基于时空聚类求解带容积约束的选址-路径问题

选址-路径问题是供应链管理和物流系统规划中的一个重要问题,对总成本具有十分重要的影响.对考虑配送中心容积约束的带时间窗的选址-路径问题进行研究,建立以总成本最小和客户满意度最大为目标的多目标规划模型,提出两阶段算法对其进行求解.首先,利用k-means聚类算法确定配送中心选址;然后,提出一种基于时间-空间双因素的客户划分方法以确定配送中心所服务客户;最后,利用粒子群算法对各配送中心的配送路径进行规划.数值算例表明,所提出的算法较其他已有算法,均能有效地降低物流运作总成本及总配送路径长度,为解决带容积约束及时间窗的选址-路径问题提供了一种新的解决思路.

Time-space cluster based location-routing problem with capacitate constraints

Location-routing is an important problem in supply chain management and logistic systems. This paper studies the location-routing problem with time windows with the consideration of the distribution centers capacitate constraints. It establishes a multi-objective programming model to minimize the total cost and maximize the customer satisfaction, and proposes a two-stage algorithm to solve the proposed model. Firstly, the k-means clustering algorithm is used to determine the location of distribution centers, and then a time-space two-factor customer division method is proposed to determine the customers served by the chosen distribution center. Finally, the particle swarm algorithm is used to optimize the vehicle routings of each distribution center. Numerical examples illustrate that the proposed algorithm can effectively reduce the total cost of logistics operation and the total distribution routing length compared with other existing algorithms, providing a new solution to the location-routing problem with capacitate constraint and time windows.