Hyper-heuristic evolution of dispatching rules: A comparison of rule representations

Evolutionary Computation

Volumn 23, Issue 2, 2015, Pages 249-277

  Branke, Jürgen ^a   Hildebrandt, Torsten ^b   Scholz Reiter, Bernd ^b  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b UNIVERSITY OF BREMEN   (Germany)

Author keywords

Artificial neural network; CMA ES; Dispatching rule; Genetic programming; Job shop scheduling; Representation

Indexed keywords

BUDGET CONTROL; COMPLEX NETWORKS; FORESTRY; GENETIC ALGORITHMS; GENETIC PROGRAMMING; HEURISTIC ALGORITHMS; HEURISTIC METHODS; MANUFACTURE; NEURAL NETWORKS; ONLINE SYSTEMS; REAL TIME SYSTEMS; SCHEDULING; STOCHASTIC SYSTEMS;

COMPLEX MANUFACTURING SYSTEMS; DISPATCHING RULES; INTUITIVE UNDERSTANDING; LINEAR REPRESENTATION; NETWORK REPRESENTATION; REPRESENTATION; SEARCH NEIGHBORHOODS; TRIAL-AND-ERROR PROCESS;

JOB SHOP SCHEDULING;

DESIGN; NEURAL NETWORKS; SCHEDULING;

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; HOSPITAL MANAGEMENT; MARKOV CHAIN; SEARCH ENGINE; THEORETICAL MODEL;

ALGORITHMS; APPOINTMENTS AND SCHEDULES; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); SEARCH ENGINE; STOCHASTIC PROCESSES;

EID: 84931057226     PISSN: 10636560     EISSN: 15309304     Source Type: Journal    
DOI: 10.1162/EVCO_a_00131     Document Type: Article

References (46)

