# Evolutionary Scheduling: A Review

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## Abstract

Early and seminal work which applied evolutionary computing methods to scheduling problems from 1985 onwards laid a strong and exciting foundation for the work which has been reported over the past decade or so. A survey of the current state-of-the-art was produced in 1999 for the European Network of Excellence on Evolutionary Computing EVONET—this paper provides a more up-to-date overview of the area, reporting on current trends, achievements, and suggesting the way forward.

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scheduling evolutionary algorithms## Preview

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## References

- 1.U. Aickelin and K. Dowsland “Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem,” Journal of Scheduling, vol. 3, no. 3, pp. 139–153, 2000.zbMATHMathSciNetGoogle Scholar
- 2.I. Al-Harkan “On merging sequencing and scheduling theory with genetic algorithms to solve stochastic job shops,” PhD thesis, University of Oklahoma, 1997.Google Scholar
- 3.L. Atlan, J. Bonnet, and M. Naillon “Learning distributed reactive strategies by genetic programming for the general job shop problem,” in Proceedings of the 7th annual Florida Artificial Intelligence Research Symposium, D. Dankel, and J. Stewman, (Eds.) IEEE Press, Pensacola, Florida, USA, May 1994.Google Scholar
- 4.T. Bagchi Multiobjective Scheduling by Genetic Algorithms, Kluwer: Boston, 1999.Google Scholar
- 5.S. Bagchi, S. Uckun, Y. Miyabe, and K. Kawamura “Exploring problem-specificrecombination operators for job shop scheduling,” in Proceedings of the Fourth International Conference on Genetic Algorithms, R. Belew and L. Booker, (Eds.) Morgan Kaufmann: San Mateo, 1991, pp. 10–17.Google Scholar
- 6.A. Bauer, B. Bullnheimer, R. Hartl, and C. Strauss “An ant colony optimization approach for the single machine tardiness problem,” in Proceedings of the 1999 Congress on Evolutionary Computation, P. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala (Eds.), IEEE Press, 1999, pp. 1445–1450.Google Scholar
- 7.J. Beasley “Or-library: Distributing test problems by electronic mail,” Journal of the Operational Research Society, vol. 41, no. 11, pp. 1069–1072, 1990.Google Scholar
- 8.R. Belew and B. L. Booker (Eds.), in Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann: San Mateo, 1991.Google Scholar
- 9.C. Bierwirth, D. Mattfeld, and H. Kopfer “On permutation representations for scheduling problems,” in Parallel Problem Solving from Nature: PPSN IV, Y. Davidor, H.-P. Schwefel, and R. Manner (Eds.) Springer-Verlag: Berlin, 1996, LNCS 1141, pp. 310–318.Google Scholar
- 10.C. Bierwith and D. Mattfeld “Production scheduling and rescheduling with genetic algorithms,” Evolutionary Computation, vol. 7, no. 1, pp. 1–17, 1999.Google Scholar
- 11.J. Blackstone, D. Phillips, and G. Hogg “A state-of-the-art survey of dispatching rules for manufacturing job operations,” International Journal of Production Research, vol. 20, pp. 27–45, 1982.Google Scholar
- 12.C. Brizuela and R. Aceves “Experimental genetic operators analysis for the multi-objective permutation flowshop,” in Evolutionary Multicriterion Optimization; EMO 2003, C. Fonseca, P. Fleming, E. Zitzler, K. Deb, and L. Thiele (Eds.) Springer-Verlag: Berlin, 2003, pp. 578–592.Google Scholar
- 13.R. Bruns “Direct chromosome representation and advanced genetic algorithms for production scheduling,” in Proceedings of the Fifth International Conference on Genetic Algorithms, S. Forrest (Ed.), Morgan Kaufmann: San Mateo, Feb. 1993, pp. 352–359.Google Scholar
