Abstract
The article considers the territory monitoring process by intelligent vehicles. The aim is to create an efficient algorithm for the iterative solution of Vehicle Routing Problem in view of danger situation on the monitoring objects as well as on the route between the objects. We introduce a group control strategy for a fleet of vehicles performing monitoring. Vehicles are considered as intelligent agents. Vehicles make optimal decisions on the route adjustment by means of negotiations on the principle of «an auction» when there are problems on the route. The route is built with the use of an ant algorithm taking into account the situation at the previous stages of the monitoring. The main advantages are that the strategy of intelligent control objects takes into account the danger of the situation on the object and a danger on the route between the objects.
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References
Rego, C., Gamboa, D., Glover, F., Osterman, C.: Traveling salesman problem heuristics: leading methods, implementations and latest advances. Eur. J. Oper. Res. 211(3), 427–441 (2011)
Woeginger, G.J.: Exact algorithms for NP-hard problems: a survey. In: Combinatorial Optimization, Lecture notes in Computer Science, vol. 2570, pp. 185–207 Springer (2003)
The Vehicle Routing Problem: Latest Advances and New Challenges.: In: Golden, B.L., Raghavan, S., Wasil, E.A. (eds.) (2008)
Allan, L.: The Dynamic Vehicle Routing Problem. LYNGBY Printed by IMM, DTU Bookbinder Hans Meyer (2001)
Frank, T.:Applying monte carlo techniques to the capacitated vehicle routing problem. Master thesis, Computer Science Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands (2010)
Jingquan, Li : Models and algorithms of real-time vehicle rescheduling problems Under Schedule Disruptions. Ph.D. thesis, The Graduate College of the University of Arizona (2006)
Dorigo, M., Gambardella, L.M.: Ant colonies for the traveling salesman problem. Bio. Syst. 43, 73–81 (1997)
Ren, F.: Autonomous agent negotiations strategies in complex environment. Ph.D. thesis, School of Computer Science and Software Engineering, Faculty of Engineering, University of Wollongong (2010)
Miaomiao, D., Hua, Yi.: Research on multi-objective emergency logistics vehicle routing problem under constraint conditions. J. Ind. Eng. Manage. JIEM. 6(1), 258–266 (2013)
Tan, K.C., Cheong, C.Y., Goh, C.K.: Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation. Eur. J. Oper. Res. 177, 813–839 (2007)
Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations Hardcover. Cambridge University Press (2008)
Multiagent Systems: A modern approach to distributed artificial intelligence. In: Gerhard, W. (ed.) MIT Press (2000)
Acknowledgments
The reported study was partially supported by RFBR, research project No. 13-08-00721-a.
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Abrosimov, V. (2015). Group Control Strategy for a Fleet of Intelligent Vehicles-Agent Performing Monitoring. In: Jezic, G., Howlett, R., Jain, L. (eds) Agent and Multi-Agent Systems: Technologies and Applications. Smart Innovation, Systems and Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-319-19728-9_11
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DOI: https://doi.org/10.1007/978-3-319-19728-9_11
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