Multi-objective scheduling problems: Determination of pruned Pareto sets

IIE Transactions (Institute of Industrial Engineers)

Volumn 40, Issue 5, 2008, Pages 552-564

  Taboada, Heidi A ^a   Coit, David W ^b  

^a UNIVERSITY OF TEXAS AT EL PASO   (United States)

^b DEPARTMENT OF INDUSTRIAL AND SYSTEMS ENGINEERING   (United States)

Author keywords

Cluster analysis; K means algorithm; Multi objective optimization; Pareto optimal set

Indexed keywords

CLUSTER ANALYSIS; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; PLANNING; PRODUCTION; SCHEDULING;

DATA CLUSTERING; K-MEANS ALGORITHM; PARETO OPTIMAL SET;

PARETO PRINCIPLE;

EID: 40749163214     PISSN: 0740817X     EISSN: 15458830     Source Type: Journal    
DOI: 10.1080/07408170701781951     Document Type: Article

References (35)

1. Arroyo, J. E. C. and Armentano, V. A. (2005) Genetic local search for multi-objective flowshop scheduling problems. European Journal of Operational Research, 167, pp. 717-738.
2. Baheranwala, F. (2005) Solution of stochastic multi-objective combinatorial problems, MS thesis. Rutgers University, Piscataway, NJ 08854, USA
3. Branke, J., Deb, K., Dierolf, H. and Osswald, M. (2004) Finding knees in multi-objective optimization. Proceedings of the Eighth Conference on Parallel Problem Solving from Nature (PPSN VIII) Springer, pp. 722-731. Berlin
4. Chankong, V. and Haimes, Y. (1983) Multiobjective Decision Making Theory and Methodology, North-Holland, New York, NY
5. Coit, D. W. and Baheranwala, F. (2005) Solution of stochastic multi-objective system reliability design problems using genetic algorithms. Proceedings of the European Safety and Reliability Conference (ESREL) Taylor and Francis, pp. 391-398. London
6. Das, I. (1999) On characterizing the 'knee' of the Pareto curve based on normal-boundary intersection. Structural Optimization, 18:23, pp. 107-115.
7. Deb, K., Pratap, A., Agrawal, S. and Meyarivan, T. (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6:2, pp. 182-197.
8. Erickson, M., Mayer, A. and Horn, J. (2001) The niched Pareto genetic algorithm 2 applied to the design of groundwater remediation systems. Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization Springer-Verlag, pp. 681-695.
9. Fonseca, C. M. and Fleming, P. J. (1993) Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. Proceedings of the Fifth International Conference on Genetic Algorithms Morgan Kauffman Publishers, pp. 416-423.
10. Hans, A. E. (1988) Multicriteria optimization for highly accurate systems. Multicriteria Optimization in Engineering and Sciences, pp. 309-352.
11. Hoogeveen, H. (2005) Multicriteria scheduling. European Journal of Operational Research, 167, pp. 592-623.
12. Horn, J., Nafpliotis, N. and Goldberg, D. E. (1994) A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation, Vol. IEEE Piscataway, NJpp. 82-87.
13. Karp, R. M. (1972) Reducibility among combinatorial problems. Complexity of Computer Computations, pp. 85-103. Plenum Press, New York, NY
14. Kaufman, L. and Rousseeuw, P. J. (1990) Finding Groups in Data. An Introduction to Cluster Analysis, Wiley, New York
15. Korhonen, P. and Halme, M. (1990) Supporting the decision maker to find the most preferred solutions for a MOLP-problem. Proceedings of the 9th International Conference on Multiple Criteria Decision Making, pp. 173-183.
16. Kulturel-Konak, S., Coit, D. and Baheranwala, F. (2008) Pruned Pareto-optimal sets for the system redundancy allocation problem based on multiple prioritized objectives. Journal of Heuristics
17. Lahdelma, R., Hokkanen, J. and Salminen, P. (1998) SMAA-stochastic multiobjective acceptability analysis. European Journal of Operational Research, 106:1, pp. 137-143.
18. Queen, J. Mac (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297.
19. Murata, T., Ishibuchi, H. and Tanaka, H. (1996) Multi-objective genetic algorithm and its applications to flowshop scheduling. Computers in Industrial Engineering, 30:4, pp. 957-968.
20. Ponnambalam, S. G., Ramkumar, V. and Jawahar, N. (2001) A multiobjective genetic algorithm for job shop scheduling. Production Planning and Control, 12:8, pp. 764-774.
21. Rao, S. S. (1991) Optimization Theory and Application, Wiley Eastern Limited, New Delhi, India
22. Rietveld, P. and Ouwersloot, H. (1992) Ordinal data in multicriteria decision making, a stochastic dominance approach to siting nuclear power plants. European Journal of Operational Research, 56, pp. 249-262.
23. Rousseeuw, P. J. (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, pp. 53-65.
24. Rousseeuw, P., Trauwaert, E. and Kaufman, L. (1989) Some silhouette-based graphics for clustering interpretation. Belgian Journal of Operations Research, Statistics and Computer Science, 29:3, pp. 35-55.
25. Schaffer, J. D. (1985) Multiple objective optimization with vector evaluated genetic algorithms. Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms Lawrence Erlbaum Associates, pp. 93-100. Hillsdale, NJ
26. Srinivas, N. and Deb, K. (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Journal of Evolutionary Computation, 2:3, pp. 221-248.
27. Taboada, H. (2007) Practical approaches to the solution of multiobjective optimization problems considering objective preferences and solution clusters, Ph D thesis. Rutgers University, Piscataway, NJ 088954, USA
28. Taboada, H., Baheranwala, F., Coit, D. and Wattanapongsakorn, N. (2005) Practical solutions of multi-objective system reliability design problems using genetic algorithms. Proceedings of the Fourth International Conference on Quality and Reliability (ICQR4), pp. 723-730.
29. Taboada, H., Baheranwala, F., Coit, D. and Wattanapongsakorn, N. (2007) Practical solutions for multi-objective optimization: An application to system reliability design problems. Reliability Engineering & System Safety, 92:3, pp. 314-322.
30. Taboada, H. and Coit, D. (2007) Data clustering of solutions for multiple objective system reliability optimization problems. Quality Technology & Quantitative Management Journal, 4:2, pp. 35-54.
31. T'kindt, V. and Billaut, J. -C. (2002) Multicriteria Scheduling: Theory, Models and Algorithms, Springer, Berlin, Germany
32. Venkat, V., Jacobson, S. and Stori, J. (2004) A post-optimality analysis algorithm for multi-objective optimization. Computational Optimization and Applications, 28, pp. 357-372.
33. Yu, L., Shih, H. M., Pfund, M., Carlyle, W. M. and Fowler, J. W. (2002) Scheduling of unrelated parallel machines: An application to PWB manufacturing. IIE Transactions, 34, pp. 921-931.
34. Zeleny, M. (1982) Multiple Criteria Decision Making, Mc Graw-Hill, New York
35. Zitzler, E. and Thiele, L. (1999) Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3:4, pp. 257-271.