The demand partitioning method for reducing aggregation errors in p-median problems

The demand partitioning method for reducing aggregation errors in the solution of aggregated p-median problems is introduced in this paper. The method consists of eliminating source A and B aggregation errors using the Current and Schilling weighting method and then partitioning the basic spatial units to eliminate source C errors. These two steps are repeated until all cost estimate error is eliminated in the solution of the problem. Data from the Central Valley of Costa Rica are used to test this demand partitioning method. Specifically, population census data are used to represent demand for services while current health clinics locations are the potential service supply points. The demand partitioning method outperforms current published methods for reducing source C errors.