The m-Center Problem: Minimax Facility Location

The m-Center Problem is to locate a given number of (emergency) facilities anywhere along a road network so as to minimize the maximum distance between these facilities and fixed demand locations assigned to them. Fundamental properties of the m-Center Problem are examined. The problem is modeled using integer programming, and is successfully attacked using a binary search technique and a combination of exact tests and heuristics. Computational results are given.

A Multi-Exchange Heuristic for the Single Source Capacitated Facility Location Problem

We present a very large-scale neighborhood (VLSN) search algorithm for the capacitated facility location problem with single-source constraints. The neighborhood structures are induced by customer multiexchanges and by facility moves. We consider both traditional single-customer multi-exchanges, detected on a suitably defined customer improvement graph, and more innovative multicustomer multi-exchanges, detected on a facility improvement graph dynamically built through the use of a greedy scheme. Computational results for some benchmark instances are reported that demonstrate the effectiveness of the approach for solving large-scale problems. A further test on real data involving an Italian factory is also presented

The p-median problem and health facilities: Cost saving and improvement in healthcare delivery through facility location

The importance of health to economic growth and development is an undisputed fact. Modern advancement in technology and healthcare has contributed to improved health and productivity, but there are many people who cannot access healthcare in a timely fashion. Factors affecting delays in accessing healthcare include inadequate supply, poor location, or lack of healthcare facilities all of which can be exacerbated by increasing healthcare costs and scarcity of resources. In this study, we develop a simple two-stage method based on the p-median problem to investigate the location and access to healthcare (emergency) facilities in urban areas. We compare the results of our new method with the results of similar existing methods using 26-node, 42-node, and 55-node data. We also show the efficiency of our method with exact methods using 150-node random data. Our method compares favorably with optimal and the existing methods