” Decision Sciences for COVID-19: Learning Through Case Studies, pp.3-26, 2022.

Z.Wang, P.Zheng, X.Li, C.H.Chen, “Implications of data-driven product design: From information age towards intelligence age,” Advanced Engineering Informatics, vol.54, pp.101793, 2022.

Y. C.Yang, S. U.Islam, A.Noor, S.Khan, W.Afsar, S.Nazir, “Influential usage of big data and artificial intelligence in healthcare,” Computational and mathematical methods in medicine, vol.2021, 2021.

M.Rahimi et al., “Cloud healthcare services: A comprehensive and systematic literature review,” Transactions on Emerging Telecommunications Technologies, vol.33, no.7, pp.e4473, 2022.

X.Chen, D.Zou, H.Xie, F.L.Wang, “Past, present, and future of smart learning: a topic-based bibliometric analysis,” International Journal of Educational Technology in Higher Education, vol.18, pp.1-29, 2021.

Z.Ren, “Optimization of Innovative Education Resource Allocation in Colleges and Universities Based on Cloud Computing and User Privacy Security,” Wireless Personal Communications, pp.1-15, 2023.

K.S.Adewole et al., “Cloud-based IoMT framework for cardiovascular disease prediction and diagnosis in personalized E-health care,” In Intelligent IoT Systems in Personalized Health Care (pp. 105-145). Academic Press, 2021.

A.Gaonkar, Y.Chukkapalli, P.J.Raman, S.Srikanth, S.Gurugopinath, “A comprehensive survey on multimodal data representation and information fusion algorithms,” In 2021 International Conference on Intelligent Technologies (CONIT) (pp. 1-8). IEEE, 2021.

P.P.Ariza-Colpas et al., “human activity recognition data analysis: History, evolutions, and new trends,” Sensors, vol.22, no.9, pp.3401, 2022.

J.Yin, “Crime Prediction Methods Based on Machine Learning: A Survey,” Computers, Materials & Continua, vol.74, no.2, 2023.

J.Egger et al., “Medical deep learning—A systematic meta-review,” Computer methods and programs in biomedicine, vol.221, pp.106874, 2022.

H.K.Bharadwaj et al., “A review on the role of machine learning in enabling IoT based healthcare applications,” IEEE Access, vol.9, pp.38859-38890, 2021.

M.Mijwil, I.E.Salem, M.M.Ismaeel, “The Significance of Machine Learning and Deep Learning Techniques in Cybersecurity: A Comprehensive Review,” Iraqi Journal For Computer Science and Mathematics, vol.4, no.1, pp.87-101, 2023.

H.Yu, Z.Zhou, “Optimization of IoT-based artificial intelligence assisted telemedicine health analysis system,” IEEE access, vol.9, pp.85034-85048, 2021.