果蔬采后分级和预冷车辆协同调度模型与算法OACSTPCD

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.