Selecting Scheduling Heuristics Using Neural Networks

References

  • Alpsan D., Townsey M., Oldamar O., Tsol A.C., Ghista D.N. Efficiency of Modified Backpropagation and Optimization Methods on a Real-World Medical Problem. Neural Networks (1995) 8:945–962CrossrefGoogle Scholar
  • Burke L.I., Ignizio J.P. Neural Networks and Operations Research: An Overview. Computers and Operations Research (1992) 19:179–189CrossrefGoogle Scholar
  • Dorsey R.E., Mayer W.J. Genetic Algorithms for Estimation Problems with Multiple Optima, Non-Differentiability, and Other Irregular Features. Journal of Business and Economic Statistics (1995) 13:53–66CrossrefGoogle Scholar
  • Dorsey R.E., Johnson J.D., Mayer W.J. (1992) . The Genetic Adaptive Neural Network Training (GANNT) for Generic Feedforward Artificial Neural Systems, Working Paper, School of Business Administration, University of Mississippi, University, MSGoogle Scholar
  • Funahashi K.-I. On the Approximate Realization of Continuous Mappings by Neural Networks. Neural Networks (1989) 2:183–192CrossrefGoogle Scholar
  • Gupta J.N.D., Sexton R.S. Comparing Backpropagation with a Genetic Algorithm for Neural Network Training. OMEGA (1999) 27:679–684CrossrefGoogle Scholar
  • Gupta J.N.D., Tunc E.A. Minimizing Tardy Jobs in a Two-Stage Hybrid Flowshop. International Journal of Production Research (1998) 36:2397–2417CrossrefGoogle Scholar
  • Gupta J.N.D., Tunc E.A. Neural Networks Approach to Select Scheduling Heuristics for a Two-Stage Hybrid Flowshop. International Journal of Management and Systems (1997) 13:283–298Google Scholar
  • Hornik K., Stinchcombe M., White H. Multilayer Feed-forward Networks are Universal Approximators. Neural Networks (1989) 2:359–366CrossrefGoogle Scholar
  • Lawler E.L., Lenstar J.K., Rinnooy Kan A.H.G., Shmoys D.B., Graves S.C., Kan A.H.G. Rinnooy, Zipkin P.H. Sequencing and Scheduling: Algorithms and Complexity. Logistics of Production and Inventory (1993) (North-Holland, Amsterdam) 445–522CrossrefGoogle Scholar
  • Lenstra J.K., Rinnooy Kan A.H.G., Brucker P. Complexity of Machine Scheduling Problems. Annals of Discrete Mathematics (1977) 1:343–362CrossrefGoogle Scholar
  • Moore J.M. An n Job, One-Machine Sequencing Algorithm for Minimizing the Number of Late Jobs. Management Science (1968) 15:102–109LinkGoogle Scholar
  • Nygard K.E., Juell P., Kabada N. Neural Networks for Selecting Vehicle Routing Heuristics. ORSA Journal on Computing (1990) 4:485–493Google Scholar
  • Rumelhart D.E., Hinton G.G., Williams R.J., Rumelhart D.E. Learning Internal Representations by Error Propagation. Parallel Distributed Processing: Exploration in the Microstructure of Cognition (1986) (MIT Press, Cambridge, MA) 318–362Google Scholar
  • Sexton R.S., Dorsey R.E., Johnson J.D. Toward Global Optimization of Neural Networks. Decision Support Systems (1998) 22:171–185CrossrefGoogle Scholar
  • Smith K.A. Neural Network for Combinatorial Optimization: A Review of More Than a Decade of Research. INFORMS Journal on Computing (1999) 11:15–34LinkGoogle Scholar
  • Tuzun D., Magent M.A., Burke L.I. Selection of Vehicle Routing Heuristics Using Heuristic Networks. International Transactions on Operations Research (1997) 4:211–221CrossrefGoogle 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.