- 14.A. Cardon, T. Galinho, and J.-P. Vacher “A multi-objective genetic algorithm in job shop scheduling problem to refine an agents’ architecture,” in Proceedings of EUROGEN’99, K. Miettinen, M. M. Mäkelä, P. Neittaanmäki, and J. Periaux (Eds.) University of Jyváskylä, Jyväskylä, Finland, 1999.Google Scholar
- 15.H. M. Cartwright and A. L. Tuson “Genetic algorithms and flowshop scheduling: Towards the development of a real-time process control system,” in Selected Papers: AISB Workshop on Evolutionary Computing, T. C. Fogarty (Ed.), Lecture Notes in Computer Science No. 865, Springer Verlag, 1994, pp. 277–290.Google Scholar
- 16.R. Cheng, M. Gen, and Y. Tsujimura “A tutorial survey of job-shop scheduling problems using genetic algorithms—i. representation,” Computers and Industrial Engineering, vol. 30, no. 4, pp. 983–997, 1996.Google Scholar
- 17.S. Chien, A. Govindjee, T. Estlin, X. Wang, T. Fisher, and R. H. Jr “Automating generation of tracking plans for a network of communications antennas,” in International Workshop on Planning and Scheduling for Space Exploration and Science: Workshop Notes, S. Chien (Ed.), NASA JPL, 1997.Google Scholar
- 18.G. A. Cleveland and S. F. Smith “Using genetic algorithms to schedule flow shop releases,” in Proceedings of the Third International Conference on Genetic Algorithms and their Applications, J. D. Schaffer (Ed.), Morgan Kaufmann: San Mateo, 1989, pp. 160–169.Google Scholar
- 19.A. Colorni, M. Dorigo, V. Maniezzo, and M. Trubian “Ant system for job-shopscheduling,” JORBEL—Belgian Journal of Operations Research, Statistics and Computer Science, vol. 34, pp. 39–53, 1994.zbMATHGoogle Scholar
- 20.R. Congram, C. Potts, and S. Van de Velde “An iterated dynasearch algorithmfor the single-machine total weighted tardiness scheduling problem,” Technical report, Faculty of Mathematical Studies, University of Southhampton, 1998.Google Scholar
- 21.D. Corne, M. Dorigo, and F. Glover, (Eds.) in New Ideas in Optimization, chap. Ant Colony Optimization. McGraw-Hill: London, 1999.Google Scholar
- 22.D. Corne and J. Ogden “Evolutionary optimisation of methodist preacher timetables,” in PATAT 97: Practice and Theory of Automated Timetabling II, 1997, pp. 142–156.Google Scholar
- 23.A. Costa, P. Vargas, F. Von Zuben, and P. Franca “Makespan minimisation on parallel processors: An immune based approach,” in Proceedings of the 2002 Congress on EvolutionaryComputation (CEC2002), Fogel et al. (Eds.) IEEE Press, 2002, pp. 920–926.Google Scholar
- 24.P. Cowling, G. Kendal, and L. Han “An investigation of a hyper-heuristic genetic algorithm applied to a trainer scheduling problem,” in Proceedings of the 2002 Congress on Evolutionary Computation (CEC2002), Fogel et al. (Eds.) IEEE Press, 2002, pp. 118–1190.Google Scholar
- 25.X. Cui, M. Li, and T. Fang “Study of population diversity of multiobjectiveevolutionary algorithm based on immune and entropy principles,” in Proceedings of the IEEE Congresson Evolutionary Computation, IEEE Press: Piscataway, NJ, 2001, pp. 1316–1321.Google Scholar
- 26.L. Davis “Job shop scheduling with genetic algorithms,” in Proceedings of the International Conference on Genetic Algorithms and their Applications, J. J. Grefenstette (Ed.), Morgan Kaufmann: San Mateo, 1985, pp. 136–140.Google Scholar
- 27.L. de Castro and J. Timmis Aritifical Immune Systems: A New Computational Intelligence Paradigm. Springer, London, 2002.Google Scholar
- 28.M. den Besten, T. Stützle, and M. Dorigo “Ant colony optimization for thetotal weighted tardiness problem,” in Parallel Problem Solving from Nature: 6th International Conferencence, M. Schoenauer et al. (Eds.) Number 1917 in Lecture Notes in Computer Science, Springer Verlag, 2000, pp. 611–620.Google Scholar
