Multi-objective Path Planning Based on K-D Fusion Algorithm

Abstract

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.

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“…Similarly, Mansouri et al 30 applied K-means as the clustering method for a collaborative CPP focused on single complex 3D infrastructure. Furthermore, Kong et al 31 proposed a fusion of the K-means and Dijkstra algorithms to allocate offline stores for sale staff and optimize the visiting path. This is a traveling salesman problem (TSP) solved by using clustering and shortest path calculations.…”