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 language||English (US)|
|Number of pages||17|
|Journal||Transactions in GIS|
|State||Published - Apr 2018|
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)