Worst-case analysis of demand point aggregation for the Euclidean p-median problem

Solving large-scale p-median problems is usually time consuming. People often aggregate the demand points in a large-scale p-median problem to reduce its problem size and make it easier to solve. Most traditional research on demand point aggregation is either experimental or assuming uniformly distributed demand points in analytical studies. In this paper we study demand point aggregation for planar p-median problem when demand points are arbitrarily distributed. Efficient demand aggregation approaches are proposed with the corresponding attainable worst-case aggregation error bounds measured. We demonstrate that these demand aggregation approaches introduce smaller worst-case aggregation error bounds than that of the honeycomb heuristic (Papadimitriou, 1981) when demand points are arbitrarily distributed. We also conduct numerical experiments to show their effectiveness. Key Words: Aggregation; Error Bounds

• Type: Journal
• Year: 2010
• Where: EOR
• Authors: Lian Qi, Zuo-Jun Max Shen
• Keywords: Demand Point Aggregation, EOR 2010, Large-scale P-median Problem, P-median Problem