Two-Level Multi-criteria Model for Calculating Multinomenclature Spare Parts of an Auto Service Enterprise Based on the Rougher Algorithm for Optimizing the Behavior of Their Particles
Volume 5, Issue 4, August 2017, Pages: 57-64
Received: Aug. 29, 2017;
Accepted: Sep. 18, 2017;
Published: Nov. 8, 2017
Views 1935 Downloads 88
Karimov Nijat Ashraf, Department of Automotive Engineering, Azerbaijan Technical University, Baku, Azerbaijan
Dyshin Oleq Aleksandr, Department of Applied Mechanics, Azerbaijan State University of Oil and Industry, Baku, Azerbaijan
Gozalov Sulhaddin Kamal, Department of Automotive Engineering, Azerbaijan Technical University, Baku, Azerbaijan
On the example of the two-criterion problem with the objective functions of the maximum, the confidence probabilities of the demand and the minimum of the total costs show the applicability of the method of Vector Optimization of Particle Swarm Optimization (VEPSO). Compared with genetic algorithms and other methods of evolutionary modeling, this method is easy to implement and has high efficiency, as well as the accelerated cost of an approximate solution of the problem from the external archive of the no dominant best solutions to the Pareto front, which is the boundary of the Pareto-optimal Compromise) solutions.
Karimov Nijat Ashraf,
Dyshin Oleq Aleksandr,
Gozalov Sulhaddin Kamal,
Two-Level Multi-criteria Model for Calculating Multinomenclature Spare Parts of an Auto Service Enterprise Based on the Rougher Algorithm for Optimizing the Behavior of Their Particles, Science Research.
Vol. 5, No. 4,
2017, pp. 57-64.
Doc. Palmer Maintenance planning and scheduling handbook. New York: Mc Grow Hill Press, 2006.
Duy Quang Nquyen, Bagajewicz Miguel. Optimization of preventive maintenance scheduling in processing plants. // Computer Aided Chemical Engineering, 2008; (25): 319-324.
Ilgin M. Ali, Tenali Semra. Paint optimization of spare parts inventory and maintenance policies using genetic algorithms // International Journal of Advanced Manufacturing Technology 2007; 30 (5): 594-604.
Eberhart R. C. and Kennedy J. A. New Optimizer Using Particle Swarm Theory // Proceedings Sixth Symposium on Micro Machine and Human Science, pp. 39-43. IEEE Service Center, Piscataway, N7.
Arcley D. H. A connectionist algorithm for genetic search // Proceedings of an Int. Conf. on Genetic Algorithms and Their Application. 1985, pp. 121-135.
Goldberg D. E. Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addision-Wesley, 1989.
Gen M., Cheng R. Genetic Algorithms and Engineering Optimization. John Wiley and Sons, Inc. New-York, Chichester, Weinheim, Brisbane, Singapore, Toronto, 2000.
Lazunov LA, Kureichik VM, Kureichik VV Genetic algorithms. - Rostov-on-Don: OOO, Rostizdat, 2004.
Baabeau E., Dorigo M. and Theraulaz G. Swarm Intelligence: From National to Artificial Systems, New York, NY: Oxford University Press, Santa Fe Institute, Studies in the Science of Complexity, 1999.
Dorigo M. and Stutzle T. Ant Colony Optimization, MIT Press, 2004.
Cordon, “Herera and Stutzle” A Review on the Ant Colony Optimization Metcheuristic: Basis, Models and New Trends Mathware Soft Computing 9 (2002).
12. Shtovba S. D. Ant algorithms // Exponenta Pro. Mathematics in applications, 2003, No. 4, p. 70-75.
Deb K. Genetic algorithms in multimodal function approximation. Master’s thesis, University of Alabama, 1989.
Parsopoules K. E., Vrahatis M. N. Recent approaches to global optimization problems through particle swarm optimization // Natural computing, 2002; 1 (2-3): 235-306.
Abido M. A. Multi-objective particle swarm optimization for optimal power flow problem // 12 th International Middle East Power System Conference, 2008: 392-396.
Baltar Alexandre M., Fontane Darell G. Use of multi-objective particle swarm optimization in water resources management// Journal of Water Resources Planning and Management, 2008: 257-265.
Leong, Wen-Fung Yen, Garg G. Dynamic swarms in PSO-based multi-objective optimization // IEEE Congress on Evolutionary Computation, 2007: 3172-3179.
Andries P. Engelbrecht write, Ying Tan translate. Basic of computer swarm intelligence. Beijing: Qinghua Universitety Press, 2009.
Dunwei Geng, Youn Zhang, Jianhua Zhang. Multi-objective particle swarm optimization based on minimal particle angle // Lecture Notes in Compute Science, 2005, 3644(1): 571-576.
Yabin Wana, Jianmin Zhao, Xisheng Jia, Yan Tian. Spare parts allocation optimization in a multi-echelon support systems based on multi-objective particle swarm optimization method // Eksploataciya i Niezawodnose - Maintenance and Reliability, 2014; 16(1): 29-36.
Karimov N. A. The calculation of the spare parts in the auto-service enterprise on the base of actual demand // Engineering Science 78-84.
Dyshin O. A., Karimov N. A. Identification of the actual distribution of demand for spare parts in car maintenance service stations // American Journal of Traffic and Transportation Engineering 26-31.
Parsopdous R. E. and Vrahatis M. N. Particle swarm optimization method in multi-objective problems // Proceedings of the ACM 2002 Symposium on Applied Computing (pp. 603-607) ACM Press.
Schaffer J. D. Multiple objective optimization with vector evaluated genetic algorithm // In Proceedings of the Ist International Conference on Genetic Algorithm (pp. 93-100). Morgan Raufmann Publishers, 1985.
Sedighizadeh D., Masehian E. Particle System Optimization Methods, Taxonomy and Applications // International Journal of Computer Theory and Engineering, 2000, vol. 1, N5, 486-502/.
Reyes-Sierra M. and Coello C. A. Multi-Objective Particle Swarm Optimizars: A Survey of the State-of0the-Art// International Journal of Computational Intelligence Research, 2006, vol. 2. N3, pp. 287-307.
Zitzler E., Deb K. and Thiele L. Comparison of multi-objective evolution algorithms empirical results// Evolutionary Computation, 2000. 8(2): 173-195.
Jin Y., Olhofer M. and Senfhoff B. Evolutionary dynamic weighted aggregation for multi-objective optimization: Why does it work and how? // Proceedings of the GECCO 2001 Conference, San Francisko, CA, pp. 1042-1049.