CAJ | 학술논문

对大规模多车场车辆路径问题，设计了基于双层模糊聚类的改进遗传算法求解框架，上层静态区域划分利用k-means技术将多车场到多客户的问题转化为一对多的子问题，下层模糊聚类从保证客户满意度和整合物流资源的角度出发，利用模糊聚类算法根据客户需求属性形成基于客户订单配送的动态客户群。进一步，通过改进选择算子和交叉算子来设计车辆路径优化的遗传算法。通过随机算例仿真实验，证明了提出方法和求解策略的有效性。
대대규모다차장차량로경문제，설계료기우쌍층모호취류적개진유전산법구해광가，상층정태구역화분이용k-means기술장다차장도다객호적문제전화위일대다적자문제，하층모호취류종보증객호만의도화정합물류자원적각도출발，이용모호취류산법근거객호수구속성형성기우객호정단배송적동태객호군。진일보，통과개진선택산자화교차산자래설계차량로경우화적유전산법。통과수궤산례방진실험，증명료제출방법화구해책략적유효성。
An improved genetic algorithm is proposed to solve the large-scale Multiple-Depot Vehicle Routing Problem (MDVRP), which is based on the presented two-stage fuzzy clustering algorithm. In the first stage, k-means is used to divide the MDVRP into several sub-problems. In terms of improving the customer satisfaction and integrating logistics resource, fuzzy clustering algorithm is applied to cluster customers into groups based on multi-attribute customer orders. Further-more, the improved GA is designed to solve the VRP by changing the selecting operator and the crossover operator. The stochastic simulation experiments show the proposed algorithm is efficient.