基于改进布谷鸟算法的物流配送中心选址研究

Research on Location Selection of Logistics Distribution Center Based on Improved Cuckoo Search Algorithm

近年来,物流行业不断发展壮大,配送中心作为物流系统中承上启下的关键节点,在减少物流配送成本、提高服务响应效率上具有十分重要的战略价值。针对供应链环节中因多源多点造成的配送成本增加、库存周转率低等痛点,文中提出了一种改进的布谷鸟算法用于解决物流配送中心选址问题。该算法在传统的布谷鸟算法的基础上,引入粒子群优化算法,将两者有效地组合起来进行求解,并在求解过程中增加自适应参数,以避免出现局部最优现象,增加全局搜索能力。实验表明,相较于其他启发式算法,在解决配送中心选址问题上,改进布谷鸟算法能够有效降低物流配送成本,加快求解速度,提高库存周转次数,为企业物流的预算方案提供参考依据。

In recent years,the logistics industry has continued to develop and grow.As a key node in the logistics system,distribution centers have very important strategic value in reducing logistics distribution costs and improving service response efficiency.In view of the pain points in the supply chain such as increased distribution costs and low inventory turnover due to multiple sources and points,this paper proposes an improved cuckoo search algorithm to solve the location selection problem of logistics distribution centers.This algorithm introduces the particle swarm optimization algorithm on the basis of the traditional cuckoo search algorithm,effectively combines the two to solve the problem,and adds adaptive parameters during the solving process to avoid local optimal phenomena and increase global search capabilities.Experiments show that compared with other heuristic algorithms,the improved cuckoo search search algorithm can effectively reduce logistics distribution costs,speed up the solution speed,and increase inventory turnover when solving distribution center location problems,providing a reference for corporate logistics budget plans.