- 29.C. Dimopoulos and A. M. S. Zalzala “Evolving scheduling policies through a genetic programming framework,” in Proceedings of the Genetic and Evolutionary Computation Conference, W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith (Eds.), Morgan Kaufmann: Orlando, Florida, USA, 13–17 July 1999, vol. 2, pp. 1231.Google Scholar
- 30.C. Dimopoulos and A. M. S. Zalzala “A genetic programming heuristic for the one-machine total tardiness problem, “in Proceedings of the Congress on Evolutionary Computation, P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala (Eds.) IEEE Press: Mayflower Hotel, Washington, D.C., USA, 6–9 July 1999, vol. 3, pp. 2207–2214.Google Scholar
- 31.C. Dimopoulos and A. M. S. Zalzala “Investigating the use of genetic programming for a classic one-machine scheduling problem,” Advances in Engineering Software, vol. 32, pp. 489–498, 2001.zbMATHGoogle Scholar
- 32.M. Dorigo, V. Maniezzo, and A. Colorni “The ant system: Optimization by a colony of cooperating anrts,” IEEE Trans. Systems, Man and Cybernetics—Part B, vol. 26, pp. 29–41, 1996.Google Scholar
- 33.U. Dorndorf and E. Pesch “Evolution based learning in a job shop scheduling environment,” Computers in Operations Research, vol. 22, pp. 25–40, 1995.zbMATHGoogle Scholar
- 34.M. Drummond “Scheduling benchmarks and related resources,” Newsletter of the AAAI SIGMAN, vol. 6, no. 3, 1993.Google Scholar
- 35.F. Easton and N. Mansour “A distributed ga for employee staffing and scheduling,” in [?] 1993, pp. 360-367.Google Scholar
- 36.A. Eiben and C. Schippers “Multi-parent’s niche:
*n*-ary crossovers on nk-landscapes,” in Proceedings of the 4th Conference on Parallel Problem Solving from Nature, H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel (Eds.) 1996, pp. 319–328.Google Scholar - 37.S. Esquivel, G. Leguizamon, F. Zuppa, and R. Gallard “A performance comparison of alternative heuristics for fsp,” in Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2002, S. Cagnoni et al. (Eds.) Springer, Berlin, 2002, pp. 51–60.Google Scholar
- 38.H.-L. Fang “Genetic algorithms in timetabling and scheduling,” PhD thesis, Department of Artificial Intelligence, University of Edinburgh, 1994.Google Scholar
- 39.H.-L. Fang, D. Corne, and P. Ross “A genetic algorithm for job-shop problems with various schedule quality criteria,” in Evolutionary Computing: 1996 AISB Workshop: Selected Papers, T. Fogarty (Ed.), Lecture Notes in Computer Science 1143, Springer, 1996, pp. 39–49.Google Scholar
- 40.H.-L. Fang, P. Ross, and D. Corne “A promising Genetic Algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems,” in Proceedings of the Fifth International Conference on Genetic Algorithms, S. Forrest (Ed.), Morgan Kaufmann: San Mateo, 1993, pp. 375–382.Google Scholar
- 41.H.-L. Fang, P. Ross, and D. Corne “A promising hybrid ga/heuristic approach to open-shop scheduling problems,” in ECAI’94: Proceedings of the 11th European Conference on Artificial Intelligence, A. Cohn (Ed.), Wiley, 1994, pp. 590–594.Google Scholar
- 42.H. Fisher and G. L. Thompson “Probabilistic learning combinations of local job-shop scheduling rules,” in Industrial Scheduling, J. F. Muth and G. L. Thompson (Eds.) Prentice Hall: Englewood Cliffs, New Jersey, 1963, pp. 225–251.Google Scholar
- 43.D. B. Fogel, M. A. El-Sharkawi, X. Yao, G. Greenwood, H. Iba, P. Marrow, and M. Shackleton (Eds.) in Proceedings of the 2002 Congress on Evolutionary Computation (CEC2002). IEEE Press, 2002.Google Scholar
- 44.S. French Sequencing and Scheduling, John Wiley: New York, 1982.zbMATHGoogle Scholar
- 45.T. Fukuda, M. Mori, and M. Tsukiyama “Immune networks using genetic algorithms for adaptive production scheduling,” in Proceedings of the 15th IFAC World Congress, G. Goodwinand R. Evans (Eds.) Pergamon Press Ltd.: London, pp. 57–60.Google Scholar
