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Memetic Algorithms in Planning, Scheduling, and Timetabling

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Evolutionary Scheduling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 49))

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References

  1. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. John Wiley & Sons, New York (1966)

    MATH  Google Scholar 

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

    Google Scholar 

  3. Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuttgart (1973)

    Google Scholar 

  4. Schwefel, H.P.: Kybernetische Evolution als Strategie der experimentellen Forschung in der Strömungstechnik. Diplomarbeit, Technische Universität Berlin, Hermann Föttinger-Institut für Strömungstechnik (1965)

    Google Scholar 

  5. Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated an- nealing. Science 220 (1983) 671-680

    MathSciNet  Google Scholar 

  6. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston, MA (1997)

    MATH  Google Scholar 

  7. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1) (1997) 67-82

    Google Scholar 

  8. Hart, W.E., Belew, R.K.: Optimizing an arbitrary function is hard for the genetic algorithm. In Belew, R.K., Booker, L.B., eds.: Proceedings of the 4th International Conference on Genetic Algorithms, San Mateo CA, Morgan Kaufmann (1991) 190-195

    Google Scholar 

  9. Davis, L.D.: Handbook of Genetic Algorithms. Van Nostrand Reinhold Computer Library, New York (1991)

    Google Scholar 

  10. Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Mar- tial Arts: Towards Memetic Algorithms. Technical Report Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, California, USA (1989)

    Google Scholar 

  11. Moscato, P.: Memetic algorithms: A short introduction. In Corne, D., Dorigo, M., Glover, F., eds.: New Ideas in Optimization. McGraw-Hill, Maidenhead, Berkshire, England, UK (1999) 219-234

    Google Scholar 

  12. Moscato, P., Cotta, C.: A gentle introduction to memetic algorithms. In Glover, F., Kochenberger, G., eds.: Handbook of Metaheuristics. Kluwer Academic Publishers, Boston MA (2003) 105-144

    Google Scholar 

  13. Moscato, P., Cotta, C., Mendes, A.S.: Memetic algorithms. In Onwubolu, G.C., Babu, B.V., eds.: New Optimization Techniques in Engineering. Springer-Verlag, Berlin Heidelberg (2004) 53-85

    Google Scholar 

  14. Dawkins, R.: The Selfish Gene. Clarendon Press, Oxford (1976)

    Google Scholar 

  15. Culberson, J.: On the futility of blind search: An algorithmic view of “No Free Lunch”. Evolutionary Computation 6 (1998) 109-127

    Google Scholar 

  16. Eiben, A.E., Raue, P.E., Ruttkay, Z.: Genetic algorithms with multi-parent re- combination. In Davidor, Y., Schwefel, H.P., Männer, R., eds.: Parallel Problem Solving From Nature III. Volume 866 of Lecture Notes in Computer Science. Springer-Verlag (1994) 78-87

    Google Scholar 

  17. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA (1989)

    MATH  Google Scholar 

  18. Jones, T.C.: Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University of New Mexico (1995)

    Google Scholar 

  19. Davidor, Y., Ben-Kiki, O.: The interplay among the genetic algorithm opera- tors: Information theory tools used in a holistic way. In Männer, R., Manderick, B., eds.: Parallel Problem Solving From Nature II, Amsterdam, Elsevier Science Publishers B.V. (1992) 75-84

    Google Scholar 

  20. Radcliffe, N.J.: Non-linear genetic representations. In Männer, R., Manderick, B., eds.: Parallel Problem Solving From Nature II, Amsterdam, Elsevier Science Publishers B.V. (1992) 259-268

    Google Scholar 

  21. Fox, B.R., McMahon, M.B.: Genetic operators for sequencing problems. In Rawl- ins, G.J.E., ed.: Foundations of Genetic Algorithms I, San Mateo, CA, Morgan Kaufmann (1991) 284-300

    Google Scholar 

  22. Cotta, C., Troya, J.M.: Genetic forma recombination in permutation flowshop problems. Evolutionary Computation 6 (1998) 25-44

    Google Scholar 

  23. Mathias, K., Whitley, L.D.: Genetic operators, the fitness landscape and the traveling salesman problem. In Männer, R., Manderick, B., eds.: Parallel Problem Solving From Nature II, Amsterdam, Elsevier Science Publishers B.V. (1992) 221- 230

    Google Scholar 

  24. Goldberg, D.E., Lingle Jr., R.: Alleles, loci and the traveling salesman problem. In Grefenstette, J.J., ed.: Proceedings of the 1st International Conference on Genetic Algorithms, Hillsdale NJ, Lawrence Erlbaum Associates (1985) 154-159

    Google Scholar 

  25. Davidor, Y.: Epistasis Variance: Suitability of a Representation to Genetic Algorithms. Complex Systems 4 (1990) 369-383

