{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:27:16Z","timestamp":1760146036601,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chunhui Project of the Chinese Ministry of Education","award":["202201245"],"award-info":[{"award-number":["202201245"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper addresses the challenges of automated pricing and replenishment strategies for perishable products with time-varying deterioration rates, aiming to assist wholesalers and retailers in optimizing their production, transportation, and sales processes to meet market demand while minimizing inventory backlog and losses. The study utilizes an improved convolutional neural network\u2013long short-term memory (CNN-LSTM) hybrid model, autoregressive moving average (ARIMA) model, and random forest\u2013grey wolf optimization (RF-GWO) algorithm. Using fresh vegetables as an example, the cost relationship is analyzed through linear regression, sales volume is predicted using the LSTM recurrent neural network, and pricing is forecasted with a time series analysis. The RF-GWO algorithm is then employed to solve the profit maximization problem, identifying the optimal replenishment quantity, type, and most effective pricing strategy, which involves dynamically adjusting prices based on predicted sales and market conditions. The experimental results indicate a 5.4% reduction in inventory losses and a 6.15% increase in sales profits, confirming the model\u2019s effectiveness. The proposed mathematical model offers a novel approach to automated pricing and replenishment in managing perishable goods, providing valuable insights for dynamic inventory control and profit optimization.<\/jats:p>","DOI":"10.3390\/sym16091245","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T03:36:38Z","timestamp":1727062598000},"page":"1245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Pricing and Dynamic Replenishment Planning Strategies for Perishable Vegetables Based on the RF-GWO Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Yongjun","family":"Pu","sequence":"first","affiliation":[{"name":"Intelligent Terminal Industry College, Chengdu Technological University, Chengdu 611730, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0116-3196","authenticated-orcid":false,"given":"Zhonglin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Network and Communication Engineering, Chengdu Technological University, Chengdu 611730, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Network and Communication Engineering, Chengdu Technological University, Chengdu 611730, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianrong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu 611730, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,22]]},"reference":[{"key":"ref_1","first-page":"267","article-title":"Leveraging Artificial Intelligence for Enhanced Sales Forecasting Accuracy: A Review of AI-Driven Techniques and Practical Applications in Customer Relationship Management Systems","volume":"4","author":"Venkataramanan","year":"2024","journal-title":"Aust. 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