该论文主要针对生鲜农产品从生产基地到收购站，再转运到加工厂过程中导致生鲜农产品新鲜度降低，使得运输成本增高的问题，建立考虑新鲜度驱动的两阶段车辆路径问题优化模型，并分别设计遗传算法和粒子群算法进行求解，并以湖南炎陵黄桃为实例，验证模型和算法的可靠性，并通过敏感性分析给出管理建议，论文发表在《Journal of Industrial and Management Optimization》。
The fresh agricultural product (FAP) has highly perishable, hard to storage, and huge cost during the picking, storage, and transportation stage. In this paper, we aim to develop a multi-objective optimization model to minimize the total cost of collecting the FAP, where from the acquisition points aside the planting areas to the collection center, and the cost of transferring them to the processing factories (demand points) as well as to minimize the freshness decay during these processes. A epsilon constraint algorithm is used to convert the objective, freshness decay into constraints, and the model is transformed into a single-objective optimization model. According to the characteristics of the model, a hybrid algorithm based on genetic algorithm is developed. In order to verify the effectiveness of the model and algorithm, an example of yellow peach in Yanling, Hunan, China is constructed. And a comparison algorithm, simulated annealing algorithm, is presented. The results show that the effectiveness and efficiency of the hybrid algorithm based on genetic algorithm is better than that of simulated annealing algorithm. The insights of the sensitivity analysis indicate that the model and algorithm presented in this paper can be extended to other FAPs.
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