A gamma heuristic for the p-median problem

Heuristic concentration (HC) is a two-stage metaheuristic that can be applied to a wide variety of combinatorial problems. It is particularly suited to location problems in which the number of facilities is given in advance. In such settings, the first stage of HC repeatedly applies some random-start interchange (or other) heuristic to produce a number of alternative facility configurations. A subset of the best of these alternatives is collected and the union of the facility sites in them is called a concentration set (CS). Among the component elements of the CS are likely to be included those sites which are members of the optimal solution. In earlier studies the second stage of HC has consisted of an exact procedure to extract the best possible solution from the CS. In this paper we demonstrate, for the p-median problem, the use of two sequentially active heuristics in the second stage of HC. That is, we offer two additional layers of heuristics to improve solutions which are found in the first stage of HC. Thus we are describing a variant of the HC metaheuristic consisting of three layers of heuristics which are utilized in sequence. We propose for this procedure the name of Gamma Heuristic.

[1]  G. Nemhauser,et al.  Exceptional Paper—Location of Bank Accounts to Optimize Float: An Analytic Study of Exact and Approximate Algorithms , 1977 .

[2]  Roberto D. Galvão,et al.  A Dual-Bounded Algorithm for the p-Median Problem , 1980, Oper. Res..

[3]  J. Current,et al.  Heuristic concentration and Tabu search: A head to head comparison , 1998 .

[4]  R. Swain A Parametric Decomposition Approach for the Solution of Uncapacitated Location Problems , 1974 .

[5]  Polly Bart,et al.  Heuristic Methods for Estimating the Generalized Vertex Median of a Weighted Graph , 1968, Oper. Res..

[6]  J. Current,et al.  An efficient tabu search procedure for the p-Median Problem , 1997 .

[7]  Manfred Horn Analysis and Computational Schemes for p-Median Heuristics , 1996 .

[8]  Charles ReVelle,et al.  Surviving in a Competitive Spatial Market: The Threshold Capture Model , 1999 .

[9]  S. Hakimi Optimum Distribution of Switching Centers in a Communication Network and Some Related Graph Theoretic Problems , 1965 .

[10]  Gerard Rushton,et al.  Strategies for Solving Large Location-Allocation Problems by Heuristic Methods , 1992 .

[11]  F. E. Maranzana,et al.  On the Location of Supply Points to Minimize Transport Costs , 1964 .

[12]  C. Revelle,et al.  Heuristic concentration: Two stage solution construction , 1997 .

[13]  M. Rao,et al.  An Algorithm for the M-Median Plant Location Problem , 1974 .

[14]  Paul J. Densham,et al.  A more efficient heuristic for solving largep-median problems , 1992 .

[15]  K. Rosing Heuristic Concentration: A Study of Stage One , 2000 .

[16]  S. L. Hakimi,et al.  Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph , 1964 .

[17]  Kenneth E. Rosing,et al.  An Empirical Investigation of the Effectiveness of a Vertex Substitution Heuristic , 1997 .

[18]  R. A. Whitaker,et al.  A Fast Algorithm For The Greedy Interchange For Large-Scale Clustering And Median Location Problems , 1983 .

[19]  Subhash C. Narula,et al.  Technical Note - An Algorithm for the p-Median Problem , 1977, Oper. Res..

[20]  Paul J. Densham,et al.  A more e cient heuristic for solving large p-median problems , 1992 .

[21]  E. Hillsman The p-Median Structure as a Unified Linear Model for Location—Allocation Analysis , 1984 .