杨信廷,王健,钱建平,邢斌,曹文琴,王贵用.基于批次关联的鲜切蔬菜采购成本-召回规模联合优化[J].农业机械学报,2016,47(2):222-227.
Yang Xinting,Wang Jian,Qian Jianping,Xing Bin,Cao Wenqin,Wang Guiyong.Joint Optimization of Purchasing Cost Recall Scale Model of Fresh-cut Vegetables Based on Batch Association[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(2):222-227.
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基于批次关联的鲜切蔬菜采购成本-召回规模联合优化   [下载全文]
Joint Optimization of Purchasing Cost Recall Scale Model of Fresh-cut Vegetables Based on Batch Association   [Download Pdf][in English]
投稿时间:2015-09-06  
DOI:10.6041/j.issn.1000-1298.2016.02.029
中文关键词:  鲜切加工  批次混合  采购成本  召回  多目标优化
基金项目:“十二五”国家科技支撑计划项目(2013BAD19B04)
作者单位
杨信廷 北京农业信息技术研究中心 
王健 北京农业信息技术研究中心
华东交通大学 
钱建平 北京农业信息技术研究中心 
邢斌 北京农业信息技术研究中心 
曹文琴 华东交通大学 
王贵用 华东交通大学 
中文摘要:鲜切蔬菜从采购、加工到包装存在批次混合,批次混合程度和采购成本相互制约,因此提出一种建立原材料与订单产品批次关联的采购-召回方案。根据鲜切加工企业的生产计划和加工流程,满足订单要求和供应商的原材料条件,建立批次混合程度和采购成本为目标函数的混合整形线性规划(Mixed-integer linear programing, MILP)模型。使用LINGO对模型逐步求解,结果显示,随着批次混合程度的增加原材料的采购成本逐渐降低,当批次混合程度为10时,采购成本最低为2 840.33元,平均召回数量177.50 kg,适用于中小型鲜切加工企业。
Yang Xinting  Wang Jian  Qian Jianping  Xing Bin  Cao Wenqin  Wang Guiyong
Beijing Research Center for Information Technology in Agriculture,Beijing Research Center for Information Technology in Agriculture,Beijing Research Center for Information Technology in Agriculture; East China Jiaotong University,Beijing Research Center for Information Technology in Agriculture,East China Jiaotong University and East China Jiaotong University
Key Words:fresh-cut processing  batch mixing  purchase cost  recall  multi-objective optimization
Abstract:The batch mixing exists for fresh-cut vegetables from purchasing, processing to packaging, and there is mutual restrictive relationship between batch mixing degree and purchasing cost. The single- objective model cannot meet the requirement of flexible production. Therefore, a kind of purchasing cost- recall model was proposed to build batch relations between raw material and order products. According to production plan and process flow of fresh-cut processing enterprise, taking the batch mixing degree and the purchasing cost as objective functions, a mixed-integer linear programming model was established to meet the order requirements and the suppliers’ raw material conditions. Since the weight of recall number was more important than that of procurement costs, and high correspondence relationship existed between recall number and batch mixing degree, the hierarchical sequence method was used to solve the multi-object model. The established model was analyzed by the LINGO software in the step-by-step solution process. The model performance was evaluated with parameters, such as purchase cost, average number of recall and the maximum number of recall. The results showed that with the increase of batch mixing degree, the procurement costs of raw materials were reduced gradually. When batch mixing degree was 10, the lowest cost of purchase was 2 840.33 yuan, the average recall quantity was 177.50 kg, and the maximum recall quantity was 420 kg. In order to evaluate the practical applicability of the model, the average recall ratio and maximum recall ratio were introduced. The results showed that when batch mixing degree was 10, the average recall ratio and maximum recall ratio were 15.5% and 36.8%, respectively, which were suitable for medium and small fresh-cut processing enterprises.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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