    Google Scholar 

  26. Davidor, Y.: Epistasis variance: A viewpoint on GA-hardness. In Rawlins, G.J.E., ed.: Foundations of Genetic Algorithms I, San Mateo, CA, Morgan Kaufmann (1991)23-35

    Google Scholar 

  27. Radcliffe, N.J., Surry, P.D.: Fitness Variance of Formae and Performance Predic- tion. In Whitley, L.D., Vose, M.D., eds.: Foundations of Genetic Algorithms III, San Francisco, CA, Morgan Kaufmann (1994) 51-72

    Google Scholar 

  28. Manderick, B., de Weger, M., Spiessens, P.: The Genetic Algorithm and the Struc- ture of the Fitness Landscape. In Belew, R.K., Booker, L.B., eds.: Proceedings of the 4th International Conference on Genetic Algorithms, San Mateo, CA, Morgan Kaufmann (1991) 143-150

    Google Scholar 

  29. Dzubera, J., Whitley, L.D.: Advanced Correlation Analysis of Operators for the Traveling Salesman Problem. In Schwefel, H.P., Männer, R., eds.: Parallel Prob- lem Solving from Nature III. Volume 866 of Lecture Notes in Computer Science., Dortmund, Germany, Springer-Verlag, Berlin, Germany (1994) 68-77

    Google Scholar 

  30. Aickelin, U., Dowsland, K.: Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem. Journal of Scheduling 3 (2000) 139-153

    MATH  MathSciNet  Google Scholar 

  31. Puente, J., Vela, C.R., Prieto, C., Varela, R.: Hybridizing a genetic algorithm with local search and heuristic seeding. In Mira, J., Álvarez, J.R., eds.: Artificial Neural Nets Problem Solving Methods. Volume 2687 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2003) 329-336

    Google Scholar 

  32. Varela, R., Serrano, D., Sierra, M.: New codification schemas for scheduling with genetic algorithms. In Mira, J., Á lvarez, J.R., eds.: Artificial Intelligence and Knowledge Engineering Applications: a Bioinspired Approach. Volume 3562 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2005) 11-20

    Google Scholar 

  33. Varela, R., Puente, J., Vela, C.R.: Some issues in chromosome codification for scheduling with genetic algorithms. In Castillo, L., Borrajo, D., Salido, M.A., Oddi, A., eds.: Planning, Scheduling and Constraint Satisfaction: From Theory to Practice. Volume 117 of Frontiers in Artificial Intelligence and Applications. IOS Press (2005) 1-10

    Google Scholar 

  34. Radcliffe, N.J.: The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence 10 (1994) 339-384

    MATH  MathSciNet  Google Scholar 

  35. Oğuz, C., Ercan, M.F.: A genetic algorithm for hybrid flow-shop scheduling with multiprocessor tasks. Journal of Scheduling 8 (2005) 323-351

    MATH  MathSciNet  Google Scholar 

  36. Cotta, C., Troya, J.M.: Embedding branch and bound within evolutionary algorithms. Applied Intelligence 18 (2003) 137-153

    MATH  Google Scholar 

  37. Ibaraki, T.: Combination with dynamic programming. In Bäck, T., Fogel, D., Michalewicz, Z., eds.: Handbook of Evolutionary Computation. Oxford University Press, New York NY (1997) D3.4:1-2

    Google Scholar 

  38. Yamada, T., Nakano, R.: A genetic algorithm with multi-step crossover for job- shop scheduling problems. In: Proceedings of the 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield, UK, Institution of Electrical Engineers (1995) 146-151

    Google Scholar 

  39. Wang, L., Zheng, D.Z.: A modified genetic algorithm for job-shop scheduling. International Journal of Advanced Manufacturing Technology 20 (2002) 72-76

    Google Scholar 

  40. Maheswaran, R., Ponnambalam, S.G., Aranvidan, C.: A meta-heuristic approach to single machine scheduling problems. International Journal of Advanced Manufacturing Technology 25 (2005) 772-776

    Google Scholar 

  41. Liaw, C.F.: A hybrid genetic algorithm for the open shop scheduling problem. European Journal of Operational Research 124 (2000) 28-42

    MATH  MathSciNet  Google Scholar 

  42. Sevaux, M., Dauzère-Pérès, S.: Genetic algorithms to minimize the weighted number of late jobs on a single machine. European Journal of Operational Research 151 (2003)296-306

    MATH  MathSciNet  Google Scholar 

  43. Burke, E.K., Newall, J., Weare, R.: A memetic algorithm for university exam timetabling. In Burke, E.K., Ross, P., eds.: Practice and Theory of Automated Timetabling. Volume 1153 of Lecture Notes in Computer Science., Berlin Heidel- berg, Springer-Verlag (1996)