1. Auger, A., and Hansen, N. (2005). A restart CMA evolution strategy with increasing population size. In Proceedings of the IEEE Congress on Evolutionary Computation, Vol. 2, pp. 1769-1776.
2. Bartz-Beielstein, T., Lasarczyk, C., and Preuß, M. (2005). Sequential parameter optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Vol. 1, pp. 773-780.
3. Blackstone, J. H., Phillips, D. T., and Hogg, G. L. (1982). A state-of-the-art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research, 20(1): 27-45.
4. Burke, E. K., Gendreau, M., Hyde, M., Kendall, G., Ozcan, G., and Qu, R. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64(12): 1695-1724.
5. Burke, E.K., Hyde, M. R., Kendall, G., Ochoa, G., Ozcan, E., and Woodward, J. R. (2009). Exploring hyper-heuristic methodologies with genetic programming. In J. Kacprzyk, L. C. Jain, and C. L. Mumford (Eds.), Computational intelligence (pp. 177-201). Berlin: Springer-Verlag.
6. Conway, R. W. (1965). Priority dispatching and job lateness in a job shop. Journal of Industrial Engineering, 16(4): 228-237.
7. Dimopoulos, C., and Zalzala, A. M. S. (2001). Investigating the use of genetic programming for a classic one-machine scheduling problem. Advances in Engineering Software, 32(6): 489-498.
8. Eguchi, T., Oba, F., and Toyooka, S. (2008). A robust scheduling rule using a neural network in dynamically changing job-shop environments. International Journal of Manufacturing Technology and Management, 14(3-4): 266-288.
9. Fu, M. C. (2001). Simulation optimization. In Proceedings of the 2001 Winter Simulation Conference, pp. 53-61.
10. Geiger, C. D., and Uzsoy, R. (2008). Learning effective dispatching rules for batch processor scheduling. International Journal of Production Research, 46(6): 1431-1454.
11. Geiger, C. D., Uzsoy, R., and Aytug, H. (2006). Rapid modeling and discovery of priority dispatching rules: An autonomous learning approach. Journal of Scheduling, 9(1): 7-34.
12. Gomez, F., Schmidhuber, J., and Miikkulainen, R. (2008). Accelerated neural evolution through cooperatively coevolved synapses. Journal of Machine Learning Research, 9: 937-965.
13. Gustafson, S. (2004). An analysis of diversity in genetic programming. Ph.D. thesis, School of Computer Science and Information Technology, University of Nottingham, Nottingham, England.
14. Hansen, N., and Ostermeier, A. (2001). Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2): 159-195.
15. Hart, E., Ross, P., and Corne, D. (2005). Evolutionary scheduling: A review. Genetic Programming and Evolvable Machines, 6(2): 191-220.
16. Haupt, R. (1989). A survey of priority rule-based scheduling. OR Spektrum, 11(1): 3-16.
17. Hershauer, J. C., and Ebert, R. J. (1975). Search and simulation selection of a job-shop sequencing rule. Management Science, 21(7): 833-843.
18. Hildebrandt, T. (2012). Jasima; An efficient Java simulator for manufacturing and logistics. Retrieved from http://code.google.com/p/jasima/
19. Hildebrandt, T., Heger, J., and Scholz-Reiter, B. (2010). Towards improved dispatching rules for complex shop floor scenarios; A genetic programming approach. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 257-264.
20. Holthaus, O., and Rajendran, C. (1997). Efficient dispatching rules for scheduling in a job shop. International Journal of Production Economics, 48(1): 87-105.
21. Holthaus, O., and Rajendran, C. (2000). Efficient jobshop dispatching rules: Further developments. Production Planning and Control, 11(2): 171-178.
22. Hornik, K., Stinchcombe, M., and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5): 359-366.
23. Jackson, D. (2011). Promoting phenotypic diversity in genetic programming. In Parallel problem solving from nature. Lecture notes in computer science, Vol. 6239 (pp. 472-481). Berlin: Springer-Verlag.
24. Jain, A., and Meeran, S. (1998). A state-of-the-art review of job-shop scheduling techniques. Tech. rep., University of Dundee.
25. Jakobović, D., Jelenkovic, L., and Budin, L. (2007). Genetic programming heuristics for multiple machine scheduling. In Genetic programming. Lecture notes in computer science, Vol. 4445 (pp. 321-330). Berlin: Springer-Verlag.
26. Jones, A., Rabelo, L. C., and Sharawi, A. T. (1999). Survey of job shop scheduling techniques. In Wiley encyclopedia of electrical and electronics engineering. New York:Wiley.
27. Jones, T., and Forrest, S. (1995). Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In Proceedings of the International Conference on Genetic Algorithms, pp. 184-192.
28. Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press.
29. Langdon, W. B., and Poli, R. (2002). Foundations of genetic programming. Berlin: Springer-Verlag.
30. Law, A. M. (2007). Simulation modeling and analysis, 4th ed. New York: McGraw-Hill.
31. Liu, H., and Dong, J. J. (1996). Dispatching rule selection using artificial neural networks for dynamic planning and scheduling. Journal of Intelligent Manufacturing, 7(3): 243-250.
32. Miller, J., and Thomson, P. (2000). Cartesian genetic programming. In Genetic programming. Lecture notes in computer science, Vol. 1802 (pp. 121-132). Berlin: Springer-Verlag.
33. Miyashita, K. (2000). Job-shop scheduling with GP. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 505-512.
34. Mouelhi-Chibani, W., and Pierreval, H. (2010). Training a neural network to select dispatching rules in real time. Computers & Industrial Engineering, 58(2): 249-256.
35. Nie, L., Shao, X., Gao, L., and Li, W. (2010). Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems. The International Journal of Advanced Manufacturing Technology, 50(58): 729-747.
36. O’Neill, M., Vanneschi, L., Gustafson, S., and Banzhaf, W. (2010). Open issues in genetic programming. Genetic Programming and Evolvable Machines, 11(3-4): 339-363.
37. Pickardt, C., Branke, J., Hildebrandt, T., Heger, J., and Scholz-Reiter, B. (2010). Generating dispatching rules for semiconductor manufacturing to minimize weighted tardiness. In Proceedings of the Winter Simulation Conference, pp. 2504-2515.
38. Pinedo, M. L. (2008). Scheduling: Theory, algorithms, and systems, 3rd ed. Berlin: Springer-Verlag.
39. Poli, R., Langdon, W. B., and McPhee, N. F. (2008). A field guide to genetic programming. UK: Lulu Enterprises.
40. Raidl, G. R., and Gottlieb, J. R. (2005). Empirical analysis of locality, heritability and heuristic bias in evolutionary algorithms: A case study for the multidimensional knapsack problem. Evolutionary Computation, 13(4): 441-475.
41. Rajendran, C., and Holthaus, O. (1999). A comparative study of dispatching rules in dynamic flowshops and jobshops. European Journal of Operational Research, 116(1): 156-170.
42. Rothlauf, F. (2003). On the locality of representations. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1608-1609.
43. Rothlauf, F. (2006). Representations for genetic and evolutionary algorithms. Studies in Fuzziness and Soft Computing, Vol. 104. Berlin: Springer-Verlag.
44. Rothlauf, F., and Goldberg, D. E. (2003). Redundant representations in evolutionary computation. Evolutionary Computation, 11(4): 381-415.
45. Suresh, V., and Chaudhuri, D. (1993). Dynamic scheduling—A survey of research. International Journal of Production Economics, 32(1), 53-63.
46. Tay, J. C., and Ho, N. B. (2008). Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering, 54(3): 453-473.