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An Iterative Two-Step Approach to Area Delineation

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Web and Wireless Geographical Information Systems (W2GIS 2019)

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.

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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)

    Article  Google Scholar 

  • Bosona, T.G., Gebresenbet, G.: Cluster building and logistics network integration of local food supply chain. Biosys. Eng. 108, 293–302 (2011)

    Article  Google Scholar 

  • Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, part I: routing construction and local search algorithms. Transp. Sci. 39, 104–118 (2005)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Cao, B., Glover, F., Rego, C.: A tabu search algorithm for cohesive clustering problems. J. Heuristics 21, 457–477 (2015)

    Article  Google Scholar 

  • Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Mesa-Arango, R., Ukkusuri, S.V.: Demand clustering in freight logistics networks. Transp. Sci. Part E 81, 36–51 (2015)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • She, B., Duque, J., Ye, X.: The Network-Max-P-Regions model. Int. J. Geogr. Inf. Sci. 31, 962–981 (2017)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Xiong, Z., Chen, R.T., Zhang Y.F.: Effective method for cluster centers’ initialization in K-means clustering. Appl. Res. Comput. (2011)

    Google Scholar 

  • 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

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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).

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Correspondence to Buyang Cao .

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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

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  • DOI: https://doi.org/10.1007/978-3-030-17246-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17245-9

  • Online ISBN: 978-3-030-17246-6

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