    Google Scholar 

  44. Burke, E.K., Smith, A.J.: A memetic algorithm to schedule planned grid main- tenance. In Mohammadian, M., ed.: Computational Intelligence for Modelling, Control and Automation, IOS Press (1999) 12-127

    Google Scholar 

  45. França, P.M., Gupta, J.N.D., Mendes, A.S., Moscato, P., Veltnik, K.J.: Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups. Computers and Industrial Engineering 48 (2005) 491- 506

    Google Scholar 

  46. Hansen, P., Mladenović, N.: Variable neighborhood search: Principles and appli- cations. European Journal of Operational Research 130 (2001) 449-467

    MATH  MathSciNet  Google Scholar 

  47. Yeh, W.C.: A memetic algorithm fo the n/2/Flowshop/αF+ βCmax scheduling problem. International Journal of Advanced Manufacturing Technology 20 (2002) 464-473

    Google Scholar 

  48. Varela, R., Gómez, A., Vela, C.R., Puente, J., Alonso, C.: Heuristic generation of the initial population in solving job shop problems by evolutionary strategies. In Mira, J., Sánchez-Andrés, J.V., eds.: Foundations and Tools for Neural Modeling. Volume 1606 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer- Verlag (1999) 690-699

    Google Scholar 

  49. Varela, R., Puente, J., Vela, C.R., Gómez, A.: A knowledge-based evolutionary strategy for scheduling problems with bottlenecks. European Journal of Opera- tional Research 145 (2003) 57-71

    MATH  Google Scholar 

  50. Burke, E.K., Petrovic, S.: Recent research directions in automated timetabling. European Journal of Operational Research 140 (2002) 266-280

    MATH  Google Scholar 

  51. Rossi-Doria, O., Paechter, B.: A memetic algorithm for university course timetabling. In: Combinatorial Optimisation 2004 Book of Abstracts, Lancaster, UK, Lancaster University (2004) 56

    Google Scholar 

  52. Laguna, M., Martí, R.: Scatter Search. Methodology and Implementations in C. Kluwer Academic Publishers, Boston MA (2003)

    Google Scholar 

  53. Nagata, Y., Kobayashi, S.: Edge assembly crossover: A high-power genetic algo- rithm for the traveling salesman problem. In Bäck, T., ed.: Proceedings of the Seventh International Conference on Genetic Algorithms, East Lansing, EE.UU., San Mateo, CA, Morgan Kaufmann (1997) 450-457

    Google Scholar 

  54. Yamada, T.>, Reeves, C.R.: Solving the Csum permutation flowshop scheduling problem by genetic local search. In: 1998 IEEE International Conference on Evolutionary Computation, Piscataway, NJ, IEEE Press (1998) 230-234

    Google Scholar 

  55. Ž d ánský, M., Poživil, J.: Combination genetic/tabu search algorithm for hybrid flowshops optimization. In: Proceedings of ALGORITMY 2002 - Conference on Scientific Computing, Vysoke Tatry, Podbanske, Slovakia (2002) 230-236

    Google Scholar 

  56. Costa, D.: An evolutionary tabu search algorithm and the NHL scheduling problem. INFOR 33 (1995) 161-178

    MATH  Google Scholar 

  57. Greistorfer, P.: Hybrid genetic tabu search for a cyclic scheduling problem. In Voß, S., Martello, S., Osman, I.H., Roucairol, C., eds.: Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer Academic Publishers, Boston, MA (1998) 213-229

    Google Scholar 

  58. Greistorfer, P.: Genetic tabu search extensions for the solving of the cyclic reg- ular max-min scheduling problem. In: International Conference on Operations Research (OR98), Zürich, Schwitzerland (1998)

    Google Scholar 

  59. Burke, E.K., Cowling, P.I., De Causmaecker, P., van den Berghe, G.: A memetic approach to the nurse rostering problem. Applied Intelligence 15 (2001) 199-214

    MATH  Google Scholar 

  60. Batenburg, K.J., Palenstijn, W.J.: A new exam timetabling algorithm. In Hes- kes, T., Lucas, P., Vuurpijl, L., Wiegerinck, W., eds.: Proceedings of the 15th Belgian-Dutch Conference on Artificial Intelligence BNAIC’03, Nijmegen, The Netherlands (2003) 19-26

    Google Scholar 

  61. Digalakis, J., Margaritis, K.: A multipopulation memetic model for the mainte- nance scheduling problem. In: Proceedings of the 5th Hellenic European Confer- ence on Computer Mathematics and its Applications, Athens, Greece, LEA Press (2001)318-323

    Google Scholar 

  62. Burke, E.K., Smith, A.J.: A memetic algorithm to schedule planned maintenance for the national grid. Journal of Experimental Algorithmics 4 (1999) 1-13

