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Designing and reporting on computational experiments with heuristic methods

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Abstract

This article discusses the design of computational experiments to test heuristic methods and provides reporting guidelines for such experimentation. The goal is to promote thoughtful, well-planned, and extensive testing of heuristics, full disclosure of experimental conditions, and integrity in and reproducibility of the reported results.

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References

  • Aarts, E., van Laarhoven, P., Lenstra, J., and Ulder, N. (1994). A computational study of local search algorithms for job shop scheduling.ORSA Journal on Computing, 6(2), 118–125.

    MATH  Google Scholar 

  • Ahuja, R., Magnanti, T., and Orlin, J. (1993).Network Flows: Theory, Algorithms, and Applications, Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Amini, M., and Barr, R. (1990). Network reoptimization algorithms: A statistically designed comparison.ORSA Journal on Computing, 5(4), 395–409.

    Google Scholar 

  • Arthur, J., and Frendewey, J. (1988). Generating traveling salemen problems with known optimal tours.Journal of the Operational Research Society, 39(2), 153–159.

    Article  MATH  Google Scholar 

  • Arthur, J., and Frendewey, J. (1994). An algorithm for generating minimum cost network flow problems with specific structure and known optimal solutions.Networks, 24(8), 445–454.

    MathSciNet  MATH  Google Scholar 

  • Barr, R., and Hickman, B. (1993). Reporting computational experiments with parallel algorithms: Issues, measures, and experts' opinions.ORSA Journal on Computing, 5(1), 2–18.

    MATH  Google Scholar 

  • Barr, R., and Hickman, B. (1994). Parallelization strategies for the network simplex algorithm.Operations Research, 42(1), 65–80.

    MATH  Google Scholar 

  • Barton, R., and Ivey, Jr., J. (1996). Nelder-mead simplex modifications for simulation optimization. Tech. rep., Department of Industrial and Systems Engineering, Pennsylvania State University, University Park, PA. To appear inManagement Science.

    Google Scholar 

  • Battiti, R., and Tecchiolli, G. (1994). Simulated annealing and tabu search in the long run: A comparison on gap tasks.Computers and Mathematics with Applications, 28(6), 1–8.

    Article  MATH  Google Scholar 

  • Bland, R., Cheriyan, J., Jensen, D., and Ladányi, L. (1993). An empirical study of min cost flow algorithms. In D. Johnson, and C. McGeoch (Eds.),Network Flows and Matching: First DIMACS Implementation Challenge, Vol. 12 ofDIMACS Series in Discrete Mathematics and Theoretical Computer Science (pp. 119–156): Providence, RI: American Mathematical Society.

    Google Scholar 

  • Box, G., and Draper, N. (1969).Evolutionary Operation, A Statistical Method for Process Improvement. New York: John Wiley.

    Google Scholar 

  • Bratley, P., Fox, B., and Schrage, L. (1983).A Guide to Simulation. New York: Springer-Verlag

    MATH  Google Scholar 

  • Cornuejols, G., Sridharan, R., and Thizy, J. (1991). A comparison of heuristics and relaxations for the capacititated plant location problem.European Journal of Operational Research, 50, 280–297.

    Article  MATH  Google Scholar 

  • Crowder, H., Dembo, R., and Mulvey, J. (1980). On reporting computational experiments with mathematical software.ACM Transactions on Mathematical Software, 5, 193–203.

    Article  Google Scholar 

  • Dyer, M., and Frieze, A. (1985). A simple heuristic for thep-centre problem.Operations Research Letters, 3(6), 285–288.

    Article  MathSciNet  MATH  Google Scholar 

  • Feo, T., and Resende, M. (1995). Greedy randomized adaptive search procedures.Journal of Global Optimization, 6, 109–133.

    Article  MathSciNet  MATH  Google Scholar 

  • Fisher, M. (1980). Worst-case analysis of heuristic algorithms.Management Science, 26(1), 1–17.

    MATH  MathSciNet  Google Scholar 

  • Floudas, C., and Pardalos, P. (1990).Collection of Test Problems for Constrained Global Optimization Algorithms, Vol. 455 ofLecture Notes in Computer Science. Springer-Verlag.

  • Garey, M., and Johnson, D. (1979).Computers and Intractability: A Guide to the Theory of NP-Completeness. San Francisco: Freeman.

    MATH  Google Scholar 

  • Gendreau, M., Hertz, A., and Laporte, G. (1994). A tabu search heuristic for the vehicle routing problem.Management Science, 40(10), 1276–1290.

    MATH  Google Scholar 

  • Gilsinn, J., Hoffman, K., Jackson, R., Leyendecker, E., Saunders, P., and Shier, D. (1977). Methodology and analysis for comparing discrete linearl 1 approximation codes.Communications in Statistics, 136, 399–413.

    Google Scholar 

  • Glover, F. (1989). Tabu search-part I.ORSA Journal on Computing, 1(3), 190–206.

