Perishable fresh vegetables and volatile prices pose dual challenges to precise supply-chain decision-making, which is vital for retailer profitability. Traditional rule-based models are inadequate for rapidly changing market conditions, necessitating intelligent decision-support systems. This study transfers risk-warning, situational-awareness, and dynamic-decision techniques from AI security to fresh-vegetable supply-chain management and proposes an integrated framework combining intelligent sensing, panoramic insight, and dynamic optimization. The framework fuses end-to-end data from customer behavior, supply-chain operations, and external markets via intelligent sensors and a centralized data hub. Large-scale analysis extracts seasonal patterns and category associations, while video analytics generate dynamic customer-preference profiles to build demand and risk models. Short-term sales forecasting uses Autoregressive Integrated Moving Average (ARIMA), and multi-objective optimization employs an Evolutionary Algorithm, (EA) and Simple Genetic Algorithm (SGA). Risk-warning algorithms detect real-time disruptions (e.g., supplier delays, quality anomalies) and together with situational-awareness, enable dynamic pricing adjustments. Empirical results show the system improves supermarket operational efficiency, increases profit, and reduces spoilage. The main contribution is the adaptation of AI-security situation awareness and risk-control concepts to supply-chain management, providing a practical path for digital and intelligent transformation of traditional retail.
References
[1] Haji, M., Kerbache, L., Muhammad, M., Al-Ansari, T. (2020) Roles of technology in improving perishable food supply chains. Logistics, 4(4), 33.
[2] Wu, S., Cheng, H., Qin, Q. (2024) Physical delivery network optimization based on ant colony optimization neural network algorithm. International Journal of Information Systems and Supply Chain Management (IJISSCM), 17(1), 1-18.
[3] LI, Y., WU, G. (2022) Comparative analysis of data preprocessing methods based on typical data set. Journal of Shenyang University of Technology, 44(2), 185-192.
[4] Adithya Ganesan, V., Divi, S., Moudhgalya, N. B., Sriharsha, U., Vijayaraghavan, V. (2019) Forecasting food sales in a multiplex using dynamic artificial neural networks. Science and Information Conference, 69-80.
[5] Panda, S. K., Mohanty, S. N. (2023) Time series forecasting and modeling of food demand supply chain based on regressors analysis. IEEE Access, 11, 42679-42700.
[6] Albeladi, K., Zafar, B., Mueen, A. (2023) Time series forecasting using LSTM and ARIMA. International Journal of Advanced Computer Science and Applications, 14(1), 313-320.
[7] Cui, Z., Shi, X., Zhang, Z., Zhang, W., Chen, J. (2024) Many-objective joint optimization of computation offloading and service caching in mobile edge computing. Simulation Modelling Practice and Theory, 133, 102917.
[8] Opara, I. K., Silue, Y., Opara, U. L., Fawole, O. A. (2025) Forecasting of postharvest fresh produce waste at the wholesale level using time series models. Agriculture & Food Security, 14(1), 24.
[9] Tort, Ö. Ö., Vayvay, Ö., Çobanoğlu, E. (2022) A systematic review of sustainable fresh fruit and vegetable supply chains. Sustainability, 14(3), 1573.
[10] Ma, P., Jia, X., Gao, M., Yi, Z., Tsai, S., He, Y., Wang, F. (2024) Innovative food supply chain through spatial computing technologies: A review. Comprehensive Reviews in Food Science and Food Safety, 23(6), e70055.
[11] Pal, A., Kant, K. (2020) Smart sensing, communication, and control in perishable food supply chain. ACM Transactions on Sensor Networks (TOSN), 16(1), 1-41.
Share and Cite
Tan, Q., Yang, H., Yang, Z. (2025) Research on Dynamic Decision Optimization of Fresh Vegetable Supply Chain Based on Intelligent Security System. Journal of Social Development and History, 1(5), 118-128. https://doi.org/10.71052/jsdh/PLUL9736