Pareto Optimality of Production Schedules in the Stage of Populations Selection of the MOIA Immune Algorithm

Abstract:

In the paper, the problem of achieving Pareto optimal solutions set with application of the elaborated Multi Objectives Immune Algorithm is presented. The Pareto frontier provides a variety of compromise solutions for contradictive criteria to a decision maker. We propose the application of the selection based on the Pareto optimality to maintain solutions with great diversity in an immune memory. Stimulation and suppression mechanisms are used to control the diversity of generated solutions. Computer simulations are done for a job shop scheduling problem.

• Periodical: Applied Mechanics and Materials (Volume 657)
• Pages: 869-873
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.657.869
• Online since: October 2014
• Authors: Iwona Paprocka*, Krzysztof Kalinowski
• Keywords: Immune Algorithm, Pareto Optimality, Schedules Evaluation, Scheduling

References

[1] H. Ishibuchi, T. Murata: A Multi–Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling, IEEE Transactions on Systems, Man, and Cybernetics- Part C: Applications and Reviews. 28, No. 3 (1998) 392-403.

DOI: 10.1109/5326.704576

[2] Yiu-Wing Leung, Yuping Wang: Multiobjective Programming Using Uniform Design and Genetic Algorithm, IEEE Transactions on Systems, Man, and Cybernetics- Part C: Applications and Reviews. 30, No. 3 (2000) 293-304.

DOI: 10.1109/5326.885111

[3] D. Lei: Pareto archive particle swarm optimization for multi-objective fuzzy job shop scheduling problems. Int. J. Adv. Manuf. Technol. 37 (2008) 157-165.

DOI: 10.1007/s00170-007-0945-8

[4] M. J. Geiger: On the distribution of Pareto optimal solutions in alternative space - the investigation on multi objective permutation flow shop scheduling problems. Technological and economic development of economy. 12, No. 1 (2006) 23-29.

DOI: 10.3846/13928619.2006.9637718

[5] R. Tavakkoli-Moghaddam, A.R. Rahimi-Vahed, A.H. Mirzaei: Solving a multi-objective no-wait flow shop scheduling problem with an immune algorithm. Int. J. Adv. Manuf. Technol. 36 (2008) 969-981.

DOI: 10.1007/s00170-006-0906-7

[6] X. Wang, L. Gao, Ch. Zhang, X. Shao: A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. Int. J. Adv. Manuf. Technol. 51 (2010) 757-767.

DOI: 10.1007/s00170-010-2642-2

[7] Kempa W, Paprocka I., Kalinowski K., Grabowik C.: Estimation of reliability characteristics in a production scheduling model with failures and time-changing parameters described by Gamma and exponential distributions. Advanced Materials Research. 837 (2014).

DOI: 10.4028/www.scientific.net/amr.837.116

[8] Kalinowski K, Grabowik C., Kempa W., Paprocka I.: The graph representation of multivariant and complex processes for production scheduling. Advanced Mater. Research. 837 (2014), 422-427.

DOI: 10.4028/www.scientific.net/amr.837.422