Abstract
This paper considers the following methods of the work scheduling: network planning techniques (critical path method, program evaluation and review technique, and graphical evaluation and review technique), method of agents cooperation in the needs-and-means networks proposed by Skobelev P.O., method of simulation and genetic algorithms integration proposed by Kureichik V.V., and method of multiagent genetic optimization developed by the authors based on the Kureichik method. As a result of the comparative analysis, the advantages of the method of multiagent genetic optimization in terms of solving the problem of subcontracting scheduling have been revealed. The multiagent genetic optimization method takes into account the nonrenewable resources, allows implementing different resource allocation strategies using simulation and multiagent modeling, and allows optimizing subcontract resources via analysis of alternative work schedules using genetic algorithms and simulation.
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Aksyonov, K. and Antonova, A.: Multiagent genetic optimisation to solve the project scheduling problem under uncertainty. International Journal on Advances in Software, 7(1&2) (2014) 1–19
Aksyonov, K., Bykov, E., Aksyonova, O., and Antonova, A.: Development of real-time simulation models: integration with enterprise information systems. In: Proceedings of the Ninth International Multi-Conference on Computing in the Global Information Technology (2014) 45–50
Borodin, A., Kiselev, Y., Mirvoda, S., and Porshnev, S.: On design of domain-specific query language for the metallurgical industry. In: Proceedings of 11th International Conference BDAS: Beyond Databases, Architectures and Structures: Communications in Computer and Information Science (2015) 505–515
Goffe, V., Ferrier, G., and Rogers, J.: Global optimization of statistical functions with simulated annealing. Journal of Econometrics 60 (1994) 65–99
Goldberg, D.: Genetic algorithms. Addison Wesley, (1989)
Kureichik, V.M., Malioukov, S., Kureichik, V.V., and Malioukov, A.: Genetic Algorithms for Applied CAD Problems. Springer (2009)
Lehman, J. and Stanley, K.: Exploiting open-endedness to solve problems through the search for novelty. In: Proceedings of the Eleventh International Conference Artificial Life (ALIFE XI) (2008) 329–336
Moder, J. and Elmaghraby, S. (Eds.): Handbook of operations research: foundations and fundamentals, Vol. 1. New York: Van Nostrand-Reinhold, 2nd. ed., (1978)
Moder, J. and Elmaghraby, S. (Eds.): Handbook of operations research: models and applications, Vol. 2. New York: Van Nostrand-Reinhold, 2nd. ed., (1978)
Pritsker, A. and Happ, W.: GERT: graphical evaluation and review technique: Part I, Fundamentals. Journal of Industrial Engineering, 17(6), (1966) 267–274
Rzevski, G., Himoff, J., and Skobelev, P.: MAGENTA technology: a family of multi-agent intelligent schedulers. In: Proceedings of International conference on multi-agent systems: Workshop on Software Agents in Information Systems and Industrial Applications 2 (SAISIA), Germany: Fraunhofer IITB (2006)
Vittikh, V. and Skobelev, P.: Multiagent interaction models for constructing the needs-and-means networks in open systems. Automation and Remote Control, 64, (2003) 162–169
Wooldridge, M.: Intelligent Agent: Theory and Practice. Knowledge Engineering Review, Vol. 10 (2), (1995)
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This work is supported by Act 211 Government of the Russian Federation, contract No 02.A03.21.0006.
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Aksyonov, K., Antonova, A., Sysoletin, E. (2018). Comparative Analysis of Subcontracting Scheduling Methods. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-7871-2_42
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DOI: https://doi.org/10.1007/978-981-10-7871-2_42
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