- 46.B. Giffler and G. Thompson “Algorithm for solving production scheduling problems,” Operations Research, vol. 8, no. 4, pp. 487–503, 1960.zbMATHMathSciNetGoogle Scholar
- 47.C. A. Grimes “Application of genetic techniques to the planning of railway track maintenance work,” in First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA, A. M. S. Zalzala (Ed.), IEE: Sheffield, UK, 12–14 Sept. 1995, vol. 414, pp. 467-472.Google Scholar
- 48.L. Han, G. Kendall, and P. Cowling “An adaptive length chromosome hyperheuristic genetic algorithm for a trainer scheduling proble,” Technical Report NOTTCS-TR-2002-5, University of Nottingham, 2002.Google Scholar
- 49.M. Hapke, A. Jaskiewicz, and K. Kurowski “Multi-objective genetic local search methods for the flowshopproblem,” in Advances in Nature-Inspired Computation: The PPSN VII Workshops, PEDAL, University of Reading, UK, 2002, pp. 22–23.Google Scholar
- 50.E. Hart and P. Ross “A heuristic combination method for solving job-shop scheduling problems,” in Parallel Problem Solving from Nature—PPSN V, A. E. Eiben, T. Bäck, M. Schoenauer, and H.-P. Schwefel (Eds.) Springer: Berlin, 1998, pp. 845–854, Lecture Notes in Computer Science 1498.Google Scholar
- 51.E. Hart and P. Ross “An immune system approach to scheduling in changing environments,” in Genetic and Evolutionary Computation Conference—GECCO 1999, W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith (Eds.), 1999, pp. 1559–1565.Google Scholar
- 52.E. Hart and P. Ross “A systematic investigation of ga performance on jobshop scheduling problems,” in Real-World Applications of Evolutionary Computing, S. Cagnoni et al. (Eds.) Springer, 2000, pp. 277-287.Google Scholar
- 53.E. Hart, P. Ross, and J. Nelson “Producing robust schedules via an artificial immune system,” in IEEE World Congress on Computational Intelligence, ICEC 1998, IEEE Press, 1998, pp. 464–469.Google Scholar
- 54.E. Hart, P. Ross, and J. Nelson “Solving a real world problem using an evolving heuristically driven schedule builder,” Evolutionary Computation, vol. 6, no. 1, pp. 61–80, 1998.Google Scholar
- 55.S. Hartmann and R. Kolisch “Experimental evaluation of state-of-the-art heuristics for the resource-constrainted scheduling problem,” European Journal of Operations Research, vol. 127, no. 1, pp. 394–407, 2000.zbMATHGoogle Scholar
- 56.J. Herrmann “A genetic algorithm for minimax optimzation problems,” in Proceedings of the 1999 Congress on Evolutionary Computation, P. Angeline, Z. Michalewicz, M. Schoenhauer, X. Yao, and A. Zalzala (Eds.) IEEE Press, 1999, vol. 2, pp. 1099–1103.Google Scholar
- 57.P. Husbands, M. McIlhagga, and R. Ives Handbook of Evolutionary Computation, chap. ‘An ecosystem model for integrated production planning,’ Institute of Physics Publishing and Oxford University Press: Bristol, New York, 1997, pp. G9.3:1–8.Google Scholar
- 58.P. Husbands and F. Mill “Simulated co-evolution as the mechanism for emergent planning and scheduling,” in Proceedings of the Fifth International Conference on Genetic Algorithms, 1991, R. Belew, and B. L. Booker (Eds.) Morgan Kaufmann: San Mateo, 1991, pp. 264–270.Google Scholar
- 59.P. Husbands, F. Mill, and S. Warrington “Genetic algorithms, production planning optimisation and scheduling,” in Parallel Problem Solving from Nature, H.-P. Schwefel and R. Männer (Eds.) vol. 496 of Lecture Notes in Computer Science, Springer, 1990, pp. 80–84.Google Scholar
- 60.H. Ishibuchi and T. Murata “Multi-objective genetic local search algorithm and its application to flowshop scheduling,” IEEE Transactions on Systems, Man and Cybernetics, vol. 28, no. 3, pp. 392–403, 1998.Google Scholar
