OPTIMAL ROUTE DETECTION BETWEEN EDUCATIONAL INSTITUTIONS OF REZEKNE MUNICIPALITY
DOI:
https://doi.org/10.17770/lner2017vol1.9.2457Keywords:
Educational institutions, Rezekne Municipality, Optimization, Travelling Salesman Problem, Simulated AnnealingAbstract
Information about the merger of schools or their optimization periodically appears in the society. It is believed that the ideal school network is not yet ready. The paper provides the analysis of the educational institutions locations by their availability. A theoretical research has been conducted and mathematically the shortest path has been calculated between different educational institutions. The paper also provides mapping of these educational institutions and location analysis of educational institutions at different levels. The main goal of the paper is to show the possibilities of applying the mathematical models in solving practical tasks – to determine the shortest path between the educational institutions. This study describes an optimization method called Simulated Annealing. The Simulated Annealing method is widely used in various combinatorial optimization tasks. Simulated Annealing is a stochastic optimization method that can be used to minimize the specified cost function given a combinatorial system with multiple degrees of freedom. In this paper the application of Travelling Salesman Problem, is demonstrated and an experiment aimed to find the shortest route between educational institutions of Rezekne Municipality is performed. It gives possibilities to analyse and search optimal school' s network in the Rezekne Municipality. Common research methods are used in this research: descriptive research method, statistical method, mathematical modellingDownloads
References
APPLEGATE, D.L., BIXBY, R., CHVATAL, V., COOK, W. (2006). The Traveling Salesman Problem: A Computational Study. Princeton University Press.
COOK, W. (2011). In Pursuit of the Traveling Salesman. Princeton: Princeton University Press, USA.
COUGHLIN, J.P., BARAN, R.H. (1985). Neural Computation in Hopfield Networks and Boltzmann Machines. University of Delaware Press.
EDUCATIONAL INSTITUTIONS. (2016). Retrieved April 15, 2017, from http://rezeknesnovads.lv/novada-iestades/izglitiba/izglitibas-iestades/ (in Latvian).
GRABUSTS, P. (2000). Solving TSP using classical simulated annealing method. Scientific Proceedings of Riga Technical University: Computer Science. Information Technology and Management Science, RTU, Riga, Issue 5, Volume 2, pp. 32-39.
GRANVILLE, V., KRIVANEK, M., RASSON J-P. (1994). Simulated annealing: A proof of convergence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), pp. 652–656.
INGBER, L. (1993). Simulated annealing: Practice versus theory. Math. Comput. Modelling, 18, pp. 29–57.
KIRKPATRICK, S., GELATT, C.D., VECCHI, M.P. (1983). Optimization by Simulated Annealing. Science, vol. 220, pp. 671-680.
KUZMINA, I. (2016). Ideālais skolu tīkls negatavs un slepens. Latvijas avīze, Retrieved April 15, 2017 from http://www.la.lv/idealais-skolu-tikls-negatavs-un-slepens/ (in Latvian).
LAARHOVEN, P.J.M., AARTS, E.H.L. (1987). Simulated Annealing: Theory and Applications. D.Reidel Publishing Company, Holland.
OTTEN, R.H., GINNEKEN, L.P. (1989). The Annealing Algorithm. Kluwer Academic Publishers.