Deterministic helper-objective sequences applied to job-shop scheduling

Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10

Volumn , Issue , 2010, Pages 431-438

  Lochtefeld, Darrell F ^a   Ciarallo, Frank W ^b  

^a AIR FORCE RESEARCH LABORATORY   (United States)

^b WRIGHT STATE UNIVERSITY   (United States)

Author keywords

Epistasis; Evolutionary algorithms (ea); Genetic algorithms (ga); Helper Objective; Job shop scheduling problem (jssp); Multi objectivization; Multiple objective evolutionary algorithms (moea); Non dominated sorting genetic algorithm version ii (nsgaii)

Indexed keywords

EPISTASIS; HELPER-OBJECTIVE; JOB SHOP SCHEDULING PROBLEMS; MULTI-OBJECTIVIZATION; MULTIPLE OBJECTIVES; NON-DOMINATED SORTING GENETIC ALGORITHMS;

MULTIOBJECTIVE OPTIMIZATION;

GENETIC ALGORITHMS;

EID: 77955866878     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1830483.1830566     Document Type: Conference Paper

References (28)

1. Baker, K. R. Introduction to Sequencing and Scheduling. John Wiley & Sons, New York, NY, 1974.
2. Bierwirth, C. A generalized permutation approach to job shop scheduling with, genetic algorithms. OR Spectrum, 17(2): 87-92, 1995.
3. Brockhoff, D., Friedrich, T., Hebbinghaus, N., Klein, C., Neumann, F. andZitzler, E. Do additional objectives make a problem harder? GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation., 765772, 2007.
4. Cheng, R., Gen, M. andTsujimura, Y. A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: hybrid genetic search strategies. Comput. Ind. Eng., 36(2): 343-364, 1999.
5. Coello Coello, C. A., Lamont, G. B. andVan Veldhuisen, D. A. Evolutionary algorithms for solving multi-objective problems. Springer, New York, 2007.
6. Corne, D. W., Jerram, N. R., Knowles, J. D. andOates, M. J. PESA-II: Region-based selection in evolutionary multiobjective optimization. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), 283-290, 2001.
7. Davidor, Y. Epistasis variance: A viewpoint on GA- hardness. Foundations of genetic algorithms, 1: 23-35, 1991.
8. Deb, K., Pratap, A., Agarwal, S. andMeyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2): 182-197, 2002.
9. Garey, M. R., Johnson, D. S. andSethi, R. The Complexity of Flowshop and Jobshop Scheduling. Mathematics of Operations Research, 1(2): 117-129, May 1976.
10. Giffler, B. andThompson, G. Algorithms for solving production-scheduling problems. Oper. Res., 487-503, 1960.
11. Goldberg, D. E. Genetic algorithms and Walsh functions: Part I, a gentle introduction. Complex Systems, 3(2): 129-152, 1989.
12. Grefenstette, J. J. Deception considered harmful. Foundations of Genetic Algorithms - 2. Morgan Kaufmann, San Francisco, 75-91, 1993.
13. Greiner, D., Emperador, J. M., Winter, G. andGalván, B. Improving Computational Mechanics Optimum Design Using Helper Objectives: An Application in Frame Bar Structures. Springer, 575-589, 2007.
14. Handle, J., Lovell, S. C., Knowles, J., Multiobjectivization by Decomposition of Scalar Cost Functions. PPSNX. Springer-Verlag, Berlin Heidelberg, 2008
15. Hopp, W. J. andSpearman, M. L. Factory Physics. Irwin/McGraw-Hill, Boston, MA, 2001.
16. J. E. Beasley's OR Library. http://people.brunel.ac.uk/~mastjjb/jeb/info. html.Accessed Nov. 11, 2009.
17. Jahne, M., Li, X. andBranke, J. Evolutionary algorithms and multi-objectivization for the travelling salesman problem. GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation., 595-602, 2009.
18. Jensen, M. T. Helper-objectives: Using multi-objective evolutionary algorithms for single-objective optimisation. Journal of Mathematical Modelling and Algorithms, 3(4), 323-347, 2004.
19. Jensen, M. T. Guiding Single-Objective Optimization Using Multi-objective Methods. Applications of Evolutionary Computing. Springer, 91-98, 2003.
20. Knowles, J. andCorne, D. The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. Congress on Evolutionary Computation (CEC99)., 98-105, 1999.
21. Knowles, J. D., Watson, R. A. andCome, D. W. Reducing local optima in single-objective problems by multiobjectivization. Lecture Notes in Computer Science, 269-283, 2001.
22. Lawler, E. L. andWood, D. E. Branch-And-Bound Methods: A Survey. Oper. Res., 14(4): 699-719, Jul. - Aug. 1966.
23. Lawrence, S. Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement). (1984).
24. Neumann, F. andWegener, I. Can Single-Objective Optimization Profit from Multiobjective Optimization? Multiobjective Problem Solving from Nature: From Concepts to Applications. Springer, 115-130, 2007.
25. Storer, R. H., Wu, S. D. andVaccari, R. New search spaces for sequencing problems with application to job shop scheduling. Management Science, 38(10): 1495-1509, 1992.
26. Vaessens, R. J. M., Aarts, E. H. L. andLenstra, J. K. Job shop scheduling by local search. INFORMS Journal on Computing, 1996.
27. Wright, M. Subcost-guided search-experiments with timetabling problems. J. Heuristics, 7(3): 251-260, 2001.
28. Zitzler, E., Laumanns, M. andThiele, L. SPEA2: Improving the strength Pareto evolutionary algorithm. EUROGEN., 95-100, 2001.