Using spatial autocorrelation to help solve the p-median problem

Authors: Hyun Kim*, Department of Geography, University of Tennessee, TN, Daniel A Griffith, School of Economic, Political and Policy Sciences, The University of Texas at Dallas, TX, Yongwan Chun, School of Economic, Political and Policy Sciences, The University of Texas at Dallas, TX
Topics: Spatial Analysis & Modeling
Keywords: Spatial Autocorrelation; spatial optimization; p-median problem; location-allocation model
Session Type: Paper
Day: 4/4/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Marshall North, Marriott, Mezzanine Level
Presentation File: No File Uploaded

This paper presents an efficient solution approach using characteristics of spatial autocorrelation to solve the p-median problem (PMP), one of the classical location-allocation problems. Location-allocation problems involve determining the locations of facilities at the same time as the allocations of non-facilities in a discrete space. The PMP has been known as NP-hard, thus obtaining optimal solutions for large instances is challenging due to a rapid increase in complexity as the number of potential locations increases. This paper proposes a model that incorporates spatial autocorrelation patterns of non-facilities to increase efficiency in obtaining the PMP solution, which is called the PMP-SA (Spatial Autocorrelation). The proposed model finds an optimal solution for large instances efficiently. The performance of the PMP-SA technique is compared with popular exact and heuristic methods using standard datasets that have appeared in the literature to drive strategic treatment options to find exact solutions for the PMP.

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