    Google Scholar 

  63. Ponnambalam, S.G., Mohan Reddy, M.: A GA-SA multiobjective hybrid search algorithm for integrating lot sizing and sequencing in flow-line scheduling. International Journal of Advanced Manufacturing Technology 21 (2003) 126-137

    Google Scholar 

  64. Houck, C., Joines, J.A., Kay, M.G., Wilson, J.R.: Empirical investigation of the benefits of partial lamarckianism. Evolutionary Computation 5 (1997) 31-60

    Google Scholar 

  65. Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation 7 (2003) 204-223

    Google Scholar 

  66. Jones, T.C., Forrest, S.: Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In Eshelman, L.J., ed.: Proceedings of the 6th International Conference on Genetic Algorithms, Morgan Kaufmann (1995) 184- 192

    Google Scholar 

  67. Merz, P., Freisleben, B.: Fitness landscapes and memetic algorithm design. In Corne, D., Dorigo, M., Glover, F., eds.: New Ideas in Optimization. McGraw-Hill, Maidenhead, Berkshire, England, UK (1999) 245-260

    Google Scholar 

  68. English, T.M.: Evaluation of evolutionary and genetic optimizers: No free lunch. In Fogel, L.J., Angeline, P.J., Bäck, T., eds.: Evolutionary Programming V, Cambridge, MA, MIT Press (1996) 163-169

    Google Scholar 

  69. Bierwirth, C., Mattfeld, D.C., Watson, J.P.: Landscape regularity and random walks for the job shop scheduling problem. In Gottlieb, J., Raidl, G.R., eds.: Evo- lutionary Computation in Combinatorial Optimization. Volume 3004 of Lecture Notes in Computer Science., Berlin, Springer-Verlag (2004) 21-30

    Google Scholar 

  70. Grefenstette, J.J.: Genetic algorithms for changing environments. In Männer, R., Manderick, B., eds.: Parallel Problem Solving from Nature II, Amsterdam, North-Holland Elsevier (1992) 137-144

    Google Scholar 

  71. Hadj-Alouane, A.B., Bean, J.C., Murty, K.G.: A hybrid genetic/optimization algorithm for a task allocation problem. Journal of Scheduling 2 (1999) 181-201

    MathSciNet  Google Scholar 

  72. Tomassini, M.: Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Springer-Verlag (2005)

    Google Scholar 

  73. França, P.M., Mendes, A.S., Müller, F., Moscato, P.: Memetic algorithms ap- plied to the single machine and parallel machine scheduling problems. In: Anais da Primeira Oficina de Planejamento e Controle da Produção em Sistemas de Manufatura, Campinas, SP, Brazil (1999)

    Google Scholar 

  74. França, P.M., Mendes, A.S., Moscato, P.: Memetic algorithms to minimize tar- diness on a single machine with sequence-dependent setup times. In Despotis, D.K., Zopounidis, C., eds.: Proceedings of the 5th International Conference of the Decision Sciences Institute, Athens, Greece (1999) 1708-1710

    Google Scholar 

  75. Mendes, A.S., Müller, F., França, P.M., Moscato, P.: Comparing meta-heuristic approaches for parallel machine scheduling problems with sequence-dependent setup times. In: Procedings of the 15th International Conference on CAD/CAM Robotics and Factories of the Future, Á guas de Lindóia, SP, Brazil (1999) 1-6

    Google Scholar 

  76. França, P.M., Gupta, J.N.D., Mendes, A.S., Moscato, P., Veltnik, K.J.: Meta- heuristic approaches for the pure flowshop manufacturing cell problem. In: Pro- ceedings of the 7th International Workshop on Project Management and Schedul- ing, Osnabrück, Germany (2000) 128-130

    Google Scholar 

  77. Mendes, A.S., França, P.M., Moscato, P.: Fuzzy-evolutionary algorithms applied to scheduling problems. In: Proceedings of the 1st World Conference on Production and Operations Management, Seville, Spain (2000) 1-10

    Google Scholar 

  78. França, P.M., Mendes, A.S., Moscato, P.: A memetic algorithm for the total tardiness single machine scheduling problem. European Journal of Operational Research 132 (2001) 224-242

    MATH  MathSciNet  Google Scholar 

  79. Mendes, A.S., França, P.M., Moscato, P.: Fitness landscapes for the total tardiness single machine scheduling problem. Neural Network World 2 (2002) 165-180

    Google Scholar 

  80. Moscato, P., Mendes, A., Cotta, C.: Scheduling and production & control. In Onwubolu, G.C., Babu, B.V., eds.: New Optimization Techniques in Engineering. Springer-Verlag, Berlin Heidelberg (2004) 655-680

