| Executing production schedules in the face of uncertainties: A review and some future directions H Aytug, MA Lawley, K McKay, S Mohan, R Uzsoy European Journal of Operational Research 161 (1), 86-110, 2005 | 939 | 2005 |
| Use of genetic algorithms to solve production and operations management problems: a review H Aytug, M Khouja, FE Vergara International Journal of Production Research 41 (17), 3955-4009, 2003 | 353 | 2003 |
| Detecting management fraud in public companies M Cecchini, H Aytug, GJ Koehler, P Pathak Management Science 56 (7), 1146-1160, 2010 | 349 | 2010 |
| Making words work: Using financial text as a predictor of financial events M Cecchini, H Aytug, GJ Koehler, P Pathak Decision Support Systems 50 (1), 164-175, 2010 | 289 | 2010 |
| Rapid modeling and discovery of priority dispatching rules: An autonomous learning approach CD Geiger, R Uzsoy, H Aytuğ Journal of Scheduling 9 (1), 7-34, 2006 | 227 | 2006 |
| A review of machine learning in scheduling H Aytug, S Bhattacharyya, GJ Koehler, JL Snowdon IEEE Transactions on Engineering Management 41 (2), 165-171, 1994 | 214 | 1994 |
| Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study H Aytug, C Saydam European Journal of Operational Research 141 (3), 480-494, 2002 | 138 | 2002 |
| New stopping criterion for genetic algorithms H Aytug, GJ Koehler European Journal of Operational Research 126 (3), 662-674, 2000 | 117 | 2000 |
| Stopping criteria for finite length genetic algorithms H Aytug, GJ Koehler INFORMS Journal on Computing 8 (2), 183-191, 1996 | 117 | 1996 |
| Accurate estimation of expected coverage: revisited C Saydam, H Aytuğ Socio-Economic Planning Sciences 37 (1), 69-80, 2003 | 81 | 2003 |
| Teaching a machine to feel postoperative pain: combining high-dimensional clinical data with machine learning algorithms to forecast acute postoperative pain PJ Tighe, CA Harle, RW Hurley, H Aytug, AP Boezaart, RB Fillingim Pain Medicine 16 (7), 1386-1401, 2015 | 71 | 2015 |
| Genetic learning of dynamic scheduling within a simulation environment H Aytug, GJ Koehler, JL Snowdon Computers & Operations Research 21 (8), 909-925, 1994 | 66 | 1994 |
| Feature selection for support vector machines using Generalized Benders Decomposition H Aytug European Journal of Operational Research 244 (1), 210-218, 2015 | 47 | 2015 |
| A Markov chain analysis of genetic algorithms with power of 2 cardinality alphabets H Aytug, S Bhattacharrya, GJ Koehler European Journal of Operational Research 96 (1), 195-201, 1997 | 45 | 1997 |
| Measures of subproblem criticality in decomposition algorithms for shop scheduling H Aytug, K Kempf, R Uzsoy International Journal of Production Research 41 (5), 865-882, 2003 | 44 | 2003 |
| A framework and a simulation generator for kanban-controlled manufacturing systems H Aytug ̄, CA Dog ̄an Computers & industrial engineering 34 (2), 337-350, 1998 | 44 | 1998 |
| Provider selection and task allocation issues in networks with different QoS levels and all you can send pricing N Kasap, H Aytug, SS Erenguc Decision Support Systems 43 (2), 375-389, 2007 | 35 | 2007 |
| Determining the number of kanbans: a simulation metamodeling approach H Aytug, CA Dogan, G Bezmez Simulation 67 (1), 23-32, 1996 | 32 | 1996 |
| Use of machine-learning classifiers to predict requests for preoperative acute pain service consultation PJ Tighe, SD Lucas, DA Edwards, AP Boezaart, H Aytug, A Bihorac Pain Medicine 13 (10), 1347-1357, 2012 | 31 | 2012 |
| Induction over strategic agents F Boylu, H Aytug, GJ Koehler Information Systems Research 21 (1), 170-189, 2010 | 30 | 2010 |
Reha UzsoyNorth Carolina State UniversityΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα ncsu.edu
