A spatial-knowledge-enhanced heuristic for solving the p-median problem

Wangshu Mu, Daoqin Tong

Research output: Contribution to journalArticle

Abstract

The p-median problem (PMP) is one of the most applied location problems in urban and regional planning. As an NP-hard problem, the PMP remains challenging to solve optimally, especially for large-sized problems. A number of heuristics have been developed to obtain PMP solutions in a fast manner. Among the heuristics, the Teitz and Bart (TB) algorithm has been found effective for finding high-quality solutions. In this article, we present a spatial-knowledge-enhanced Teitz and Bart (STB) heuristic method for solving PMPs. The STB heuristic prioritizes candidate facility sites to be examined in the solution set based on the spatial distribution of demand and service provision. Tests based on a range of PMPs demonstrate the effectiveness of the STB heuristic. This new algorithm can be incorporated into current commercial GIS packages to solve a wide range of location-allocation problems.

Original languageEnglish (US)
Pages (from-to)477-493
Number of pages17
JournalTransactions in GIS
Volume22
Issue number2
DOIs
StatePublished - Apr 1 2018

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p-median
heuristics
service provision
regional planning
urban planning
GIS
spatial distribution

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Cite this

A spatial-knowledge-enhanced heuristic for solving the p-median problem. / Mu, Wangshu; Tong, Daoqin.

In: Transactions in GIS, Vol. 22, No. 2, 01.04.2018, p. 477-493.

Research output: Contribution to journalArticle

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