- 61.A. Jain and S. Meeran “A state-of-the-art review of job-shop scheduling techniques,” Technical report, University of Dundee, 1998.Google Scholar
- 62.A. Jan, M. Yamamoto, and A. Ohuchi “Evolutionary algorithms for nurse scheduling problem,” in Congress on Evolutionary Computation, 2000, IEEE Press: Piscataway, NJ, pp. 196–203.Google Scholar
- 63.M. Jensen “Finding worst-cae flexible schedules using co-evolution,” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, Spector et al. (Eds.) Morgan Kaufmann: San Francisco, California, USA, 2001, pp. 1144–1151.Google Scholar
- 64.M. Jensen and T. Hansen “Robust solutions to job-shop problems,” in Proceedings of the 1999 Congress on Evolutionary Computation, P. Angeline, Z. Michalewicz, M. Schoenhauer, X. Yao, and A. Zalzala, (Eds.) IEEE Press, 1999, vol. 2, pp. 1138–1144.Google Scholar
- 65.D. J. John “Co-evolution with the bierwith-mattfeld hybrid scheduler,” in Proceedings of the Genetic and Evolutionary Computation Conference (GEECCO), W. B. Langdon, et al. (Eds.) Morgan Kaufmann Publishers: New York, 2002, pp. 259.Google Scholar
- 66.A. Juels and M. Wattenberg “Stochastic hillclimbing as a baseline method for evaluating genetic algorithms,” Technical Report UCB Technical Report CSD-94-834, Department of Computer Science, University of California at Berkeley, 1994.Google Scholar
- 67.J. Kaschel, B. Meier, M. Fischer, and T. Teich “Evolutionary real-world shop floor scheduling using parallelization and parameter coevolution,” in Proceedings of the Genetic and Evolutionary Computation Conference, D. Whitley et al. (Eds.) Morgan Kaufmann: San Francisco, CA, 2000, pp. 697–701.Google Scholar
- 68.M. Kidwell “Using genetic algorithms to schedule distributed tasks on a bus-based system,” in Proceedings of the Fifth International Conference on Genetic Algorithms, S. Forrest (Ed.), Morgan Kaufmann: San Mateo, 1993, pp. 368–374.Google Scholar
- 69.R. King, S. Russ, A. Lambert, and D. Reese “An artificial immune system model for intelligent agents,” Future Generation Computer Systems, Elsevier Science, vol. 17, pp. 335–343, 1999.Google Scholar
- 70.S. Kobayashi, I. Ono, and M. Yammamura “An efficient genetic algorithm for job shop scheduling problems,” in ICGA, L. J. Eshelman (Ed.), Morgan Kaufmann, 1995, pp. 506–511.Google Scholar
- 71.R. Kolisch and A. Sprecher “Psplib—a project scheduling problem library,” Eurpoean Journal of Operations Research, vol. 96, no. 1, pp. 205–216, 1996.Google Scholar
- 72.W. Langdon “Scheduling maintenance of electrical power transmission networks using genetic programming,” in Artificial Intelligence Techniques in Power Systems, K. Warwick, A. Ekwue, and R. Aggarwal (Eds.) IEE Press, UK, 1997, chap. 10, pp. 220–237.Google Scholar
- 73.W. Langdon “Scheduling planned maintenance of the south wales region of the national grid,” in Evolutionary Computing: AISB International Workshop 1997: Selected Papers, D. Corne, and J. Shapiro (Eds.), Lecture Notes in Computer Science 1305, Springer: Berlin, 1997, pp. 181–197.Google Scholar
- 74.W. B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska (Eds.) in GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann Publishers: New York, 2002.Google Scholar
- 75.S.-C. Lin, E. Goodman, and W. Punch “Investigating parallel genetic algorithms on job shop scheduling problems,” in Evolutionary Programming VI, P. Angeline, R. Reynolds, J. McDonnell, and R. Eberhart (Eds.) Lecture Notes in Computer Science 1213, Springer, 1997, pp. 383–393.Google Scholar
- 76.S.-C. Lin, E. D. Goodman, and W. F. Punch “A genetic algorithm approach to dynamic job-shop scheduling problems,” in Proceedings of the Seventh International Conference on Genetic Algorithms, T. Back (Ed.), Morgan-Kaufmann, 1997, pp. 481–489.Google Scholar
- 77.
