An Ejection Chain Approach for the Generalized Assignment Problem

Published Online:https://doi.org/10.1287/ijoc.1030.0036

References

  • Amini M. M., Racer M. A hybrid heuristic for the generalized assignment problem. Eur. J. Oper. Res. (1995) 87:343–348CrossrefGoogle Scholar
  • Berge C.The Theory of Graphs and Its Applications (1962) . Methuen, London, and Wiley, New York. (Translated by A. Doig.)Google Scholar
  • Bertsekas D. P.Nonlinear Programming (1995) . Athena Scientific, Belmont, MAGoogle Scholar
  • Cattrysse D. G., Salomon M., Van Wassenhove L. N. A set partitioning heuristic for the generalized assignment problem. Eur. J. Oper. Res. (1994) 72:167–174CrossrefGoogle Scholar
  • Chu P. C., Beasley J. E. A genetic algorithm for the generalized assignment problem. Comput. Oper. Res. (1997) 24:17–23CrossrefGoogle Scholar
  • Dongarra J. J. Performance of various computers using standard linear equations software. (1999) . Technical report. Computer Science Department, University of Tennessee, Knoxville, TN 37996-1301, and Mathematical Sciences Section, Oak Ridge National Laboratory, Oak Ridge, TN 37831, http://www.netlib.org/benchmark/performance.psGoogle Scholar
  • Edmonds J. Maximum matching and a polyhedron with 0, 1-vertices. J. Res. National Bureau of Standards (1965) 69B:125–130CrossrefGoogle Scholar
  • Fisher M. L. The Lagrangian relaxation method for solving integer programming problems. Management Sci. (1981) 27:1–18LinkGoogle Scholar
  • Garey M. R., Johnson D. S.Computers and Intractability: A Guide to the Theory of NP-Completeness (1979) (W. H. Freeman and Company, New York) Google Scholar
  • Glover F. Tabu search–A tutorial. Interfaces (1990) 20(1):74–94LinkGoogle Scholar
  • Glover F. Ejection chain moves for generalized assignment problems. (1997) . Manuscript. Leeds School of Business, University of Colorado, Boulder, COGoogle Scholar
  • Held M., Karp R. M. The traveling salesman problem and minimum spanning trees: Part II. Math. Programming (1971) 1:6–25CrossrefGoogle Scholar
  • Johnson D. S., Paterson M. S. Local optimization and the traveling salesman problem. Automata, Languages and Programming, Lecture Notes in Computer Science (1990) (Springer-Verlag, Berlin) 446–461No.443CrossrefGoogle Scholar
  • Kernighan B. W., Lin S. An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. (1970) 49:291–307CrossrefGoogle Scholar
  • Laguna M., Kelly J. P., González-Velarde J. L., Glover F. Tabu search for the multilevel generalized assignment problem. Eur. J. Oper. Res. (1995) 82:176–189CrossrefGoogle Scholar
  • Lin S., Kernighan B. W. An effective heuristic algorithm for the traveling salesman problem. Oper. Res. (1973) 21:498–516LinkGoogle Scholar
  • Lorena L. A. N., Narciso M. G. Relaxation heuristics for a generalized assignment problem. Eur. J. Oper. Res. (1996) 91:600–610CrossrefGoogle Scholar
  • Lourenço H. R., Serra D. Adaptive search heuristics for the generalized assignment problem. Mathware Soft Comput. (2002) 9:209–234Google Scholar
  • Lourenço H. R., Zwijnenburg M., Osman I. H., Kelly J. P. Combining the large-step optimization with tabu-search: Application to the job-shop scheduling problem. Meta-Heuristics: Theory & Applications (1996) (Kluwer Academic Publishers, Boston, MA) 661–675CrossrefGoogle Scholar
  • Martello S., Toth P. An algorithm for the generalized assignment problem. Proc. Ninth IFORS Internat. Conf. on Operational Res. (1981) Hamburg, Germany, July 1981. North-Holland, Amsterdam, The Netherlands:589–603Google Scholar
  • Martello S., Toth P.Knapsack Problems: Algorithms and Computer Implementations (1990) (John Wiley & Sons, Chichester, U.K.) Google Scholar
  • Martin O., Otto S. W., Felten E. W. Large-step Markov chains for the traveling salesman problem. Complex Systems (1991) 5:299–326Google Scholar
  • Martin O., Otto S. W., Felten E. W. Large-step Markov chains for the TSP incorporating local search heuristic. Oper. Res. Lett. (1992) 11:219–224CrossrefGoogle Scholar
  • Nauss R. M. Solving the generalized assignment problem: An optimizing and heuristic approach. INFORMS J. Comput. (2003) 15(3):249–266LinkGoogle Scholar
  • Nonobe K., Ibaraki T. A tabu search approach to the CSP (constraint satisfaction problem) as a general problem solver. Eur. J. Oper. Res. (1998) 106:599–623CrossrefGoogle Scholar
  • Osman I. H. Heuristics for the generalized assignment problem: Simulated annealing and tabu search approaches. OR Spektrum (1995) 17:211–225CrossrefGoogle Scholar
  • Pesch E., Glover F. TSP ejection chains. Discrete Appl. Math. (1997) 76:165–181CrossrefGoogle Scholar
  • Racer M., Amini M. M. A robust heuristic for the generalized assignment problem. Ann. Oper. Res. (1994) 50:487–503CrossrefGoogle Scholar
  • Rego C. Relaxed tours and path ejections for the traveling salesman problem. Eur. J. Oper. Res. (1998a) 106:522–538CrossrefGoogle Scholar
  • Rego C. A subpath ejection chain method for the vehicle routing problem. Management Sci. (1998b) 44:1447–1459LinkGoogle Scholar
  • Rego C., Roucairol C., Osman I. H., Kelly J. P. A parallel tabu search algorithm using ejection chains for the vehicle routing problem. Meta-Heuristics: Theory & Applications (1996) (Kluwer Academic Publishers, Boston, MA) 661–675CrossrefGoogle Scholar
  • Sahni S., Gonzalez T. P-complete approximation problems. J. Association Comput. Machinery (1976) 23:555–565CrossrefGoogle Scholar
  • Savelsbergh M. A branch-and-price algorithm for the generalized assignment problem. Oper. Res. (1997) 45:831–841LinkGoogle Scholar
  • Trick M. A. A linear relaxation heuristic for the generalized assignment problem. Naval Res. Logist. (1992) 39:137–151CrossrefGoogle Scholar
  • Yagiura M., Yamaguchi T., Ibaraki T. A variable depth search algorithm with branching search for the generalized assignment problem. Optim. Methods Software (1998) 10:419–441CrossrefGoogle Scholar
  • Yagiura M., Yamaguchi T., Ibaraki T., Voß S., Martello S., Osman I. H., Roucairol C. A variable depth search algorithm for the generalized assignment problem. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization (1999) (Kluwer Academic Publishers, Boston, MA) 459–471CrossrefGoogle 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.