Regionalization of primary health care units: An iterated greedy algorithm for large-scale instances

Abstract

In this paper, we study the problem of multi-institutional regionalization of primary health care units. The problem consists of deciding where to place new facilities, capacity expansions for existing facilities, and demand allocation in a multi-institutional system to minimize the total travel distance from demand points to health care units. It is known that traditional exact methods as branch-and-bound are limited to solving small- to medium-size instances of the problem. Given that real world-instances can be large, in this paper we propose an iterated greedy algorithm with variable neighborhood descent search for handling large-scale instances. Within this solution framework, several methods are developed. A greedy constructive method and two deconstruction strategies are developed. Another interesting component is the exact optimization of a demand allocation subproblem that is obtained when the location of facilities is previously fixed. An empirical assessment using real-world data from the State of Mexico’s Public Health Care System is carried out. The results demonstrate the effectiveness of the proposed metaheuristic in handling large-scale instances.