Research article Special Issues

Pricing and sales-cycle optimization for deteriorating fresh agricultural products with live-streaming investment and discounts

  • Published: 11 September 2026
  • 90B05, 90B50, 90C30

  • Live-streaming e-commerce creates a channel for selling fresh agricultural products, but e-tailers that operate with a fixed order quantity must balance demand conversion, inventory clearance, and deterioration losses. This paper developed a two-stage inventory model with a Weibull time-varying deterioration rate, live-streaming investment, deterioration-stage discounts, and live-streaming sales commission costs. The model jointly determines the regular selling price, sales-cycle length, and live-streaming investment intensity to maximize average profit per unit time. A second-order Maclaurin approximation and a structural solution method are used to identify candidates at the inventory-depletion boundary and exogenous sales-cycle bounds. Results showed that live-streaming investment and deterioration-stage discounts accelerate demand and clearance but do not necessarily increase profit, because their benefits may be offset by investment costs, sales commission costs, lower unit revenue, and deterioration-related costs. The optimal sales cycle is determined by boundary comparison rather than an interior first-order condition. The study provides an integrated basis for coordinated pricing, live-streaming investment, and inventory-clearance decisions in fresh-product e-commerce.

    Citation: Lijun Wu, Xuan Li, Haiping Ren. Pricing and sales-cycle optimization for deteriorating fresh agricultural products with live-streaming investment and discounts[J]. Journal of Industrial and Management Optimization, 2026, 22(10): 4945-4977. doi: 10.3934/jimo.2026171

    Related Papers:

  • Live-streaming e-commerce creates a channel for selling fresh agricultural products, but e-tailers that operate with a fixed order quantity must balance demand conversion, inventory clearance, and deterioration losses. This paper developed a two-stage inventory model with a Weibull time-varying deterioration rate, live-streaming investment, deterioration-stage discounts, and live-streaming sales commission costs. The model jointly determines the regular selling price, sales-cycle length, and live-streaming investment intensity to maximize average profit per unit time. A second-order Maclaurin approximation and a structural solution method are used to identify candidates at the inventory-depletion boundary and exogenous sales-cycle bounds. Results showed that live-streaming investment and deterioration-stage discounts accelerate demand and clearance but do not necessarily increase profit, because their benefits may be offset by investment costs, sales commission costs, lower unit revenue, and deterioration-related costs. The optimal sales cycle is determined by boundary comparison rather than an interior first-order condition. The study provides an integrated basis for coordinated pricing, live-streaming investment, and inventory-clearance decisions in fresh-product e-commerce.