    MATH  Google Scholar 

  • Glover, F., Karney, D., Klingman, D., and Napier, A. (1974). A computational study on start procedures, basis change criteria, and solution algorithms for transportation problems.Management Science, 20, 793–813.

    MathSciNet  MATH  Google Scholar 

  • Golden, B., Assad, A., Wasil, E., and Baker, E. (1986). Experimentation in optimization.European Journal of Operational Research, 27, 1–16.

    MathSciNet  MATH  Google Scholar 

  • Golden, B., and Stewart, W. (1985). Empirical analysis of heuristics. In E. Lawler, J. Lenstra A. Rinnooy Kan, and D. Shmoys (Eds.),The Travelling Salesman Problem, a Guided Tour of Combinatorial Optimization (pp. 207–249). Chichester (U.K.): Wiley.

    Google Scholar 

  • Greenberg, H. (1990). Computational testing: Why, how and how much?ORSA Journal on Computing, 2, 7–11.

    Google Scholar 

  • Held, M., and Karp, R. (1970). The travelling-salesman problem and minimum spanning trees.Operations Research, 18, 1138–1162.

    MathSciNet  MATH  Google Scholar 

  • Held, M., and Karp, R. (1971). The travelling-salesman problem and minimum spanning trees: Part ii.Mathematical Programming, 1, 6–25.

    Article  MathSciNet  MATH  Google Scholar 

  • Hochbaum, D., and Shmoys, D. (1985). A best possible heuristic for thek-center problem.Mathematics of Operations Research, 10(2), 180–184.

    MathSciNet  MATH  Google Scholar 

  • Holland, J. (1975),Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.

    Google Scholar 

  • Hooker, J. (1994). Needed: An empirical science of algorithms.Operations Research, 42(2), 201–212.

    MATH  Google Scholar 

  • Hooker, J. (1995). Testing heuristics: We have it all wrong.Journal of Heuristics, 1(1), 33–42.

    MATH  MathSciNet  Google Scholar 

  • Hopfield, J., and Tank, D. (1985). Neural computation of decisions in optimization problems.Biological Cybernetics, 52, 141.

    MathSciNet  MATH  Google Scholar 

  • Jackson, R., Boggs, P., Nash, S., and Powell, S. (1990). Report of the ad hoc committee to revise the guidelines for reporting computational experiments in mathematical programming.Mathematical Programing, 49, 413–425.

    Article  MathSciNet  Google Scholar 

  • Jackson, R., and Mulvey, J. (1978). A critical review of comparisons of mathematical programming algorithms and software (1953–1977).J. Research of the National Bureau of Standards, 83, 563–584.

    MathSciNet  MATH  Google Scholar 

  • Johnson, D. (1990). Local optimization and the traveling salesman problem. InProceedings of the 17th Colloquium on Automata, Languages and programming, Lecture Notes in Computer Science 443 (pp. 446–461). Berlin: Springer-Verlag.

    Google Scholar 

  • Johnson, D., Bentley, J., McGeoch, L., and Rothberg, E. (1995). Near-optimal solutions to very lage traveling salesman problems. Tech. rep., monograph, in preparation.

  • Johnson, D., and Papadimitrious, C. (1985). Performance guarantees for heuristics. In E. Lawler, J. Lenstra, A. Rinnooy Kan, and D. Shmoys (Eds.),The Travelling Salesman Problem, A Guided Tour of Combinatorial Optimization (pp. 145–180). Chichester (U.K.): Wiley.

    Google Scholar 

  • Kelly, J., Golden, B., and Assad, A. (1992). Cell suppression: Disclosure protection for sensitive tabular data.Networks, 22(4), 397–417.

    MATH  Google Scholar 

  • Kelly, J., Golden, B., and Assad, A. (1993). Large-scale controlled rounding using tabu search and strategic oscillation.Annals of Operations Research, 41, 69–84.

    Article  MATH  Google Scholar 

  • Klingman, D., and Mote, J. (1987). Computational analysis of large-scale pure networks. Presented at the Joint National Meeting of ORSA/TIMS, New Orleans.

  • Klingman, D., Napier, A., and Stutz, J. (1974). Netgen: A program for generating large scale capacitated assignment, transportation, and minimum cost flow network problems.Management Science, 20, 814–821.

    MathSciNet  MATH  Google Scholar 

  • Knox, J. (1994). Tabu search performance on the symmetric travelling salesman problem.Computers & Operations Research, 21(8), 867–876.

    Article  MATH  Google Scholar 

  • Lawler, E., Lenstra, J., Kan, A. Rinnooy, and Shmoys, D. (1985).The Travelling Salesman Problem, A Guided Tour of Combinatorial Optimization. Chiehester (U.K.): Wiley.

    Google Scholar 

  • Lin, B., and Rardin, R. (1977). Development of a parametric generating procedure for integer programming test problems.Journal of the ACM, 24, 465–472.

