Abstract:

The application of the emerging mobile grading and pre-cooling technology to the post-harvest processing of fruits and vegetables in the field can help reduce quality losses, which has also spawned the problem of collaborative optimization of post-harvest first mile cold chain logistics links. Taking the post-harvest grading and pre-cooling stages as examples, this paper comprehensively considers the specific collaborative scenario including the best pre-cooling time for fruits and vegetables and the service order of first grading and then pre-cooling, and develops a collaborative optimization model for scheduling grading and pre-cooling resources. Different from the existing related models, this paper considers special effect of delayed pre-cooling on the freshness of fruits and vegetables, and designs a delayed pre-cooling cost function to minimize service operation costs while ensuring product quality. It designs a hybrid genetic algorithm to solve the model, which combines the genetic algorithm and the neighborhood search algorithm to enhance the local and global search capabilities of the hybrid algorithm. In the proposed algorithm, the solution representation based on the dual sequence, the crossover operator based on the optimal insertion strategy, and the three-stage neighborhood search-based mutation operator are developed in combination with the dual-demand characteristics of the problem and key collaborative constraints, aiming to improve the convergence speed and solution quality of the algorithm. Compared with the standard genetic algorithm and the variable neighborhood search algorithm, it is verified that the algorithm proposed in this paper can converge to higher quality solutions faster when solving large-scale instances. The validity of the model is proved using the practical grading and pre-cooling data of the peach industry in Luochuan County, Shaanxi Province. This paper helps to introduce the idea of collaborative optimization into the first mile cold chain logistics of post-harvest fruits and vegetables, and provides innovative solutions for reducing post-harvest losses of fruits and vegetables in China.

Key words: the first mile, mobile grading and pre-cooling, collaborative scheduling, hybrid genetic algorithm, neighborhood search

摘要：

新兴的移动式分级、预冷技术应用于果蔬田间采后处理，有助于降低采后损耗，也催生了采后“最先一公里”冷链物流环节协同运作优化问题。以采后分级、预冷环节为例，综合考虑果蔬最佳预冷时间、先分级后预冷的服务顺序等特有协同情景，构建了移动式分级预冷资源协同调度优化模型。与现有模型不同，本研究考虑延迟预冷对果蔬新鲜度的特殊影响，设计了延迟预冷成本函数，在保障产品质量的同时最小化服务运作成本。设计混合遗传算法对模型进行求解，该算法融合了遗传算法与邻域搜索算法，增强混合算法的局部和全局搜索能力。其中，结合问题的双需求特点及关键协同约束，设计了基于双序列的解的表达方式、基于最佳插入策略的交叉算子以及基于三阶段邻域搜索的变异操作，以提高算法的收敛速度与求解质量。通过与标准遗传算法和变邻域搜索算法对比，验证了本文算法在求解大规模算例时可以更快收敛到更高质量的解。基于陕西省洛川县水蜜桃产业的分级预冷数据证明了模型的合理性。本研究有助于把协同运作优化思想引入果蔬采后“最先一公里”冷链物流环节，为降低我国果蔬采后损耗提供创新性解决思路。

关键词: 最先一公里, 移动式分级和预冷, 协同调度, 混合遗传算法, 邻域搜索