    加载中


    [1] National Bureau of Statistics of China, Total retail sales of consumer goods in December 2025, 2026. Available from: https://www.stats.gov.cn/sj/zxfb/202601/t20260119_1962323.html.
    [2] People's Daily, Rural online retail sales grew by 6.7% in 2025, 2026. Available from: https://paper.people.com.cn/rmrb/pc/content/202602/08/content_30139315.html.
    [3] China International Electronic Commerce Center Research Institute, Report on the high-quality development of live-streaming e-commerce, 2025. Available from: https://ciecc.ec.com.cn/upload/article/20250508/20250508102036216.pdf.
    [4] D. Z. Zhang, S. Chen, N. Zhou, P. Shi, Location optimization of fresh food e-commerce front warehouse, Math. Biosci. Eng. , 20 (2023), 14899-14919.https://doi.org/10.3934/mbe.2023667 doi: 10.3934/mbe.2023667
    [5] Z. Song, C. Liu, R. Shi, How do fresh live broadcast impact consumers' purchase intention? Based on the SOR theory, Sustainability, 14 (2022), 14382.https://doi.org/10.3390/su142114382 doi: 10.3390/su142114382
    [6] X. G. Zhang, N. Mo, Ordering and pricing strategy of perishable goods inventory based on Weibull function and price discount, J. Chongqing Norm. Univ. Nat. Sci., 37 (2020), 1-5.
    [7] W. H. Jiang, X. D. Ding, Y. L. Li, L. Xu, Joint decisions on ordering, pricing and preservation technology investment for deteriorating items with stock-dependent demand, Control Decis. , 35 (2020), 2578-2588.https://doi.org/10.13195/j.kzyjc.2019.0195 doi: 10.13195/j.kzyjc.2019.0195
    [8] X. Y. Ai, J. L. Zhang, H. X. Xu, L. Wang, Joint pricing and replenishment policy for non-instantaneous deteriorating items considering investment in preservation technology, J. Syst. Manag. , 29 (2020), 150-157.https://doi.org/10.3969/j.issn.1005-2542.2020.01.016 doi: 10.3969/j.issn.1005-2542.2020.01.016
    [9] G. P. Li, Y. R. Duan, J. Z. Huo, Ordering, pricing and preservation investment decision for non-instantaneously deteriorating items, Syst. Eng. Theory Pract. , 36 (2016), 1422-1434.
    [10] L. G. Cui, Y. L. Li, J. X. Liu, Y. Tian, Joint replenishment and pricing decisions for multiple products considering preservation technology investment, Ind. Eng. Manag. , 28 (2023), 17-26.https://doi.org/10.19495/j.cnki.1007-5429.2023.03.003 doi: 10.19495/j.cnki.1007-5429.2023.03.003
    [11] J. Kaushik, Inventory model for perishable items for ramp type demand with an assumption of preservative technology and Weibull deterioration, Int. J. Procure. Manag. , 18 (2023), 238-259.https://doi.org/10.1504/IJPM.2023.133232 doi: 10.1504/IJPM.2023.133232
    [12] J. Kaushik, A business analytics inventory model for ramp-type demand with Weibull deterioration rate: A risk management approach, Oper. Res. Forum, 5 (2024), 115.https://doi.org/10.1007/s43069-024-00393-x doi: 10.1007/s43069-024-00393-x
    [13] J. Kaushik, Optimal replenishment policy for deteriorating items with linear demand and Weibull deterioration: Adapting to changing customer preferences, Int. J. Syst. Assur. Eng. Manag., (2025).https://doi.org/10.1007/s13198-025-03030-w
    [14] S. M. Moshtagh, Y. Zhou, M. Verma, Optimal markdown policies for perishable products with fixed shelf life, Int. J. Prod. Res., 63 (2025), 5692-5722.https://doi.org/10.1080/00207543.2025.2461133 doi: 10.1080/00207543.2025.2461133
    [15] S. M. Moshtagh, Y. Zhou, M. Verma, Dynamic inventory and pricing control of a perishable product with multiple shelf-life phases, Transp. Res. Part E Logist. Transp. Rev. , 195 (2025), 103960.https://doi.org/10.1016/j.tre.2025.103960 doi: 10.1016/j.tre.2025.103960
    [16] H. Zhou, K. Chen, S. Wang, Two-period pricing and inventory decisions of perishable products with partial lost sales, Eur. J. Oper. Res., 310 (2023), 611-626.https://doi.org/10.1016/j.ejor.2023.03.010 doi: 10.1016/j.ejor.2023.03.010