    Google Scholar 

  81. Cotta, C., Alba, E., Troya, J.M.: Stochastic reverse hillclimbing and iterated local search. In: Proceedings of the 1999 Congress on Evolutionary Computation, Washington D.C., IEEE Neural Network Council - Evolutionary Programming Society - Institution of Electrical Engineers (1999) 1558-1565

    Google Scholar 

  82. Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory, Washington DC (1990)

    Google Scholar 

  83. Krasnogor, N.: Studies on the Theory and Design Space of Memetic Algorithms. PhD thesis, Faculty of Engineering, Computer Science and Mathematics. University of the West of England. Bristol, United Kingdom (2002)

    Google Scholar 

  84. Sevaux, M., Sörensen, K.: A genetic algorithm for robust schedules. In: Proceedings of the 8th International Workshop on Project Management and Scheduling. (2002)330-333

    Google Scholar 

  85. Cheng, R., Gen, M.: Parallel machine scheduling problems using memetic algorithms. Computers and Industrial Engineering 33 (1997) 761-764

    Google Scholar 

  86. Bonfim, T.R., Yamakami, A.: Neural network applied to the coevolution of the memetic algorithm for solving the makespan minimization problem in parallel machine scheduling. In Ludermir, T.B., de Souto, M.C.P., eds.: Proceedings of the 7th Brazilian Symposium on Neural Networks (SBRN 2002), Recife, Brazil, IEEE Computer Society (2002) 197-199

    Google Scholar 

  87. Yamada, T., Reeves, C.R.: Permutation flowshop scheduling by genetic local search. In: Proceedings of the 2nd International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, London, UK, Institution of Electrical Engineers (1997) 232-238

    Google Scholar 

  88. Reeves, C.R., Yamada, T.: Genetic algorithms, path relinking and the flowshop sequencing problem. Evolutionary Computation 6 (1998) 230-234

    Google Scholar 

  89. Glover, F.: Scatter search and path relinking. In Corne, D., Dorigo, M., Glover, F., eds.: New Methods in Optimization. McGraw-Hill, London (1999) 291-316

    Google Scholar 

  90. Sevaux, M., Jouglet, A., Oğuz, C.: MLS+CP for the hybrid flowshop scheduling problem. In: Workshop on the Combination of metaheuristic and local search with Constraint Programming techniques, Nantes, France (2005)

    Google Scholar 

  91. Sevaux, M., Jouglet, A., Oğuz, C.: Combining constraint programming and memetic algorithm for the hybrid flowshop scheduling problem. In: ORBEL 19th annual conference of the SOGESCI-BVWB, Louvain-la-Neuve, Belgium (2005)

    Google Scholar 

  92. Yamada, T., Nakano, R.: A fusion of crossover and local search. In: IEEE International Conference on Industrial Technology ICIT’96, Shangai, China, IEEE Press (1996) 426-430

    Google Scholar 

  93. Yamada, T., Nakano, R.: Scheduling by genetic local search with multi-step crossover. In Voigt, H.M., Ebeling, W., Rechenberg, I., Schwefel, H.P., eds.: Parallel Problem Solving From Nature IV. Volume 1141 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (1996) 960-969

    Google Scholar 

  94. Wang, L., Zheng, D.Z.: An effective hybrid optimization strategy for job-shop scheduling problems. Computers & Operations Research 28 (2001) 585-596

    MATH  MathSciNet  Google Scholar 

  95. Paechter, B., Cumming, A., Luchian, H.: The use of local search suggestion lists for improving the solution of timetable problems with evolutionary algorithms. In Fogarty, T.C., ed.: AISB Workshop on evolutionary computing. Volume 993 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (1995) 86-93

    Google Scholar 

  96. Rankin, B.: Memetic timetabling in practice. In Burke, E.K., Ross, P., eds.: Proceedings of the 1st International Conference on the Practice and Theory of Automated Timetabling. Volume 1153 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (1996) 266-279

    Google Scholar 

  97. Paechter, B., Cumming, A., Norman, M.G., Luchian, H.: Extensions to a memetic timetabling system. In Burke, E.K., Ross, P., eds.: Proceedings of the 1st Interna- tional Conference on the Practice and Theory of Automated Timetabling. Volume 1153 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (1996)251-265

    Google Scholar 

  98. Burke, E.K., Newall, J.P.: Investigating the benefits of utilising problem specific heuristics within a memetic timetabling algorithm. Workin Paper NOTTCS-TR- 97-6, dept. of Computer Science, University of Nottingham, UK (1997)

    Google Scholar 

  99. Burke, E.K., Newall, J.: A multi-stage evolutionary algorithm for the timetable problem. IEEE Transactions on Evolutionary Computation 3 (1999) 63-74

