Authors L. A. Gladkov, N. V. Gladkova, S. A. Gromov
Month, Year 09, 2017 @en
Index UDC 658.512.2.011.5
Abstract The article considers a new approach to solving the problems of operational planning of the production process. The definition of the task of operational planning of production is given. The place of the tasks of operational planning in the general theory of schedules is shown. The formulation of the task of operational planning for machine-building enterprises is given, specifics of the solution of such tasks at machine-building enterprises are noted, constraints and the objective function of the optimization problem are given. The structure of the proposed hybrid algorithm for operational planning of the production process is described. A method for coding solutions for using them in a hybrid algorithm is developed. The chromosome structure is described, which is a set of tasks for the planning period or the operational plan as a whole. New modifications of genetic operators aimed at increasing the diversity of the current population and overcoming local optima are proposed. Examples of implementation of modified genetic operators that take into account the specifics of solving operational planning problems are given. The proposed methodology and the sequence of calculation of the value of the criterion for assessing the quality of the solutions obtained are described. Additional restrictions were introduced on the area of feasible solutions to the problem and a methodology for calculating fines for violating established restrictions was proposed. The sequence of operations and the structure of the developed algorithm are given. A hybrid model of the algorithm based on integration of methods of genetic search and principles of fuzzy control is proposed. The characteristics of the developed algorithm are studied and its time complexity is determined. A series of computational experiments was conducted to analyze and compare the quality of the solutions obtained, as well as to determine the best values of the control parameters of the algorithm. Based on the analysis, conclusions are drawn about the merits and demerits of the proposed algorithm.

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Keywords Operational production planning problems; genetic algorithm; fuzzy logic; scheduling theory; optimization; hybrid algorithm.
References 1. Lazarev A.A., Gafarov E.R. Teoriya raspisaniy. Zadachi i algoritmy [The scheduling theory. Problems and algorithms]. Moscow: Izd-vo MGU, 2011, 224 p.
2. Conway R.M., Maxwell W.L., Miller L.W. Theory of Scheduling. 2nd ed. Dover Publications, Mineola NY, 2004.
3. Vysochin S.V. Printsipy postroeniya sistem dlya rascheta proizvodstvennykh raspisaniy [Principles of building systems to calculate production schedules], SAPR i grafika [CAD and graphics], 2008, No. 9, pp. 57-59.
4. Pinedo M. Scheduling: Theory, Algorithms and Systems. 3nd ed. Springer Verlag, New York, 2008.
5. Leung J.Y.T. Handbook of Scheduling, Boca Raton, Florida: Chapman & Hall/CRC, 2004.
6. Tanaev V.S. Vvedenie v teoriyu raspisaniy [Introduction to the theory of schedules]. Moscow: Nauka, 1975, 256 p.
7. Michael A., Takagi H. Dynamic control of genetic algorithms using fuzzy logic techniques, Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann, 1993, pp. 76-83.
8. Lee M.A., Takagi H. Integrating design stages of fuzzy systems using genetic algorithms, Proceedings of the 2nd IEEE International Conference on Fuzzy System, 1993, pp. 612-617
9. Herrera F., Lozano M. Fuzzy Adaptive Genetic Algorithms: design, taxonomy, and future directions, J. Soft Computing. Springer-Verlag, 2003, pp. 545-562
10. Kennedy J., Eberhart R.C. Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, 1995, pp. 1942-1948.
11. Hayder M., Tony H., Naz E.I., Hybrid Algorithm for the Optimization of Training Convolutional Neural Network, International Journal of Advanced Computer Science and Applications, 2015, Vol. 6, No. 10, pp. 79-85.
12. Gladkov L.A., Kureychik V.V., Kureychik V.M. Geneticheskie algoritmy [Genetic algorithms]. Moscow: Fizmatlit, 2010.
13. Gladkov L.A., Kureychik V.V., Kureychik V.M., Sorokoletov P.V. Bioinspirirovannye metody v optimizatsii [Bioinspired methods in optimization]. Moscow: Fizmatlit, 2009, 384 p.
14. Emel'yanov V.V., Kureychik V.V., Kureychik V.M. Teoriya i praktika evolyutsionnogo modelirovaniya [Theory and practice of evolutionary modeling]. Moscow: Fizmatlit, 2003.
15. Knysh D.S., Kureychik V.M. Parallel'nye geneticheskie algoritmy: Problemy, obzor i sostoyanie [Parallel genetic algorithms: Problem overview and state], Izvestiya RAN. Teoriya i sistemy upravleniya [Journal of Computer and Systems Sciences International], 2010, No. 4, pp. 72-82.
16. Gladkov L.A., Gladkova N.V. Osobennosti ispol'zovaniya nechetkikh geneticheskikh algoritmov dlya resheniya zadach optimizatsii i upravleniya [Features of use of fuzzy genetic algorithms for the decision of problems of optimisation and control], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2009, No. 4 (93), pp. 130-136.
17. King R.T.F.A., Radha B., Rughooputh H.C.S. A fuzzy logic controlled genetic algorithm for optimal electrical distribution network reconfiguration, Proceedings of 2004 IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan, 2004, pp. 577-582
18. Zhongyang X., Zhang Y., Zhang L., Niu S. A parallel classification algorithm based on hybrid genetic algorithm, Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, 2006, pp. 3237-3240
19. Gladkov L., Gladkova N., Leiba S. Manufacturing scheduling problem based on fuzzy genetic algorithm, In: Proceeding of IEEE East-West Design & Test Symposium – (EWDTS’2014). Kiev, Ukraine, 2014, pp. 209-212.
20. Gladkov L.A., Gladkova N.V., Leiba S.N. Manufacturing Scheduling Problem Based on Fuzzy Genetic Algorithm, Proceedings of IEEE East-West Design & Test Symposium (EWDTS’2014). Kiev, Ukraine, September 26–29, 2014, pp. 209-213.

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