Synthetic Optimization Problem Generation: Show Us the Correlations!

Published Online:https://doi.org/10.1287/ijoc.1090.0330

References

  • Amini M. M., Racer M. A rigorous computational comparison of alternative solution methods for the generalized assignment problem. Management Sci. (1994) 40(7):868–890LinkGoogle Scholar
  • Balas E., Martin C. H. Pivot and complement—A heuristic for 0–1 programming. Management Sci. (1980) 26(1):86–96LinkGoogle Scholar
  • Balas E., Zemel E. An algorithm for large zero-one knapsack problems. Oper. Res. (1980) 28(5):1130–1154LinkGoogle Scholar
  • Bertsimas D., Demir R. An approximate dynamic programming approach to multidimensional knapsack problems. Management Sci. (2002) 48(4):550–565LinkGoogle Scholar
  • Cario M., Nelson B. Modeling and generating random vectors with arbitrary marginal distributions and correlation matrix. (1997) . Technical report, Northwestern University, Evanston, ILGoogle Scholar
  • Cario M., Clifford J., Hill R., Yang J., Yang K., Reilly C. H., Uszoy R., Schmeiser B. Alternative methods for generating synthetic generalized assignment problems. Proc. 4th Indust. Engrg. Res. Conf. (1995) (Institute of Industrial Engineers, Norcross, GA) 1080–1089Google Scholar
  • Cario M. C., Clifford J. J., Hill R. R., Yang J., Yang K., Reilly C. H. An investigation of the relationship between problem characteristics and algorithm performance: A case study of the GAP. IIE Trans. (2002) 34(March):297–312CrossrefGoogle Scholar
  • Fisher M. L., Jaikumar R., Van Wassenhove L. N. A multiplier adjustment method for the generalized assignment problem. Management Sci. (1986) 32(9):1095–1103LinkGoogle Scholar
  • Fréville A., Plateau G. An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem. Discrete Appl. Math. (1994) 49(1–3):189–212CrossrefGoogle Scholar
  • Fréville A., Plateau G. The 0–1 bidimensional knapsack problem: Toward an efficient high-level primitive tool. J. Heuristics (1996) 2(2):147–167CrossrefGoogle Scholar
  • Guignard M., Rosenwein M. B. An improved dual based algorithm for the generalized assignment problem. Oper. Res. (1989) 37(4):658–663LinkGoogle Scholar
  • Hall N. G., Posner M. E. Generating experimental data for computational testing with machine scheduling applications. Oper. Res. (2001) 49(6):854–865LinkGoogle Scholar
  • Hill R. R., Reilly C. H. Multivariate composite distributions for coefficients in synthetic optimization problems. Eur. J. Oper. Res. (2000a) 121(1):64–77CrossrefGoogle Scholar
  • Hill R. R., Reilly C. H. The effects of coefficient correlation in two-dimensional knapsack problems on solution procedure performance. Management Sci. (2000b) 46(2):302–317LinkGoogle Scholar
  • Hooker J. N. Needed: An empirical science of algorithms. Oper. Res. (1994) 42(2):201–212LinkGoogle Scholar
  • Iman R. L., Conover W. J. A distribution-free approach to inducing rank correlation among input variables. Comm. Statist. Simulation Comput. (1982) 11(3):311–334CrossrefGoogle Scholar
  • John T. Tradeoff solutions in single machine production scheduling for minimizing flow time and maximum penalty. Comput. Oper. Res. (1989) 16(5):471–479CrossrefGoogle Scholar
  • Loulou R., Michaelides E. New greedy heuristics for the multi-dimensional 0–1 knapsack problem. Oper. Res. (1979) 27(6):1101–1114LinkGoogle Scholar
  • Martello S., Toth P., Mingozzi A., Sandi C. The 0–1 knapsack problem. Combinatorial Optimization (1979) (John Wiley & Sons, New York) 237–279Google Scholar
  • Martello S., Toth P., Brans J. An algorithm for the generalized assignment problem. Proc. 9th IFORS Conf. (1981) (North Holland, Amsterdam) 589–603Google Scholar
  • Martello S., Toth P. Algorithms for knapsack problems. Ann. Discrete Math. (1987) 31:213–258Google Scholar
  • Martello S., Toth P. A new algorithm for the 0–1 knapsack problem. Management Sci. (1988) 34(5):633–644LinkGoogle Scholar
  • Martello S., Toth P. Upper bounds and algorithms for hard 0–1 knapsack problems. Oper. Res. (1997) 45(5):768–778LinkGoogle Scholar
  • Martello S., Pisinger D., Toth P. Dynamic programming and strong bounds for the 0–1 knapsack problem. Management Sci. (1999) 45(3):414–424LinkGoogle Scholar
  • Martello S., Pisinger D., Toth P. New trends in exact algorithms for the 0–1 knapsack problem. Eur. J. Oper. Res. (2000) 123(2):325–332CrossrefGoogle Scholar
  • Moore B. A., Peterson J. A., Reilly C. H., Balci O., Sadowski R. P., Nance R. E. Characterizing distributions of discrete bivariate random variables for simulation and evaluation of solution methods. Proc. 22nd Conf. Winter Simulation (1990) New Orleans(IEEE Computer Society, Washington, DC) 294–302CrossrefGoogle Scholar
  • Pisinger D. A minimal algorithm for the 0–1 knapsack problem. Oper. Res. (1997) 45(5):758–767LinkGoogle Scholar
  • Potts C. N., Van Wassenhove L. N. Algorithms for scheduling a single machine to minimize the weighted number of late jobs. Management Sci. (1988) 34(7):843–858LinkGoogle Scholar
  • Reilly C. H., Nelson B. L., Kelton W. D., Clark G. M. Optimization problems with uniformly distributed coefficients. Proc. 23rd Conf. Winter Simulation (1991) Phoenix(IEEE Computer Society, Washington, DC) 866–874CrossrefGoogle Scholar
  • Reilly C. H., Evans G. W., Mollaghasemi M., Russell E. C., Biles W. E. A comparison of alternative input models for synthetic optimization problems. Proc. 25th Conf. Winter Simulation (1993) Los Angeles(IEEE Computer Society, Washington, DC) 356–364CrossrefGoogle Scholar
  • Reilly C. H. Generating coefficients for optimization test problems with implicit correlation induction. Proc. 1997 IEEE-SMC Conf. (1997) Orlando, FL(IEEE, Los Alamitos, CA) 2438–2443CrossrefGoogle Scholar
  • Reilly C. H., Farrington P. A., Nembhard H. B., Sturrock D. T., Evans G. W. Input models for synthetic optimization problems. Proc. 31st Conf. Winter Simulation: Simulation—A Bridge to the Future (1999) 1(ACM, New York) 116–121CrossrefGoogle Scholar
  • Rushmeier R. A., Nemhauser G. L. Experiments with parallel branch-and-bound algorithms for the set covering problem. Oper. Res. Lett. (1993) 13(5):277–285CrossrefGoogle Scholar
  • Trick M. A. A linear relaxation heuristic for the generalized assignment problem. Naval Res. Logist. (1992) 39(2):137–151CrossrefGoogle Scholar
  • Yang J. A computational study on 0–1 knapsack problems generated under explicit correlation induction. (1994) . Unpublished master's dissertation, Ohio State University, ColumbusGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.