Evolutionary and Swarm Intelligence Algorithms pp 143-167 | Cite as
Genetic Programming for Job Shop Scheduling
- 3 Citations
- 928 Downloads
Abstract
Designing effective scheduling rules or heuristics for a manufacturing system such as job shops is not a trivial task. In the early stage, scheduling experts rely on their experiences to develop dispatching rules and further improve them through trials-and-errors, sometimes with the help of computer simulations. In recent years, automated design approaches have been applied to develop effective dispatching rules for job shop scheduling (JSS). Genetic programming (GP) is currently the most popular approach for this task. The goal of this chapter is to summarise existing studies in this field to provide an overall picture to interested researchers. Then, we demonstrate some recent ideas to enhance the effectiveness of GP for JSS and discuss interesting research topics for future studies.
Keywords
Genetic programming Job shop scheduling HeuristicReferences
- 1.Asano, M., Ohta, H.: A heuristic for job shop scheduling to minimize total weighted tardiness. Comput. Ind. Eng. 42, 137–147 (2002)CrossRefGoogle Scholar
- 2.Balas, E., Vazacopoulos, A.: Guided local search with shifting bottleneck for job shop scheduling. Manage. Sci. 44, 262–275 (1998)CrossRefGoogle Scholar
- 3.Banzhaf, W., Nordin, P., Keller, R., Francone, F.: Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco (1998)CrossRefGoogle Scholar
- 4.Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Dario, P., Sandini, G., Aebischer, P. (eds.) Robots and Biological Systems: Towards a New Bionics? NATO ASI Series, vol. 102, pp. 703–712. Springer, Berlin, Heidelberg (1993). https://doi.org/10.1007/978-3-642-58069-7_38CrossRefGoogle Scholar
- 5.Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Inc., New York, NY, USA (1999). http://portal.acm.org/citation.cfm?id=328320
- 6.Branke, J., Hildebrandt, T., Scholz-Reiter, B.: Hyper-heuristic evolution of dispatching rules: a comparison of rule representations. Evol. Comput. (2014) (in press). ( https://doi.org/10.1162/EVCO_a_00131)
- 7.Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.R.: Exploring hyper-heuristic methodologies with genetic programming. In: Mumford, C., Jain, L. (eds.) Computational Intelligence, Intelligent Systems Reference Library, vol. 1, pp. 177–201. Springer, Berlin, Heidelberg (2009)Google Scholar
- 8.Cheng, V.H.L., Crawford, L.S., Menon, P.K.: Air traffic control using genetic search techniques. In: McClamroch, N.H., Sano, A., Gruebel, G. (eds.) In: Proceedings of the 1999 IEEE International Conference on Control Applications, vol. 1, pp. 249–254. IEEE Press, Piscataway, NJ (1999)Google Scholar
- 9.Chiang, T.C., Shen, Y.S., Fu, L.C.: A new paradigm for rule-based scheduling in the wafer probe centre. Int. J. Prod. Res. 46(15), 4111–4133 (2008)CrossRefGoogle Scholar
- 10.Dimopoulos, C., Zalzala, A.M.S.: Investigating the use of genetic programming for a classic one-machine scheduling problem. Adv. Eng. Softw. 32(6), 489–498 (2001)CrossRefGoogle Scholar
- 11.El-Bouri, A., Balakrishnan, S., Popplewell, N.: Sequencing jobs on a single machine: a neural network approach. Eur. J. Oper. Res. 126(3), 474–490 (2000)MathSciNetCrossRefGoogle Scholar
- 12.Essafi, I., Mati, Y., Dauzère-Pérès, S.: A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem. Comput. Oper. Res. 35(8), 2599–2616 (2008)MathSciNetCrossRefGoogle Scholar
- 13.Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, 2nd edn. Springer, Germany (2006)zbMATHGoogle Scholar
- 14.Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Math. Oper. Res. 1(2), 117–129 (1976)MathSciNetCrossRefGoogle Scholar
- 15.Geiger, C.D., Uzsoy, R., Aytuğ, H.: Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J. Sched. 9(1), 7–34 (2006)CrossRefGoogle Scholar
- 16.Giffler, B., Thompson, G.L.: Algorithms for solving production-scheduling problems. Oper. Res. 8(4), 487–503 (1960)MathSciNetCrossRefGoogle Scholar
- 17.Goncalves, J.F., de Magalhaes Mendes, J.J., Resende, M.G.C.: A hybrid genetic algorithm for the job shop scheduling problem. Eur. J. Oper. Res. 167(1), 77–95 (2005)MathSciNetCrossRefGoogle Scholar
