Abstract
It is proposed to use ant algorithms together with the object-oriented simulation models. To optimize the functioning of the automated technological complex machining together with a modified ant algorithm it is designed object model, which allows to calculate the fitness function and evaluate potential solutions. The transition and calculation of the concentration for synthetic pheromone rules are determined for supposed directed ant algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Skobtsov, Y., Sekirin, A., Zemlyanskaya, S., Chengar, O., Skobtsov, V., Potryasaev, S.: Application of object-oriented simulation in evolutionary algorithms. In: Silhavy, R., Senkerik, R., Oplatkova, Z.K., Silhavy, P., Prokopova, Z. (eds.) Automation Control Theory Perspectives in Intelligent Systems. AISC, vol. 466, pp. 453–462. Springer, Cham (2016). doi:10.1007/978-3-319-33389-2_43
Chengar, O.V.: Development of the “directed” ant algorithm to optimize production schedules. Bull. Kherson Nat. Tech. Univ. 1(46), 212–217 (2013). (in Russian). ISBN 5-7763-2514-5-Kherson
Chengar, O.V.: Graph analytical model of flexible manufacturing modules download automated machine-building enterprise. J. East Ukrainian Nat. Univ. 13(167), 239–245 (2011). Chenhar, O.V., Savkova, E.O., Lugansk
Skobtsov, Y.A., Speransky, D.V.: Evolutionary Computation: Hand Book. The National Open University “INTUIT”, Moscow, 331 p. (2015). (in Russian)
Dorigo, M.: Swarm intelligence, ant algorithms and ant colony optimization. Reader for CEU Summer University Course «Complex System», pp. 1–34. Central European University, Budapest (2001)
Acknowledgments
The research described in this paper is partially supported by the Russian Foundation for Basic Research (grants 15-07-08391, 15-08-08459, 16-07-00779, 16-08-00510, 16-08-01277, 16-29-09482-ofi-i, 17-08-00797, 17-06-00108, 17-01-00139, 17-20-01214), grant 074-U01 (ITMO University), project 6.1.1 (Peter the Great St.Petersburg Polytechnic University) supported by Government of Russian Federation, Program STC of Union State “Monitoring-SG” (project 1.4.1-1, project 6MCГ/13-224-2), state order of the Ministry of Education and Science of the Russian Federation № 2.3135.2017/K, state research 0073–2014–0009, 0073–2015–0007, International project ERASMUS +, Capacity building in higher education, № 73751-EPP-1-2016-1-DE-EPPKA2-CBHE-JP, Innovative teaching and learning strategies in open modelling and simulation environment for student-centered engineering education.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Skobtsov, Y., Chengar, O., Skobtsov, V., Pavlov, A.N. (2017). Synthesis Production Schedules Based on Ant Colony Optimization Method. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-57261-1_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57260-4
Online ISBN: 978-3-319-57261-1
eBook Packages: EngineeringEngineering (R0)