Name
Abstract | ||
|---|---|---|
For many express companies, express shops are the first line of serving customers. Reasonable location of express shops is very important to improve customer satisfaction. To balance the operation cost for the express company and the convenience of customers, we need to shorten the distance of customers to their closest shop while maintaining an appropriate express amount at each shop. In order to optimize the location of express shops, a heuristic clustering algorithm considering the constraints of service scope and service capability is proposed. The validity of the model is validated by DB Shanghai regional data, and the constrained clustering algorithm is compared with immune genetic algorithm and K-means method. The results show that, within the ideal service capacity and distance constraints, the proposed clustering algorithm can cover 31%-35% more demand than immune genetic algorithm, 1.4%-13% higher than K-means method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IEEM45057.2020.9309889 | 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) |
Keywords | DocType | ISBN |
express shop location,service scope,service capability,clustering algorithm | Conference | 978-1-5386-7221-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianchao Zhang | 1 | 313 | 39.57 |
| Xiao LIU | 2 | 59 | 8.40 |
| J. Guo | 3 | 255 | 31.34 |