- 18.Hildebrandt, T., Heger, J., Scholz-Reiter, B.: Towards improved dispatching rules for complex shop floor scenarios—a genetic programming approach. In: Pelikan, M., Branke, J. (eds.) In: GECCO’10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 257–264. ACM Press, New York (2010)Google Scholar
- 19.Hildebrandt, T., Branke, J.: On using surrogates with genetic programming. Technical Report, Warwick Business School (2014)Google Scholar
- 20.Holthaus, O., Rajendran, C.: Efficient jobshop dispatching rules: further developments. Prod. Plann. Control 11(2), 171–178 (2000)CrossRefGoogle Scholar
- 21.Hunt, R., Johnston, M., Zhang, M.: Evolving “less-myopic” scheduling rules for dynamic job shop scheduling with genetic programming. In: GECCO’14: Proceedings of Genetic and Evolutionary Computation Conference (2014) (to appear)Google Scholar
- 22.Ingimundardottir, H., Runarsson, T.P.: Supervised learning linear priority dispatch rules for job-shop scheduling. In: Coello Coello, C.A. (ed.) Learning and Intelligent Optimization, LNCS, vol. 6683, pp. 263–277. Springer, Berlin, Heidelberg (2011)CrossRefGoogle Scholar
- 23.Jakobović, D., Budin, L.: Dynamic scheduling with genetic programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) Genetic Programming, LNCS, vol. 3905, pp. 73–84. Springer, Berlin, Heidelberg (2006)CrossRefGoogle Scholar
- 24.Jakobović, D., Marasović, K.: Evolving priority scheduling heuristics with genetic programming. Appl. Soft Comput. 12(9), 2781–2789 (2012)CrossRefGoogle Scholar
- 25.Jayamohan, M.S., Rajendran, C.: New dispatching rules for shop scheduling: a step forward. Int. J. Prod. Res. 38, 563–586 (2000)CrossRefGoogle Scholar
- 26.Jayamohan, M.S., Rajendran, C.: Development and analysis of cost-based dispatching rules for job shop scheduling. Eur. J. Oper. Res. 157(2), 307–321 (2004)CrossRefGoogle Scholar
- 27.Jedrzejowicz, P., Ratajczak-Ropel, E.: Agent-based gene expression programming for solving the RCPSP/max problem. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) Adaptive and Natural Computing Algorithms. Lecture Notes in Computer Science, vol. 5495, pp. 203–212. Springer, Berlin, Heidelberg (2009)CrossRefGoogle Scholar
- 28.Johnston, M., Liddle, T., Zhang, M.: A relaxed approach to simplification in genetic programming. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Şima Uyar, A. (eds.) Genetic Programming, LNCS, vol. 6021, pp. 110–121. Springer, Berlin, Heidelberg (2010)CrossRefGoogle Scholar
- 29.Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA (1992)zbMATHGoogle Scholar
- 30.Kreipl, S.: A large step random walk for minimizing total weighted tardiness in a job shop. J. Sched. 3, 125–138 (2000)MathSciNetCrossRefGoogle Scholar
- 31.Kuczapski, A.M., Micea, M.V., Maniu, L.A., Cretu, V.I.: Efficient generation of near optimal initial populations to enhance genetic algorithms for job-shop scheduling. Inf. Technol. Control 39(1), 32–37 (2010)Google Scholar
- 32.van Laarhoven, P.J.M., Aarts, E.H.L., Lenstra, J.K.: Job shop scheduling by simulated annealing. Oper. Res. 40(1), 113–125 (1992)MathSciNetCrossRefGoogle Scholar
- 33.Lourenco, H.R.: Job-shop scheduling: computational study of local search and large-step optimization methods. Eur. J. Oper. Res. 83(2), 347–364 (1995)CrossRefGoogle Scholar
- 34.McKay, K.N., Safayeni, F.R., Buzacott, J.A.: Job-shop scheduling theory: what is relevant? Interfaces 18, 84–90 (1988)CrossRefGoogle Scholar
- 35.Miyashita, K.: Job-shop scheduling with genetic programming. In: Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.G. (eds.) In: GECCO 2000: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 505–512. Morgan Kaufmann, San Francisco (2000)Google Scholar
- 36.Nguyen, S., Zhang, M., Johnston, M., Tan, K.: Learning iterative dispatching rules for job shop scheduling with genetic programming. Int. J. Adv. Manuf. Technol. 67(1–4), 85–100 (2013)CrossRefGoogle Scholar
- 37.Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: A computational study of representations in genetic programming to evolve dispatching rules for the job shop scheduling problem. IEEE Trans. Evol. Comput. 17(5), 621–639 (2013)CrossRefGoogle Scholar