    Google Scholar 

  100. Alkan, A., Ö zcan, E.: Memetic algorithms for timetabling. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation, Canberra, Australia, IEEE Press (2003) 1796-1802

    Google Scholar 

  101. Wilke, P., Gröbner, M., Oster, N.: A hybrid genetic algorithm for school timetabling. In McKay, B., Slaney, J., eds.: AI 2002: Advances in Artificial Intelli- gence, 15th Australian Joint Conference on Artificial Intelligence. Volume 2557 of Lecture Notes in Computer Science., Canberra, Australia, Springer (2002) 455- 464

    Google Scholar 

  102. Burke, E.K., Jackson, K., Kingston, J.H., Weare, R.F.: Automated university timetabling: The state of the art. The Computer Journal 40 (1997) 565-571

    Google Scholar 

  103. Burke, E.K., Landa Silva, J.D.: The design of memetic algorithms for scheduling and timetabling problems. In Krasnogor, N., Hart, W., Smith, J., eds.: Recent Advances in Memetic Algorithms. Volume 166 of Studies in Fuzziness and Soft Computing. Springer-Verlag (2004) 289-312

    Google Scholar 

  104. Petrovic, S., Burke, E.K.: University timetabling. In Leung, J., ed.: Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Chapman Hall/CRC Press (2004)

    Google Scholar 

  105. Semet, Y., Schoenauer, M.: An efficient memetic, permutation-based evolutionary algorithm for real-world train timetabling. In: Proceedings of the 2005 Congress on Evolutionary Computation, Edinburgh, UK, IEEE Press (2005) 2752-2759

    Google Scholar 

  106. Hertz, A., de Werra, D.: Using tabu search techniques for graph coloring. Computing 39 (1987) 345-351

    MATH  MathSciNet  Google Scholar 

  107. Costa, D.: On the use of some known methods for T-colorings of graphs. Annals of Operations Research 41 (1993) 343-358

    MATH  Google Scholar 

  108. Schönberger, J., Mattfeld, D.C., Kopfer, H.: Memetic algorithm timetabling for non-commercial sport leagues. European Journal of Operational Research 153 2004102-116

    MATH  MathSciNet  Google Scholar 

  109. Aickelin, U.: Nurse rostering with genetic algorithms. In: Proceedings of young operational research conference 1998, Guildford, UK (1998)

    Google Scholar 

  110. De Causmaecker, P., van den Berghe, G.: Using tabu search as a local heuristic in a memetic algorithm for the nurse rostering problem. In: Proceedings of the 13th Conference on Quantitative Methods for Decision Making (ORBEL XIII), Brussels (1999)

    Google Scholar 

  111. Gröbner, M., Wilke, P.: Optimizing employee schedules by a hybrid genetic algorithm. In Boers, E.J.W., et al., eds.: Applications of Evolutionary Computing 2001. Volume 2037 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2001) 463-472

    Google Scholar 

  112. Burke, E.K., De Causmaecker, P., van den Berghe, G.: Novel metaheuristic approaches to nurse rostering problems in belgian hospitals. In Leung, J., ed.: Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Chapman Hall/CRC Press (2004) 44.1-44.18

    Google Scholar 

  113. Ö zcan, E.: Memetic algorithms for nurse rostering. In Yolum, P., Güngör, T., Gürgen, F.S., Ö zturan, C.C., eds.: Computer and Information Sciences - ISCIS 2005, 20th International Symposium (ISCIS). Volume 3733 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2005) 482-492

    Google Scholar 

  114. Li, J., Kwan, R.S.K.: A fuzzy genetic algorithm for driver scheduling. European Journal of Operational Research 147 (2003) 334-344

    MATH  Google Scholar 

  115. Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6 (1995) 109-133

    MATH  MathSciNet  Google Scholar 

  116. Li, J., Kwan, R.S.K.: A self adjusting algorithm for driver scheduling. Journal of Heuristics 11 (2005) 351-367

    MATH  Google Scholar 

  117. Burke, E.K., Smith, A.J.: Hybrid evolutionary techniques for the maintenance scheduling problem. IEEE Transactions on Power Systems 15 (2000) 122-128

    Google Scholar 

  118. Burke, E.K., Smith, A.J.: A memetic algorithm for the maintenance scheduling problem. In: Proceedings of the 4th International Conference on Neural Infor- mation Processing ICONIP’97, Dunedin, New Zealand, Springer-Verlag (1997) 469-474

    Google Scholar 

  119. Burke, E., Clark, J., Smith, J.: Four methods for maintenance scheduling. In Smith, G., Steele, N., Albrecht, R., eds.: Artificial Neural Nets and Genetic Algorithms 3, Wien New York, Springer-Verlag (1998) 264-269

    Google Scholar 

  120. Evans, S., Fletcher, I.: A variation on a memetic algorithm for boiler scheduling. In Hotz, L., Krebs, T., eds.: Proceedings Workshop Planen und Konfigurieren (PuK-2003), Hamburg, Germany (2003)

    Google Scholar 

  121. Valenzuela, J., Smith, A.: A seeded memetic algorithm for large unit commitment problems. Journal of Heuristics 8 (2002) 173-195

    Google Scholar 

  122. Marriot, K., Stuckey, P.J.: Programming with constraints. The MIT Press, Cambridge, Massachusetts (1998)

    Google Scholar 

  123. Smith, B.M.: A tutorial on constraint programming. Research Report 95.14, University of Leeds, School of Computer Studies, England (1995)

    Google Scholar 

  124. Dechter, R.: Constraint processing. Morgan Kaufmann (2003)

    Google Scholar 

  125. Apt, K.R.: Principles of constraint programming. Cambridge University Press (2003)

    Google Scholar 

  126. Frühwirth, T., Abdennadher, S.: Essentials of constraint programming. Cognitive Technologies Series. Springer-Verlag (2003)

    Google Scholar 

  127. Tsang, E.: Foundations of constraint satisfaction. Academic Press, London and San Diego (1993)

    Google Scholar 

  128. Larrosa, J., Morancho, E., Niso, D.: On the practical use of variable elimination in constraint optimization problems: ‘still life’ as a case study. Journal of Artificial Intelligence Research 23 (2005) 421-440

    MATH  Google Scholar 

  129. Baptiste, P., Le Pape, C.: Constraint propagation and decomposition techniques for highly disjunctive and highly cumulative project scheduling problems. Constraints 5 (2000) 119-139

    MATH  MathSciNet  Google Scholar 

  130. Barták, R.: Constraint satisfaction for planning and scheduling. In Vlahavas, I., Vrakas, D., eds.: Intelligent Techniques for Planning. Idea Group, Hershey, PA, USA (2005)

    Google Scholar 

  131. Le Pape, C.: Constraint-based scheduling: A tutorial. Proceedings of the 1st International Summer School on Constraint Programming (2005)

    Google Scholar 

  132. Kilby, P., Prosser, P., Shaw, P.: A comparison of traditional and constraint-based heuristic methods on vehicle routing problems with side constraints. Constraints 5 (2000)389-414

    MATH  MathSciNet  Google Scholar 

  133. Oliveira, E., Smith, B.M.: A combined constraint-based search method for single-track railway scheduling problem. In Brazdil, P., Jorge, A., eds.: Proceedings of the 10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving. Volume 2258 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2001) 371-378

    Google Scholar 

  134. Wallace, R.J., Freuder, E.C.: Supporting dispatchability in schedules with consumable resources. Journal of Scheduling 8 (2005) 7-23

    MATH  MathSciNet  Google Scholar 

  135. Khemmoudj, M.O.I., Porcheron, M., Bennaceur, H.: Using constraint programming and local search for scheduling of electricité de france nuclear power plant outages. In: Proceedings of the Workshop on the Combination of Metaheuris- tic and Local Search with Constraint Programming techniques, Nantes, France (2005)

    Google Scholar 

  136. Bistarelli, S.: Semirings for Soft Constraint Solving and Programming. Volume 2962 of Lecture Notes in Computer Science. Springer-Verlag, Berlin Heidelberg (2004)

    Google Scholar 

  137. Rossi, F.: Preference reasoning. In van Beek, P., ed.: Proceedings of the 11th International Conference on Principles and Practice of Constraint Programming. Volume 3709 of Lecture Notes in Computer Science., Berlin Heidelberg, SpringerVerlag (2005) 9-12

    Google Scholar 

  138. Backer, B.D., Furnon, V., Shaw, P., Kilby, P., Prosser, P.: Solving vehicle routing problems using constraint programming and metaheuristics. Journal of Heuristics 6 (2000)501-523

    MATH  Google Scholar 

  139. Yun, Y.S., Gen, M.: Advanced scheduling problem using constraint programming techniques in SCM environment. Computers & Industrial Engineering 43 (2002) 213-229

    Google Scholar 

  140. Merlot, L.T.G., Boland, N., Hughes, B.D., Stuckey, P.J.: A hybrid algorithm for the examination timetabling problem. In Burke, E.K., Causmaecker, P.D., eds.: Proceedings of the 4th International Conference on the Practice and Theory of Automated Timetabling. Volume 2740 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2003) 207-231

    Google Scholar 

  141. Terashima, H.: Combinations of GAs and CSP strategies for solving examination timetabling problems. PhD thesis, Instituto Tecnológico y de Estudios Superiores de Monterrey (1998)