- 78.S. Matsui, I. Watanabe, and K. Tokoro “Real-coded parameter-free genetic algorithms for job-shop scheduling,” in Parallel Problem Solving from Nature—PPSN VII, J. Merelo-Guervos, P. Adamidis, H.-G. Beyer, J.-L. Fernandez-Villacanas, and H.-P. Schwefel (Eds.) 2002, pp. 801–810.Google Scholar
- 79.D. C. Mattfield Evolutionary Search and the Job-Shop, Physica-Verlag, Heidelberg, 1996.Google Scholar
- 80.D. Merkle and M. Middendorf “An ant algorithm with a new pheromone evaluation rule for total tardiness problems,” in Applications of Evolutionary Computing, EvoWorkshops (2000), S. Cagnoni et al. (Eds.) vol. 1803 of LNCS, Springer-Verlag, 2000, pp. 287–296.Google Scholar
- 81.D. Merkle, M. Middendorf, and H. Schmeck “Ant colony optimization for resource-constrained project scheduling,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ‘00), D. Whitleyet al. (Eds.) Morgan Kaufmann, 2000: Las Vegas, Nevada, USA, July 8–12,2000, pp. 893–900.Google Scholar
- 82.K. Miyashita “Job-shop scheduling with GP,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), D. Whitley, D. Goldberg, E. Cantu-Paz, L. Spector I. Parmee, and H.-G. Beyer (Eds.) Morgan Kaufmann: Las Vegas, Nevada, USA, 10–12 July 2000, pp. 505–512.Google Scholar
- 83.D. Montana, G. Bidwell, G. Vidaver, and J. Herrero “Scheduling and route selection for military land moves using genetic algorithms,” in Congress on Evolutionary Computation, P. Angeline, Z. Michalewicz, M. Schoenauer X. Yao, and A. Zalzala (Eds.) IEEE Press, 1999, pp. 1118-1123.Google Scholar
- 84.D. Montanta “A reconfigurable optimising scheduler,” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO -2001, L. Spector et al. (Eds.) Morgan Kaufmann: San Francisco, California, USA, 2001, pp. 1159–1166.Google Scholar
- 85.M. Mori and T. Fukuda “Immune algorithm and its application to factory load dispatching,” in Proc. of the JAPAN-USA Symposium on Flexible Automation, 1994, pp. 1343–1346.Google Scholar
- 86.M. Mori, M. Tsukiyama, and T. Fukuda “Adapative scheduling system inspired by the immune system,” in Proceedings of the IEEE Conference on Systems, Man and Cybernetics, IEEE Computer Society Press: Los Alamitos, US, 1998, pp. 3833–3837.Google Scholar
- 87.T. Morton and D. Pentico Heuristic Scheduling Systems, John Wiley: New York, 1993.Google Scholar
- 88.R. Nakano and T. Yamada “Conventional genetic algorithm for job shop problems,” in Proceedings of the Fifth International Conference on Genetic Algorithms, R. Belew and B. L. Booker (Eds.) Morgan Kaufmann: San Mateo, 1991.Google Scholar
- 89.Nasa new millenium program. http://nmp.jpl.nasa.gov/.
- 90.Neosoft http://www.NeoSoft.com/benchmarx/.
