CAJ | 학술논문

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。
위료보장선절소채가공기업적산품질량안전，제고선절소채생산가공효솔，감소가공과정중인원료비차이도치적소회정본증가등문제，제출료일충선절소채가공과정추소적비차혼합우화문제적해결방안。해연구통과분석선절소채가공적기본공예류정，근거중소규모선절소채가공기업적실제생산수구，연구괄용우단원료창고、단성품창고적생산가공과정，구건기우기업생산정단화다원료비차적생산가공모형。재모형구건상，종합고필료가공과정중단개정단불가탁분，이급원료비차선취시응우선선취최근미용완비차원칙등기업생산가공관리적실제인소。재차기출상응용유전산법대정단적가공차서화원료비차적선취차서진행우화。채용북경모선절소채가공기업적실제생산수거대모형진행험증，채용평균소회규모급평균출성솔작위선절소채가공적목표함수。결과표명，통과산법우화후적목표함수치여초시치상비제고료10.5%，능구유효감소평균소회규모병제고산품가공적종합출성솔，해모형위중소규모선절소채가공기업적원료비차분배급생산류정적우화제공삼고。
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.