    [17] M. Hasiloglu-Ciftciler, O. Kaya, Dynamic inventory sharing, ordering, and pricing strategies for perishable foods to maximize profit and minimize waste, Comput. Ind. Eng. , 205 (2025), 111158.https://doi.org/10.1016/j.cie.2025.111158 doi: 10.1016/j.cie.2025.111158
    [18] A. Salmasnia, F. Kohan, Joint optimization of pricing, inventory control, and preservation technology investment under both quality and quantity deteriorating, Sci. Iran., 31 (2024), 269-281.https://doi.org/10.24200/sci.2021.57804.5424 doi: 10.24200/sci.2021.57804.5424
    [19] A. Kavoosi, R. Tavakkoli-Moghaddam, H. Sajedi, N. Tajik, K. Tafakkori, Dynamic pricing and inventory control of perishable products by a deep reinforcement learning algorithm, Expert Syst. Appl., 291 (2025), 128570.https://doi.org/10.1016/j.eswa.2025.128570 doi: 10.1016/j.eswa.2025.128570
    [20] N. Mohamadi, S. T. Akhavan Niaki, M. Taher, A. Shavandi, An application of deep reinforcement learning and vendor-managed inventory in perishable supply chain management, Eng. Appl. Artif. Intell. , 127 (2024), 107403.https://doi.org/10.1016/j.engappai.2023.107403 doi: 10.1016/j.engappai.2023.107403
    [21] L. Hou, T. Nie, J. Zhang, Pricing and inventory strategies for perishable products in a competitive market considering strategic consumers, Transp. Res. Part E Logist. Transp. Rev. , 184 (2024), 103478.https://doi.org/10.1016/j.tre.2024.103478 doi: 10.1016/j.tre.2024.103478
    [22] J. Chen, S. Kang, Joint decision on pricing and inventory replenishment of agri-food with dual-channel sales, Ind. Eng. J. , 26 (2023), 39-46.https://doi.org/10.3969/j.issn.1007-7375.2023.03.005 doi: 10.3969/j.issn.1007-7375.2023.03.005
    [23] L. Pan, X. J. Xu, R. T. Zhou, Dual-channel dynamic pricing of community fresh food supply chains from a competition perspective, Chin. J. Manag. Sci. , 32 (2024), 300-310.https://doi.org/10.16381/j.cnki.issn1003-207x.2021.1506 doi: 10.16381/j.cnki.issn1003-207x.2021.1506
    [24] W. L. Wang, Z. Y. He, S. X. Zhang, Joint decision of freshness-keeping effort and promotion effort in a dual-channel fresh produce supply chain from a dynamic perspective, Chin. J. Manag. Sci. , 33 (2025), 301-311.https://doi.org/10.16381/j.cnki.issn1003-207x.2022.2070 doi: 10.16381/j.cnki.issn1003-207x.2022.2070
    [25] Y. Liu, B. Yan, X. Chen, Decisions of dual-channel fresh agricultural product supply chains based on information sharing, Int. J. Retail Distrib. Manag. , 52 (2024), 910-930.https://doi.org/10.1108/IJRDM-10-2022-0401 doi: 10.1108/IJRDM-10-2022-0401
    [26] Y. W. Bian, X. Q. Hou, S. Yan, X. Yan, Store sales mode selection strategies for fresh products considering product freshness, Front. Sci. Technol. Eng. Manag. , 45 (2026), 42-50.
    [27] M. Y. Zhang, J. T. Guo, X. G. Yu, Joint pricing and inventory decisions for perishable agricultural products under e-commerce, Forecasting, 40 (2021), 32-37.
    [28] Y. Cao, C. Q. Yi, G. Y. Wan, Pricing and service decisions in a dual-channel supply chain based on manufacturer online channel selection, J. Ind. Eng. Eng. Manag. , 35 (2021), 189-199.https://doi.org/10.13587/j.cnki.jieem.2021.02.017 doi: 10.13587/j.cnki.jieem.2021.02.017
    [29] H. Du, K. Lu, Visualization service investment strategies for a self-operated fresh agricultural product e-tailer, J. Retail. Consum. Serv. , 75 (2023), 103455.https://doi.org/10.1016/j.jretconser.2023.103455 doi: 10.1016/j.jretconser.2023.103455
    [30] Y. B. Xiao, X. H. Wang, J. Yu, C. Zhao, Live-streaming e-commerce: Management challenges and potential research directions, Chin. J. Manag. Sci. , 33 (2025), 251-264.https://doi.org/10.16381/j.cnki.issn1003-207x.2021.1113 doi: 10.16381/j.cnki.issn1003-207x.2021.1113
    [31] W. Jin, W. Zhang, The impact of e-commerce live streaming on purchase intention for sustainable green agricultural products: A study in the context of agricultural tourism integration, Sustainability, 17 (2025), 6850.https://doi.org/10.3390/su17156850 doi: 10.3390/su17156850