- 91.B. Norman and J. Bean “Random keys genetic algorith m for job shop scheduling,” Technical report, Department of Industrial and Operations Engineering, University of Michigan, 1994.Google Scholar
- 92.B. Norman and J. Bean “Operation sequencing and tool assignment for multiple spindle cnc machines,” in ICEC’97: Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, IEEE Press: Piscataway, NJ, 1997, pp. 425–429.Google Scholar
- 93.E. Nowicki and C. Smutnicki “A fast tabu search algorithm for the permutation flowshop problem,” European Journal of OR, Elsevier, vol. 91, pp. 160–175, 1996.zbMATHGoogle Scholar
- 94.I. Ono, M. Yamamura, and S. Kobayashi “A genetic algorithm for job shop scheduling problems using job-based order crossover,” in ICEC’96: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, IEEE, 1996, pp. 547–552.Google Scholar
- 95.S. Ottner “Developing scheduling software using genetic algorithms in a commercial environment,” in GECC) 2002: Proceedings of the Genetic and Evolutionary Computation Conference, W. B. Langdon et al. (Eds.)Morgan Kaufmann Publishers: New York, 2002, pp. 80–87.Google Scholar
- 96.R. Padman and S. Roehrig “A genetic programming approach for heuristic selection in constrained project scheduling,” in Interfaces in Computer Science and Operations Research: Advances in Metaheuristics, Optimization, and Stochastic Modeling Technologies, R. S. Barr, R. V. Helgason, and J. L. Kennington (Eds.) Kluwer Academic Publishers: Norwell, MA, USA, 1997, chap. 18, pp. 405-421.Google Scholar
- 97.G. Palmer An Integrated Approach to Manufacturing Planning. PhD thesis, School of Engineering, University of Huddersfield, 1994.Google Scholar
- 98.S. Panwalker and W. Iskander “A survey of scheduling rules,” Operations Research, vol. 25, no. 1, pp. 45–61, 1977.MathSciNetGoogle Scholar
- 99.P. Poon and N. Carter “J. Genetic algorithm crossover operators for ordering applications,” Computers and Operations Research, vol. 22, pp. 135–147, 1995.zbMATHGoogle Scholar
- 100.E. Ramat, G. Venturini, C. Lente, and M. Simane “Solving the multiple resource constrained project scheduling problem with a hybrid genetic algorithm,” in Proceedings of the Seventh International Conference on Genetic Algorithms, T. Back (Ed.), Morgan Kaufmann, 1997, pp. 489–496.Google Scholar
- 101.S. Rana, A. Howe, K. Mathias, and D. Whitley “Comparing heuristic, evolutionary and local search approaches to scheduling,” in The Third Artificial Intelligence Planning Systems Conference—AIPS-96, B. Drabble (Ed.), AAAI Press, 1996, pp. 174–181.Google Scholar
- 102.C. Reeves and T. Yamada “Genetic algorithms, path-relinking and the flowshop sequencing problem,” Evolutionary Computation, vol. 6, pp. 45–60, 1998.Google Scholar
- 103.P. Ross, E. Hart, and D. Corne Some Observations about ga Based Timetabling, Springer-Verlag: Heidelberg, 1997, pp. 115–130.Google Scholar
- 104.K. R. Ryu, J. Hwang, H. R. Choi, and K. K. Cho “A genetic algorithm hybrid for hierarchical reactive scheduling,” in Proceedings of the Seventh International Conference on Genetic Algorithms, T. Back (Ed.), Morgan Kaufmann, 1997, pp. 497–505.Google Scholar
- 105.J. Sakuma and S. Kobayashi “Extrapolation-directed crossover for job-shop scheduling problems: Complementary combination with jox,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ‘00), D. Whitley, et al. (Eds.)Morgan Kaufmann, 2000, Las Vegas, Nevada, USA, July 8–12, 2000, pp. 973–980.Google Scholar
- 106.H. Sawai and S. Kizu “Parameter-free genetic algorithm inspired by “disparity theory of evolution,””in Proceedings of PPSN-V, Springer-Verlag: Berlin-Heidelberg, 1998, pp. 702–711.Google Scholar
- 107.K. Shaw and P. Fleming “Initial study of practical multi-objective genetic algorithms for scheduling the production of chilled ready meals,” in Proceedings of Mendel’96, the 2nd International Mendel Conference on Genetic Algorithms, P. Osmera (Ed.), ISBN 80-214-0769-7, 1996.Google Scholar
- 108.L. Spector, E. D. Goodman, A. Wu, W. B. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. H. Garzon, and E. Burke (Eds.) in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO -2001, Morgan Kaufmann: San Francisco, California, USA, 2001.Google Scholar