- 38.Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Dynamic multi-objective job shop scheduling: a genetic programming approach. In: Etaner-Uyar, A.Ş., Özcan, E., Urquhart, N. (eds.) Automated Scheduling and Planning, Studies in Computational Intelligence, vol. 505, pp. 251–282. Springer, Berlin, Heidelberg (2013)CrossRefGoogle Scholar
- 39.Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Learning reusable initial solutions for multi-objective order acceptance and scheduling problems with genetic programming. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) Genetic Programming, LNCS, vol. 7831, pp. 157–168. Springer, Berlin, Heidelberg (2013)CrossRefGoogle Scholar
- 40.Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevolution genetic programming. IEEE Trans. Evol. Comput. 18(2), 193–208 (2014)CrossRefGoogle Scholar
- 41.Nguyen, S.: Automatic design of dispatching rules for job shop scheduling with genetic programming. Ph.D. thesis, Victoria University of Wellington (2013)Google Scholar
- 42.Nie, L., Gao, L., Li, P., Li, X.: A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates. J. Intell. Manuf. 24(4), 763–774 (2013)CrossRefGoogle Scholar
- 43.Nie, L., Shao, X., Gao, L., Li, W.: Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems. Int. J. Adv. Manuf. Technol. 50(5–8), 729–747 (2010)CrossRefGoogle Scholar
- 44.Nie, L., Bai, Y., Wang, X., Liu, K.: Discover scheduling strategies with gene expression programming for dynamic flexible job shop scheduling problem. In: Tan, Y., Shi, Y., Ji, Z. (eds.) Adv. Swarm Intell. 7332, 383–390 (2012)Google Scholar
- 45.Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Manage. Sci. 42, 797–813 (1996)CrossRefGoogle Scholar
- 46.Ouelhadj, D., Petrovic, S.: A survey of dynamic scheduling in manufacturing systems. J. Sched. 12(4), 417–431 (2009)MathSciNetCrossRefGoogle Scholar
- 47.Petrovic, S., Fayad, C., Petrovic, D., Burke, E., Kendall, G.: Fuzzy job shop scheduling with lot-sizing. Ann. Oper. Res. 159, 275–292 (2008)MathSciNetCrossRefGoogle Scholar
- 48.Pickardt, C.W., Hildebrandt, T., Branke, J., Heger, J., Scholz-Reiter, B.: Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems. Int. J. Prod. Econ. 145(1), 67–77 (2013)CrossRefGoogle Scholar
- 49.Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 3rd edn. Springer, New York (2008)zbMATHGoogle Scholar
- 50.Pinedo, M., Singer, M.: A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop. Naval Res. Logistics 46(1), 1–17 (1999)MathSciNetCrossRefGoogle Scholar
- 51.Ponnambalam, S.G., Ramkumar, V., Jawahar, N.: A multiobjective genetic algorithm for job shop scheduling. Prod. Plann. Control 12(8) (2001)CrossRefGoogle Scholar
- 52.Potts, C.N., Strusevich, V.A.: Fifty years of scheduling: a survey of milestones. J. Oper. Res. Soc. 60(Supplement 1), 41–68 (2009). http://www.palgrave-journals.com/jors/journal/v60/ns1/abs/jors20092a.htmlCrossRefGoogle Scholar
- 53.Sels, V., Gheysen, N., Vanhoucke, M.: A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions. Int. J. Prod. Res. 50(15), 4255–4270 (2011)CrossRefGoogle Scholar
- 54.Sha, D., Hsu, C.Y.: A hybrid particle swarm optimization for job shop scheduling problem. Comput. Ind. Eng. 51(4), 791–808 (2006)CrossRefGoogle Scholar
- 55.Tay, J.C., Ho, N.B.: Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Comput. Ind. Eng. 54(3), 453–473 (2008)CrossRefGoogle Scholar
- 56.Wong, P., Zhang, M.: Algebraic simplification of gp programs during evolution. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation. pp. 927–934. GECCO’06 (2006)Google Scholar
- 57.Xing, L.N., Chen, Y.W., Wang, P., Zhao, Q.S., Xiong, J.: A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl. Soft Comput. 10(3), 888–896 (2010)CrossRefGoogle Scholar
- 58.Yamada, T., Nakano, R.: A genetic algorithm with multi-step crossover for job-shop scheduling problems. In: GALESIA: First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications. pp. 146–151 (1995)Google Scholar
- 59.Yin, W.J., Liu, M., Wu, C.: Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming. In: Sarker, R., Reynolds, R., Abbass, H., Tan, K.C., McKay, B., Essam, D., Gedeon, T. (eds.) In: The 2003 Congress on Evolutionary Computation (CEC 2003), vol. 2, pp. 1050–1055. IEEE Press, Piscataway, NJ (2003)Google Scholar