    Article  MathSciNet  MATH  Google Scholar 

  • Lin, B., and Rardin, R. (1979). Controlled experimental design for statistical comparison of integer programming algorithms.Management Science, 25(12), 33–43.

    Article  MathSciNet  Google Scholar 

  • Lin, S., and Kernighan, B. (1973). An effective heuristic algorithm for the traveling-salesman problem.Operations Research, 21(2), 498–516.

    MathSciNet  MATH  Google Scholar 

  • Martello, S., and Toth, P. (1990).Knapsack Problems. Chichester (U.K.): Wiley.

    MATH  Google Scholar 

  • Mason, R., Gunst, R., and Hess, J. (1989).Statistical Design and Analysis of Experiments. New York: Wiley.

    Google Scholar 

  • McGeoch, C. (1986).Experimental Analysis of Algorithms. Ph.D. thesis, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA.

    Google Scholar 

  • McGeoch, C. (1995). Toward an experimental method for algorithm simulation.INFORMS Journal on Computing, to appear.

  • McGeoch, C. (1992). Analyzing algorithms by simulation: Variance reduction techniques and simulation speedups.ACM Computing Surveys, 24(5), 195–212.

    Article  Google Scholar 

  • Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., and Teller, E. (1953). Equation of state calculation by fast computing machines.Journal of Chemical Physics, 21, 1087–1091.

    Article  Google Scholar 

  • Montgomery, D. (1984).Design and Analysis of Experiments. New York: Wiley.

    Google Scholar 

  • Mulvey, J. (1982).Evaluating Mathematical Programming Techniques. Berlin: Springer-Verlag.

    MATH  Google Scholar 

  • Nance, R., Moose, Jr., R., and Foutz, R. (1987). A statistical technique for comparing heuristics: An example from capacity assignment strategies in computer network design.Communications of the ACM, 30(5), 430–442.

    Article  Google Scholar 

  • Nelder, J., and Mead, R. (1965). A simplex method for function minimization.Computer Journal, 7, 308–313.

    MATH  Google Scholar 

  • Nygard, K., Juell, P., and Kadaba, N. (1990). Neural networks for selecting vehicle routing heuristics.ORSA Journal on Computing, 2(4), 353–364.

    MATH  Google Scholar 

  • O'Neill, R. (1982). A comparison of real-world linear programs and their randomly generated analogs. In J. Mulvey (Ed.),Evaluating Mathematical Programming Techniques (pp. 44–59). Berlin: Springer-Verlag.

    Google Scholar 

  • Rardin, R., and Lin, B. (1982). Test problems for computational experiments—issues and techniques. In J. Mulvey (Ed.),Evaluating Mathematical Programming Techniques (pp. 8–15). Berlin: Springer-Verlag.

    Google Scholar 

  • Reeves, C. (1993a). Evaluation of heuristic performance. In C. Reeves, (Ed.),Modern Heuristic Techniques for Combinatorial Problems. New York: Wiley.

    Google Scholar 

  • Reeves, C. (1993b).Evaluation of Heuristic Performance. New York: Wiley.

    Google Scholar 

  • Reinelt, G. (1991). TSPLIB—a travelling salesman problem library.ORSA Journal on Computing, 3(4), 376–384.

    MATH  Google Scholar 

  • Resende, M., and Ribeiro, C. (1995). A GRASP for graph planarization. Tech. rep., AT&T Bell Laboratories, Murray Hill, NJ.

    Google Scholar 

  • Rothfarb, B., Frank, H., Rosebaum, D., Steiglitz, K., and Kleitman, D. (1970). Optimal design of offshore natural gas pipeline systems.Operations Research, 18, 992–1020.

    Google Scholar 

  • Stewart, W. (1987). An accelerated branch exchange heuristic for the traveling salesman problem.Networks, 17, 423–437.

    MATH  MathSciNet  Google Scholar 

  • Stewart, W., Kelly, J., and Laguna, M. (1995). Solving vehicle routing problems using generalized assignments and tabu search. Tech. rep., Graduate School of Business, College of William and Mary, Williamsburg, VA.

    Google Scholar 

  • Taguchi, G., and Wu, Y.-I. (1979).Introduction to Off-Live Quality Control. Central Japan Quality Control Association, Meieki Nakamura-ku Magaya, Japan.

    Google Scholar 

  • Tufte, E. (1983).The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press.

    Google Scholar 

  • Zanakis, S., Evans, J., and Vazacopoulos, A. (1989). Heuristic methods and applications: A categorized survey.European Journal of Operational Research, 43, 88–110.

    Article  MathSciNet  MATH  Google Scholar 

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Barr, R.S., Golden, B.L., Kelly, J.P. et al. Designing and reporting on computational experiments with heuristic methods. J Heuristics 1, 9–32 (1995). https://doi.org/10.1007/BF02430363

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