Open AccessMulti-objective Path Planning Based on K-D Fusion Algorithm
Author(s) -
Supeng Kong,
Surong Chen,
Zhaoxi Zhong,
Bin Xu,
Tianlei Shi,
Xijing Zhu,
Yong Yue,
Wei Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1828/1/012037
Subject(s) - dijkstra's algorithm , computer science , workload , path (computing) , task (project management) , cluster analysis , algorithm , selection (genetic algorithm) , shortest path problem , data mining , operations research , artificial intelligence , computer network , engineering , theoretical computer science , graph , systems engineering , operating system
How to arrange sales staff to visit offline stores reasonably is a critical task in the Fast Moving Consumer Goods (FMCG) industry. Based on the K-means and Dijkstra algorithms (K-D fusion algorithm), this paper proposed an algorithm to automatically allocate offline stores for sales staff and optimize the visiting path, thereby improving management efficiency. A new initial cluster center selection approach was proposed for the K-means algorithm to select its initial clustering center with the consideration of outlier points. The sales staff’s visiting path to the store was planned by the Dijkstra algorithm. The performance of our K-D fusion algorithm was evaluated in terms of grouping rationality and path planning optimization. Experimental results show that our algorithm can comprehensively consider several factors such as the number of stores, store types, and geographical locations, and distribute the workload more evenly to all the sales staff. In addition, it can also optimize the visiting path for sales staff, which can effectively improve the efficiency of sales staff to visit offline stores.