    [32] Y. Sun, X. Shao, X. Li, Y. Guo, K. Nie, How live streaming influences purchase intentions in social commerce: An IT affordance perspective, Electron. Commer. Res. Appl. , 37 (2019), 100886.https://doi.org/10.1016/j.elerap.2019.100886 doi: 10.1016/j.elerap.2019.100886
    [33] J. Guo, Y. Li, Y. Xu, K. Zeng, How live streaming features impact consumers purchase intention in the context of cross-border e-commerce? A research based on SOR theory, Front. Psychol. , 12 (2021), 767876.https://doi.org/10.3389/fpsyg.2021.767876 doi: 10.3389/fpsyg.2021.767876
    [34] H. Sima, S. Wu, Z. J. Yan, N. Luo, Y. Chen, The impact of livestreaming e-commerce on consumer purchase intention in China, J. Consum. Mark. , 42 (2025), 717-730.https://doi.org/10.1108/JCM-02-2024-6564 doi: 10.1108/JCM-02-2024-6564
    [35] L. A. San-José, J. Sicilia, B. Alcaide, Optimal policy for an inventory system with demand dependent on price, time and frequency of advertisement, Comput. Oper. Res., 128 (2021), 105169.https://doi.org/10.1016/j.cor.2020.105169 doi: 10.1016/j.cor.2020.105169
    [36] A. N. Liang, S. Y. Wang, Dynamic pricing and production optimization for two-stage sales of fresh agricultural products, J. Highw. Transp. Res. Dev., 41 (2024), 214-222.https://doi.org/10.3969/j.issn.1002-0268.2024.04.022 doi: 10.3969/j.issn.1002-0268.2024.04.022
    [37] W. L. Wang, Y. Ren, S. X. Zhang, The impact of live-streaming commerce on domain traffic investment and channel selection in agricultural product supply chains, Chin. J. Manag., 20 (2023), 1216-1224.https://doi.org/10.3969/j.issn.1672-884x.2023.08.012 doi: 10.3969/j.issn.1672-884x.2023.08.012
    [38] F. Zhang, H. M. Liu, J. Wu, Comparison of sales modes in fresh agricultural product supply chains considering live-streaming commerce, Comput. Eng. Appl. , 59 (2023), 293-304.https://doi.org/10.3778/j.issn.1002-8331.2303-0270 doi: 10.3778/j.issn.1002-8331.2303-0270
    [39] J. Yu, L. Wang, W. Q. Zhang, C. Zhou, Winning markets via live-streams: Competitive manufacturers' channel strategies, J. Retail. Consum. Serv. , 87 (2025), 104391.https://doi.org/10.1016/j.jretconser.2025.104391 doi: 10.1016/j.jretconser.2025.104391
    [40] Y. Y. Chen, Y. Yuan, Q. G. Bai, Y. Y. Wang, X. P. Cao, Pricing and coordination in a dual-channel supply chain considering streamer marketing effort, Comput. Eng. Appl., 61 (2025), 322-331.https://doi.org/10.3778/j.issn.1002-8331.2410-0284 doi: 10.3778/j.issn.1002-8331.2410-0284
    [41] Z. Zhang, Z. Chen, M. Wan, S. Qiu, Choice of product quality in supply chain of live-streaming e-commerce under different power structures, Aust. J. Manag. , 50 (2025), 266-286.https://doi.org/10.1177/03128962231196322 doi: 10.1177/03128962231196322
    [42] X. Zhang, J. Zhang, Pricing and channel selection strategies in e-commerce supply chain with hybrid channels and live streaming, J. Syst. Manag., 34 (2025), 27-39.
    [43] W. J. Yang, J. T. Zhang, W. T. Yang, J. X. Wei, Live-stream promotional pricing strategies of online retailers considering strategic consumers, J. Syst. Manag. , 35 (2026), 127-143.
    [44] C. Zhou, J. Yu, Y. Qian, Should live-streaming platforms nonexclusively promote brands from traditional retail platforms? J. Retail. Consum. Serv., 80 (2024), 103930.https://doi.org/10.1016/j.jretconser.2024.103930
    [45] Z. Chen, C. Ji, L. Zhang, J. Zhang, Strategic choices in live-streaming e-commerce: Balancing live-streaming selling mode and return-freight insurance strategy, J. Retail. Consum. Serv., 86 (2025), 104347.https://doi.org/10.1016/j.jretconser.2025.104347 doi: 10.1016/j.jretconser.2025.104347
    [46] L. Wang, Y. Song, T. J. Fan, Impact of introduction of live streaming on dual-channel retailing, J. Syst. Manag. , 34 (2025), 231-240.
  • Reader Comments
  • © 2026 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(183) PDF downloads(17) Cited by(0)

Article outline

Figures and Tables

Figures(5)  /  Tables(8)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog