Abstract
Recent advances of e-commerce development require the timely delivery of goods. Amongst many challenges to deal with, a logistics company should effectively delineate a service area for vehicles or persons to deliver goods or services to the clients with the minimal overall travel costs while balancing their workloads. Each service area contains a certain number of clients to be serviced, and the problem to be solved here is basically a spatial clustering one. However, most existing clustering methods usually ignore the objective of balancing workloads among clusters. This paper introduces an approach attempting to partition a service area effectively. The objectives of the problem include generating spatially continuous and mutually exclusive clusters (subareas), minimizing the travel distance, and balancing the workloads among clusters. A series of experiments are conducted in order to evaluate the performance of the proposed approach. Based on the benchmarks it appears that the proposed approach performs better with respect to the above three objectives.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barreto, S., Ferreira, C., Paixao, J., Santos, B.S.: Using clustering analysis in a capacitated location-routing problem. Eur. J. Oper. Res. 179, 968–977 (2007)
Bosona, T.G., Gebresenbet, G.: Cluster building and logistics network integration of local food supply chain. Biosys. Eng. 108, 293–302 (2011)
Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, part I: routing construction and local search algorithms. Transp. Sci. 39, 104–118 (2005)
Cao, B., Glover, F.: Creating balanced and connected clusters for improved service delivery routes in logistics planning. J. Syst. Sci. Syst. Eng. 19, 453–480 (2010)
Cao, B., Glover, F., Rego, C.: A tabu search algorithm for cohesive clustering problems. J. Heuristics 21, 457–477 (2015)
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010)
Li, X., Claramunt, C., Kung, H.T., Guo, Z.Y., Wu, J.P.: A decentralized and continuity-based algorithm for delineating capacitated shelters’ service areas. Environ. Plan. B: Plan. Des. 35, 593–608 (2008)
Mesa-Arango, R., Ukkusuri, S.V.: Demand clustering in freight logistics networks. Transp. Sci. Part E 81, 36–51 (2015)
Özdamar, L., Demir, O.: A hierarchical clustering and routing procedure for large scale disaster relief logistics planning. Transp. Sci. Part E 48, 591–602 (2012)
She, B., Duque, J., Ye, X.: The Network-Max-P-Regions model. Int. J. Geogr. Inf. Sci. 31, 962–981 (2017)
Sheu, J.B.: An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transp. Sci. Part E 43, 687–709 (2007)
Xiong, Z., Chen, R.T., Zhang Y.F.: Effective method for cluster centers’ initialization in K-means clustering. Appl. Res. Comput. (2011)
Zhang, B., Yin, W.J., Xie, M., Dong, J.: Geo-spatial clustering with non-spatial attributes and geographic non-overlapping constraint: a penalized spatial distance measure. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 1072–1079. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71701-0_121
Acknowledgements
We are indebted to three anonymous reviewers for insightful observations and suggestions that have helped to improve our paper. The work is partially supported by the projects funded by National Natural Science Foundation of China (grant numbers: 41771410 and 41401173).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Chen, Q., Cao, B., Claramunt, C., Yi, H. (2019). An Iterative Two-Step Approach to Area Delineation. In: Kawai, Y., Storandt, S., Sumiya, K. (eds) Web and Wireless Geographical Information Systems. W2GIS 2019. Lecture Notes in Computer Science(), vol 11474. Springer, Cham. https://doi.org/10.1007/978-3-030-17246-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-17246-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17245-9
Online ISBN: 978-3-030-17246-6
eBook Packages: Computer ScienceComputer Science (R0)