文章摘要
刘琳,贾鹏,高犇,赵雪婷.新鲜度限制约束下物流配送中心选址-路径优化[J].包装工程,2022,(5):232-241.
LIU Lin,JIA Peng,GAO Ben,ZHAO Xue-ting.Location Routing Optimization of Logistics Distribution Center under Freshness Limitation[J].Packaging Engineering,2022,(5):232-241.
新鲜度限制约束下物流配送中心选址-路径优化
Location Routing Optimization of Logistics Distribution Center under Freshness Limitation
投稿时间:2021-03-15  
DOI:10.19554/j.cnki.1001-3563.2022.05.032
中文关键词: 选址-路径问题  新鲜度约束  物流配送中心  双层目标规划模型
英文关键词: location routing  freshness limitation  distribution center  two-level programming model
基金项目:国家自然科学基金(72174035,71774018);辽宁省“兴辽英才计划”(XLYC2008030);辽宁省社会科学规划基金(L21CGL003)
作者单位
刘琳 大连海事大学 交通运输工程学院辽宁 大连 116026 
贾鹏 大连海事大学 综合交通运输协同创新中心辽宁 大连 116026 
高犇 大连海事大学 航运经济与管理学院辽宁 大连 116026 
赵雪婷 大连海事大学 航运经济与管理学院辽宁 大连 116026 
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中文摘要:
      目的 满足生鲜产品交付时较高的新鲜度要求,解决多产品、多车型情景下的配送中心选址-路径优化问题。方法 构建考虑碳排放成本和满足客户对产品交付最低新鲜度要求的双层目标规划模型。上层模型以配送中心固定成本、库存管理成本最小化为优化目标,下层模型以车辆固定成本、运输成本、碳排放成本、惩罚成本最小化为优化目标,并结合模型特点,采用两阶段启发式算法进行求解。结果 采用的两阶段启发式算法相对于遗传算法的平均成本解改进率为1.22%,相对于K-means聚类求解算法的平均解改进率为3.03%;两阶段启发式算法相对于遗传算法最优解运算时间的平均提高率为24.8%,相对于传统K-means聚类求解算法的平均提高率为33.0%。结论 经算例对比研究发现,不同新鲜度要求下对配送中心的选址以及车辆路径的安排有显著影响,企业可通过合理规划物流网络和准确评估客户对产品的新鲜度要求等手段实现企业物流成本的降低。
英文摘要:
      The work aims to meet the requirements of high freshness when fresh products are delivered, and realize the optimization of location routing of distribution center under the scenario of multiple products and multiple vehicle models. A two-level goal programming model was constructed in view of considering the cost of carbon emission and meeting the customers' requirements for the minimum freshness of product at delivery. The optimization goal of the upper model was to minimize the fixed cost of distribution center and inventory management cost, while the optimization goal of the lower model was to minimize the fixed cost of vehicles, transportation cost, carbon emission cost and punishment cost. Combined with the characteristics of the model, a two-stage heuristic algorithm was used to solve the problem. The average cost improvement rate of the two-stage heuristic algorithm was 1.22% compared with that of genetic algorithm, and 3.03% compared with that of K-means clustering algorithm. The average improvement rate of the two-stage heuristic algorithm was 24.8% compared with that of genetic algorithm, and 33.0% compared with that of traditional K-means clustering algorithm. Through the comparative study of calculation examples, different freshness requirements have a significant impact on the location of distribution centers and the arrangement of vehicle routes. Enterprises can reduce logistics costs by means of reasonable logistics network planning and accurate evaluation of customers' requirements for product freshness.
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