- 109.T. Starkweather, S. McDaniel, K. Mathias, D. Whitley, and C. Whitley “A comparison of genetic sequencing operators,” in Proceedings of the Fifth International Conference on Genetic Algorithms, R. Belew and B. L. Booker (Eds.) Morgan Kaufmann: San Mateo, 1991.Google Scholar
- 110.T. St ü tzle “An ant approach for the flowshop problem,” in Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing (EUFIT’98), H. Zimmerman (Ed.), Verlag Mainz: Aachen, Germany, 1998, vol. 3, pp. 1560–1564.Google Scholar
- 111.T. St ü tzle and H. Hoos “Improvements on the ant system: Introducing
*MAX*_{M}*IN*ant system,” in Artificial Neural Networks and Genetic Algorithms, G. Smith and R. Steele (Eds.) Springer-Verlag: Wien, 1998, pp. 245–259.Google Scholar - 112.G. Syswerda “Schedule optimization using genetic algorithms,” in Handbook of Genetic Algorithms, L. Davis (Ed.), New York: Van Nostrand Reinhold, 1991, pp. 332–349.Google Scholar
- 113.E. Taillard “Some efficient heuristic methods for the flowshop sequencing problem,” European Journal of Operations Research, Elsevier, vol. 47, pp. 65–74, 1990.zbMATHMathSciNetGoogle Scholar
- 114.E. Taillard “Benchmarks for basic scheduling problems,” European Journal of O perations R esearch, Elsevier, vol. 64, pp. 278–285, 1993.zbMATHGoogle Scholar
- 115.D. Todd and P. Sen “Multiple criteria scheduling using genetic algorithms in a shipyard environment,” in Proceedings of the 9th International Conference on Computer Applications in Shipbuilding, K. Johannson and T. Koyama (Eds.)ISBN 4930966027, 1997, pp. 259–274.Google Scholar
- 116.R. Vaessens, E. Aarts, and J. Lenstra “Job shop scheduling by local search,” INFORMS Journal of Computing, vol. 8, pp. 302–317, 1996.CrossRefzbMATHGoogle Scholar
- 117.M. Vásquez and L. Whitley “A comparison of genetic algorithms for the dynamic job shop scheduling problem,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ‘00), D. Whitley et al. (Eds.) Morgan Kaufmann, 2000, Las Vegas, Nevada, USA, July 8–12, 2000, pp. 1011-1018.Google Scholar
- 118.M. Vásquez and L. Whitley “A comparison of genetic algorithms for the static job shop scheduling problem,” in PPSN VI: Proceedings of the Parallel Problem Solving from Nature Conference, M. Schoenhauer (Ed.), Springer: London, 2000, pp. 303–312.Google Scholar
- 119.J. Watson, L. Barbulescu, A. Howe, and L. Whitley “Algorithm performance and problem structure for flowshop scheduling,” in Proceedings of the Sixteenth National Conference on Artificial Intelligence, J. Hendler et al. (Eds.) AAAI: Menlo Park, CA, 1999, pp. 688–695.Google Scholar
- 120.D. Whitley, D. E. Goldberg, E. Cant ú -Paz, L Spector, I. C. Parmee, and H.-G. Beyer (Eds.) in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ‘00), Morgan Kaufmann, 2000, Las Vegas, Nevada, USA, July 8–12, 2000.Google Scholar
- 121.D. Whitley, T. Starkweather, and D. Fuquay “Scheduling problems and travelling salesmen: The genetic edge recombination operator,” in Proceedings of the Third International Conference on Genetic Algorithms, J. D. Schaffer (Ed.), Morgan Kaufmann: San Mateo, 1989, pp. 133–140.Google Scholar
- 122.D. Whitley, T. Starkweather, and D. Shaner “Traveling salesman and sequence scheduling: Quality solutions using genetic edge recombination,” in Handbook of Genetic Algorithms, L. Davis (Ed.), Van Nostrand Reinhold: New York, 1991, pp. 350–372.Google Scholar
- 123.D. Wolpert and W. Macready “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997.Google Scholar
- 124.S. Wu, E. Byeon, and R. Storer “A graph-theoretic decomposition of the job-shop scheduling problem to achieve scheduling robustness,” Operations Research, vol. 47, pp. 113–124, 1999.zbMATHMathSciNetGoogle Scholar
- 125.T. Yamada and R. Nakano “Scheduling by genetic local search with multi-step crossover,” in Parallel Problem Solving from Nature—PPSN IV, H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel (Eds.) Lecture Notes in Computer Science 1141, Springer, 1996, pp. 960–969.Google Scholar
- 126.J. Zhang, L. Zhao, and W. Kwon “Scheduling and optimization for a class of single-stage hybrid manufacturing systems,” in Proceedings of the IEEE International Conference on Robotics and Automation, IEEE, 2001, pp. 3115–3120.Google Scholar

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