CAJ | 학술논문

物流配送客户聚类问题是物流配送研究领域的基本问题，实际问题要求考虑客户分布的地理特性、客户配送量（即需求量）及配送车辆负载量等因素．针对配送中心未知的客户聚类问题，提出了一种考虑配送路网结构和配送量约束的聚类算法．利用所提出的“最短主干道距离”，克服传统欧式距离不考虑配送道路信息的缺陷，在此基础上，利用约束聚类思想对传统后中心划分聚类算法CLARANS进行改进，使其在考虑地理信息的同时，能满足客户配送量和车辆负载量约束．最后对提出的算法进行了数值实验．
물류배송객호취류문제시물류배송연구영역적기본문제，실제문제요구고필객호분포적지리특성、객호배송량（즉수구량）급배송차량부재량등인소．침대배송중심미지적객호취류문제，제출료일충고필배송로망결구화배송량약속적취류산법．이용소제출적“최단주간도거리”，극복전통구식거리불고필배송도로신식적결함，재차기출상，이용약속취류사상대전통후중심화분취류산법CLARANS진행개진，사기재고필지리신식적동시，능만족객호배송량화차량부재량약속．최후대제출적산법진행료수치실험．
Customer clustering is a fundamental issue in the logistics distribution study. Considering that retailers are bounded on certain geographic location and the clustering process is highly influenced by the distribution scale （that is the goods scale the customer demand） and loading scale of vehicles, we proposed a customer clustering method constrained by road network and distribution scale, for the clustering process with the location of distribution center unknown. The concept of ＂shortest arterial road distance （SARD）＂ takes distribution road information into account, which is ignored by the traditional Euclidean distance. Furthermore, a constrained clustering method for customers based on improved CLARANS was given, which consider the geographic information and fulfill the constraints of distribution scale. Finally, numeric experiments on both synthetic and real datasets justify the effectiveness of the method.