Dynamic multi-objective time-temperature management for climacteric fruit cold storage considering ripeness windows and energy consumption

文献类型： 外文期刊

第一作者： Chen, Qian

作者： Chen, Qian;Qian, Jianping;Li, Huibin;Lin, Xintao;Li, Jiali;Chen, Qian;Qian, Jianping;Li, Huibin;Lin, Xintao;Li, Jiali;Liu, Zihan;Zhao, Zhiyao

作者机构：

关键词： Climacteric fruit; Cold storage; Dynamic multi-objective optimization; Time-temperature management; Sustainability

期刊名称：JOURNAL OF FOOD ENGINEERING （ 影响因子：5.8； 五年影响因子：5.9 ）

ISSN： 0260-8774

年卷期： 2025 年 387 卷

页码：

收录情况： SCI

摘要： Cold chain logistics (CCL) can effectively maintain the quality and safety of perishable products through lowtemperature circulation, but extra energy is densely used at the cost of economy and emissions. The multiobjective trade-off is critical for promoting sustainable time-temperature management (TTM) in CCL. For climacteric fruit in cold storage, the post-harvest ripening process should be controlled by TTM to meet various ripeness requirements of retailers with different distribution distances, market conditions. Meanwhile, randomly appeared order demands will dynamically influence complex sustainable decision-making of temperaturecontrolled path. Therefore, this paper aims to solve the dynamic multi-objective TTM optimization problem considering ripeness windows and energy consumption, for achieving online sustainable temperature control of climacteric fruit cold storage. Constructing 3D service window involving ripeness, outbound time, and cargo volume, then the multi-retailer satisfaction is designed as a significant optimization objective. Additionally, to improve operational efficiency and reduce environmental impact, energy consumption needs to be minimized under reasonable constraints. Following above objective strategy, a dynamic multi-objective TTM model is proposed with two-stage: 1) improved NSGA-II-based global optimization to find optimal temperature-controlled path for fixed retailers in real-time; 2) new stochastic retailer comes up after demand matching, if accepting order, an update optimization will be triggered. A numerical study was implemented to verify proposed method, with robustness value of 0.82-1. Compared with constant low-temperature storage, the optimized comprehensive loss is the smallest at 3.374. The results indicate that the hybrid-retailer demand-driven multi-criteria decision support is effective and robust for sustainable TTM of climacteric fruit cold chain.

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