Capacity-Constrained Network-Voronoi Diagram

Kwangsoo Yang, Apurv Hirsh Shekhar, Dev Oliver, Shashi Shekhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Given a graph and a set of service center nodes, a Capacity Constrained Network-Voronoi Diagram (CCNVD) partitions the graph into a set of contiguous service areas that meet service center capacities and minimize the sum of the shortest distances from graph-nodes to allotted service centers. The CCNVD problem is important for critical societal applications such as assigning evacuees to shelters and assigning patients to hospitals. This problem is NPO-hard; it is computationally challenging because of the large size of the transportation network and the constraint that service areas must be contiguous in the graph to simplify communication of allotments. Previous work has focused on honoring either service area contiguity (e.g., Network Voronoi Diagrams) or service center capacity constraints (e.g., min-cost flow), but not both. We introduced a novel Pressure Equalizer (PE) approach for CCNVD to meet the capacity constraints of service centers while maintaining the contiguity of service areas. However, we find that the main bottleneck of the PE algorithm is testing whether service areas are contiguous. We propose novel algorithms that reduce the computational cost. Experiments using road maps from five different regions demonstrate that the proposed approaches significantly reduce computational cost for the PE approach.

Original languageEnglish (US)
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages2
ISBN (Electronic)9781509020195
StatePublished - Jun 22 2016
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: May 16 2016May 20 2016

Publication series

Name2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016


Other32nd IEEE International Conference on Data Engineering, ICDE 2016

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