鲜切蔬菜加工过程追溯的原料批次混合优化模型构建

为了保障鲜切蔬菜加工企业的产品质量安全,提高鲜切蔬菜生产加工效率,减少加工过程中因原料批次而导致的召回正本增加等问题,提出了一种鲜切蔬菜加工过程追溯的批次混合优化问题的解决方案。该研究通过分析鲜切蔬菜加工的基本工艺流程,根据中小规模鲜切蔬菜加工企业的实际生产需求,研究适用于单原料仓库、单成品仓库的生产加工过程,构建基于企业生产订单和多原料批次的生产加工模型。在模型构建上,综合考虑了加工过程中单个订单不可拆分,以及原料批次选取时应优先选取最近未用完批次原则等企业生产加工管理的实际因素。在此基础上应用遗传算法对订单的加工次序和原料批次的选取次序进行优化。采用北京某鲜切蔬菜加工企业的实际生产数据对模型进行验证,采用平均召回规模及平均出成率作为鲜切蔬菜加工的目标函数。结果表明,通过算法优化后的目标函数值与初始值相比提高了10.5%,能够有效减少平均召回规模并提高产品加工的综合出成率,该模型为中小规模鲜切蔬菜加工企业的原料批次分配及生产流程的优化提供参考。

Establishment of materials batch mixing optimization model for traceability of fresh-cuts fruits and vegetables processing

In order to protect the quality and safety of products of the fresh cut vegetable processing and improve fresh cut vegetable production and processing efficiency, a fresh cut vegetable processing batch mixing optimization model was put forward, which can reduce the recall cost resulted from the mixed batches during the manufacture. The batch-mixing phenomenon of raw materials often occurs during the production process of fresh-cut vegetables. On the other hand, different batches of materials have different properties which will exert some effects on the rate of the production yield. Proper allocation of the materials' batches will reduce the recall cost of defective products. In order to enhance the ability of production efficiency and improve the supervision on the quality and safety for fresh-cut vegetables, an optimized model for this problem should be developed. In this research, after the analysis of the basic process in fresh-cut vegetables manufacture, a model based on the order of the daily manufacture in enterprise and the batch of materials was established, which could reduce the batch-mixing level of the materials and improve the production efficiency. The manufacture process of the fresh-cut vegetables was taken as the core research point, and the experiment parameters were obtained from a cooperative enterprise in Beijing which was a typical manufacture enterprise of fresh-cut vegetables. Unlike other areas of vegetable manufacture, the manufacture of fresh-cut vegetables needed to take the different products and different batches of material processing into account. According to the actual production requirements of small and medium-sized fresh-cut vegetable processing enterprises, the production process, which could be well applicable to the pattern of single material warehouse and single warehouse of finished product, was studied in details. Based on the reality limits, the orders' sequence and the batch of materials should be optimized. The processing of product order and the selection and sorting of raw material batch belong to the NP-Hard problem, and the scale of the problem rapidly increased with the expanding of the order and batch. So it was necessary to adopt intelligent optimization methods to solve the problem. And based on the actual condition in the manufacture enterprises of fresh-cut vegetables, the processing sequence of the manufacture order and the selection sequence of the different batches of materials were optimized using genetic algorithm. The population size was set to 100, two-point crossover rate was set to 0.9, the mutation rate was set to 0.1, the weight of material mixing was set to 0.3, the average yield for the processing was set to 0.7 and the maximum amount of iterations was limited to 50. The objective function consisted of the average recall size and the average yield of product. The model was applied in a manufacture enterprise of fresh-cut fruits and vegetables, and the test results showed that the value of the objective function was improved by 10.5%, which could reduce the average recall rate and improve the production efficiency. This model can provide suggestion of material batch distribution and optimization of production for the small and medium-sized fresh-cut vegetable processing enterprises.