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| 基于蚁群算法的荔枝冷链物流配送成本模型优化 |
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曾志雄1,2, 邹炽导1,3, 韦鉴峰1,4, 陆华忠1,2, 吕恩利1,2, 阮清松1
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1.华南农业大学 工程学院，广州 510642;2.国家农产品冷链物流装备研发专业中心，广州 510642;3.广东省教育考试院，广州 510631;4.桂林航天工业学院，桂林 541004
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| 摘要: |
| 目的 为了确保荔枝的采摘、预冷、贮藏、配送和销售等环节能在短时间内完成，以提高品质安全和新鲜度，降低物流成本。方法 以配送车运输、冷链能耗、荔枝损耗、时间窗惩罚等4个主要因素为研究对象，构建各因素成本模型，并确定荔枝冷链物流配送过程中成本最优的目标优化函数。利用蚁群算法对荔枝冷链物流配送过程中成本最优目标函数算例进行求解，得到荔枝冷链物流配送路径网络优化路线图。结果 当算法启发因子α=1，期望启发式因子β=4，信息素的挥发程度系数ρ=0.75，蚁群数量m=600，信息素强度系数Q=0.9，最大迭代次数Nmax=600，迭代次数超过300时，迭代趋于稳定。结论 当算法运算的迭代次数越多，其优化结果越趋于稳定。该优化模型能够实现对荔枝冷链物流配送成本的优化设计，提高荔枝配送效率，并降低物流成本，为荔枝的冷链物流配送提供参考。 |
| 关键词: 荔枝 冷链物流 模型构建 优化模型 蚁群算法 |
| DOI：10.19554/j.cnki.1001-3563.2019.11.008 |
| 分类号:S377 |
| 基金项目:国家重点研发计划（2018YFD0701002）；现代农业产业技术体系建设专项（CARS-33-13）；广东省科技计划（2017B020206005）；广东省省级（基础研究及应用研究）重大项目（2016KZDXM028）；广州市科技计划（201704020067）；广东大学生科技创新培育专项资金（PDJHA0072） |
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| Optimization of Distribution Cost Model of Cold Chain Logistics for Litchi Based on Ant Colony Algorithm |
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ZENG Zhi-xiong1,2, ZOU Chi-dao1,3, WEI Jian-feng1,4, LU Hua-zhong1,2, LYU En-li1,2, RUAN Qing-song1
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1.College of Engineering, South China Agricultural University, Guangzhou 510642, China;2.National Research Center of Cold Chain Logistics Equipment of Agricultural Product, Guangzhou 510640, China;3.Education Examinations Authority of Guangdong Province, Guangzhou 510631, China;4.Guilin University of Aerospace Technology, Guilin 541004, China
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| Abstract: |
| The work aims to ensure that the picking, pre-cooling, storage, distribution and sales of litchi can be completed in a short time, so as to improve quality safety and freshness, and reduce logistics costs. With the four main factors like distribution vehicle transportation, cold chain energy consumption, litchi loss and time window penalty as study objects, the cost model of each factor was constructed and the cost optimal objective optimization function in the process of litchi cold chain logistics distribution was determined. The ant colony algorithm was used to solve the cost optimal objective function example in the process of litchi cold chain logistics distribution, so as to obtain the network optimization roadmap of litchi cold chain logistics distribution path. The experimental results showed that when the algorithm heuristic factor α=1, the expected heuristic factor β=4, the pheromone volatilization coefficient ρ=0.75, the ant colony number m=600, the pheromone intensity coefficient Q=0.9, the maximum iteration number Nmax=600, and the number of iterations exceeded 300, the iteration tended to be stable. The more the number of iterations of the algorithm is, the more stable the optimization result is. The optimization model can optimize the distribution cost of litchi cold chain logistics, improve the distribution efficiency of litchi and reduce the logistics cost, and provide reference for litchi cold chain logistics distribution. |
| Key words: litchi cold chain logistics model building optimization model ant colony algorithm |
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