    Google Scholar 

  142. Landa Silva, J.D., Burke, E.K., Petrovic, S.: An introduction to multiobjective metaheuristics for scheduling and timetabling. In X., G., M., S., Sörensen K. and, T.V., eds.: Metaheuristic for Multiobjective Optimisation. Volume 535 of Lecture Notes in Economics and Mathematical Systems. Springer (2004) 91-129

    Google Scholar 

  143. Krasnogor, N., Smith, J.E.: Emergence of profitable search strategies based on a simple inheritance mechanism. In Spector, L., et al., eds.: Proceedings of the 2001 Genetic and Evolutionary Computation Conference, Morgan Kaufmann (2001) 432-439

    Google Scholar 

  144. Kendall, G., Soubeiga, E., Cowling, P.I.: Choice function and random hyperheuristics. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL’02). (2002) 667-671

    Google Scholar 

  145. Burke, E.K., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyperheuristics: an emerging direction in modern search technology. In Glover, F., Kochenberger, G., eds.: Handbook of Metaheuristics. Kluwer Academic Publishers, Boston MA (2003) 457-474

    Google Scholar 

  146. Cowling, P.I., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In Burke, E., Erben, W., eds.: Selected Papers of the 3rd PATAT conference. Volume 2079 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2000) 176-190

    Google Scholar 

  147. Cowling, P.I., Kendall, G., Soubeiga, E.: Hyperheuristics: A tool for rapid proto-typing in scheduling and optimisation. In Cagnoni, S., et al., eds.: Applications of Evolutionary Computing. Volume 2279 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2002) 1-10

    Google Scholar 

  148. Cowling, P.I., Kendall, G., Soubeiga, E.: Hyperheuristics: A robust optimisation method applied to nurse scheduling. In Merelo, J.J., et al., eds.: Parallel Problem Solving from Nature VII. Volume 2439 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2002) 851-860

    Google Scholar 

  149. Burke, E.K., Kendall, G., Soubeiga, E.: A tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics 9 (2003) 451-470

    Google Scholar 

  150. Burke, E.K., McCollum, B., Meisels, A., Petrovic, S., Qu, R.: A graph-based hyper-heuristic for educational timetabling problems. European Journal of Operational Research (2006) In press.

    Google Scholar 

  151. Cowling, P.I., Ouelhadj, D., Petrovic, S.: Dynamic scheduling of steel casting and milling using multi-agents. Production Planning and Control 15 (2002) 1-11

    Google Scholar 

  152. Cowling, P.I., Ouelhadj, D., Petrovic, S.: A multi-agent architecture for dynamic scheduling of steel hot rolling. Journal of Intelligent Manufacturing 14 (2002) 457-470

    Google Scholar 

  153. Ouelhadj, D., Petrovic, S., Cowling, P.I., Meisels, A.: Inter-agent cooperation and communication for agent-based robust dynamic scheduling in steel production. Advanced Engineering and Informatics 18 (2005) 161-172

    Google Scholar 

  154. Cotta, C., Moscato, P.: Evolutionary computation: Challenges and duties. In Menon, A., ed.: Frontiers of Evolutionary Computation. Kluwer Academic Publishers, Boston MA (2004) 53-72

    Google Scholar 

  155. Barnier, N., Brisset, P.: Solving Kirkman’s schoolgirl problem in a few seconds. Constraints 10 (2005) 7-21

    MATH  MathSciNet  Google Scholar 

  156. Dotú, I., del Val, A., van Hentenryck, P.: Scheduling social tournaments. In van Beek, P., ed.: Proceedings of the 11th International Conference on Principles and Practice of Constraint Programming. Volume 3709 of Lecture Notes in Computer Science., Berlin Heidelberg, Springer-Verlag (2005) 845

    Google Scholar 

  157. Cheng, T.C.E., Diamond, J.: Optimal scheduling in film production to minimize talent hold cost. Journal of Optimization Theory and Applications 79 (1993) 197-206

    MathSciNet  Google Scholar 

  158. Smith, B.M.: Constraint programming in practice: scheduling a rehearsal. Re- search Report APES-67-2003, APES group (2003)

    Google Scholar 

  159. Fink, A., Voß, S.: Applications of modern heuristic search methods to pattern sequencing problems. Computers & Operations Research 26 (1999) 17-34

    MATH  Google Scholar 

  160. Downey, R., Fellows, M.: Parameterized Complexity. Springer-Verlag (1998)

    Google Scholar 

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Cotta, C., Fernàndez, A.J. (2007). Memetic Algorithms in Planning, Scheduling, and Timetabling. In: Dahal, K.P., Tan, K.C., Cowling, P.I. (eds) Evolutionary Scheduling. Studies in Computational Intelligence, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48584-1_1

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