{"id":55,"date":"2014-09-15T07:21:40","date_gmt":"2014-09-15T10:21:40","guid":{"rendered":"https:\/\/exatas.ufpr.br\/degraf_paulo\/?page_id=55"},"modified":"2019-06-28T13:25:25","modified_gmt":"2019-06-28T16:25:25","slug":"publicacoes-citacoes-dos-meus-trabalhos","status":"publish","type":"page","link":"https:\/\/exatas.ufpr.br\/degraf_paulo\/publicacoes-citacoes-dos-meus-trabalhos\/","title":{"rendered":"Publica\u00e7\u00f5es \u2799 Cita\u00e7\u00f5es dos meus trabalhos"},"content":{"rendered":"
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trabalhos que citam:<\/b> Siqueira, P.H. ; Scheer, S. ; Steiner, M.T.A. A new approach to solve the Traveling Salesman Problem. Neurocomputing (Amsterdam), v. 70, p. 1013-1021, 2007.<\/p>\n<\/div>\n

53. Saadatmand-Tarzjan, M. (2018). On computational complexity of the constructive-optimizer neural network for the traveling salesman problem. Neurocomputing, 321, 82-91. <\/div>\n
52. Ottoni, A. L., Nepomuceno, E. G., & de Oliveira, M. S. (2018). A Response Surface Model Approach to Parameter Estimation of Reinforcement Learning for the Travelling Salesman Problem. Journal of Control, Automation and Electrical Systems, 29(3), 350-359.<\/div>\n
51. Agrawal, A. P., & Kaur, A. (2017). An empirical evaluation of memory less and memory using meta-heuristics for solving travelling salesman problem. International Journal of Computational Systems Engineering, 3(4), 228-236.<\/div>\n
50. Wagh, A., & Nemade, V. (2017). Query Optimization using Modified Ant Colony Algorithm. International Journal of Computer Applications, 167(2).<\/div>\n
49. Qin, L. (2016). Hybrid algorithm for multi objective optimization problem. In MECHANICS AND MECHANICAL ENGINEERING: Proceedings of the 2015 International Conference (MME2015) (pp. 869-879).<\/div>\n
48. Jiang, Z., Discrete Bat Algorithm for Traveling Salesman Problem. 3rd International Conference on Information Science and Control Engineering, p. 343-347, 2016.<\/div>\n
47. Lin, Y., Bian, Z., Liu, X. Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing \u2013 tabu search algorithm to solve the symmetrical traveling salesman problem. Applied Soft Computing, V. 49, N. 1, p. 937-952, 2016.<\/div>\n
46. Duan, P., Ai,Y. Research on an Improved Ant Colony Optimization Algorithm and its Application. International Journal of Hybrid Information Technology, V. 9, N. 4, p. 223-234, 2016.<\/div>\n
45. Kosolov, A. D. The toolkit for automation of parallel solving of combinatorial problems in heterogeneous distributed computing environment. Modern problems of science and education, V. 2, N. 2, p. 275-283, 2015.<\/div>\n
44. Tarkov, M. S. Solving the traveling salesman problem using a recurrent neural network. Numerical Analysis and Applications, V. 8, N. 3, p. 275-283, 2015.<\/div>\n
43. Tarkov, M. S. Mapping parallel program graphs onto graphs of distributed computer systems by neural network algorithms. Parallel Programming: Practical Aspects, Models and Current Limitations, 2014.<\/div>\n
42. Agrawal, A. P.; Kaur, A. A comparative analysis of memory using and memory less algorithms for Quadratic Assignment Problem. Confluence The Next Generation Information Technology Summit (Confluence), 2014 5th International Conference, IEEE, p. 815-820, 2014.<\/div>\n
41. Michael Jr, D. R. W. Mind, Body, World: Foundations of Cognitive Science. AU Press Athabasca University, 2013.<\/div>\n
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40. Li, Y.; Ma, K.; Zhang, J. An Efficient Multicore based Parallel Computing Approach for TSP Problems. 2013 Ninth International Conference on Semantics, Knowledge and Grids, SKG 2013, p. 98-104, 2013.<\/p>\n<\/div>\n

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39. Tarkov, M.S. On the efficient construction of hamiltonian cycles in distributed computer systems by recurrent neural networks. 2013 International Siberian Conference on Control and Communications, SIBCON 2013, p. 1-4, 2013.<\/p>\n<\/div>\n

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38. Tarkov, M.S. On the efficient construction of Hamiltonian cycles in distributed computer systems recurrent neural networks. COORDINATING COUNCIL, p. 157-170, 2013.<\/p>\n<\/div>\n

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37. Santos, A.; Barradas, A.; Labidini, S.; Costa, N. A Comparison Between Optimization Heuristics of the Best Path Problem Applied to S-Route, GEOProcessing 2013, The Fifth International Conference on Advanced Geographic Information Systems, Applications, and Services, Nice, France, p. 172-177, 2013.<\/p>\n<\/div>\n

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36. Deng, W.; Chen, R.; He, B.; Liu, Y.; Yin, L.; Guo, J. A novel two-stage hybrid swarm intelligence optimization algorithm and application, Soft Computing, v. 16, n. 10, p. 1707-1722, 2012.<\/p>\n<\/div>\n

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35. Zhang, X.X.; Tong, J.W.; Liu, T. Hybrid path relinking algorithm for solving Traveling Salesman problem, Computer Engineering, V. 38, n. 12, p. 122-125, 2012.<\/p>\n<\/div>\n

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34. Liu, F. A dual population parallel ant colony optimization algorithm for solving the traveling salesman problem, Journal of Convergence Information Technology, V. 7, n. 5, p. 66-74, 2012.<\/p>\n<\/div>\n

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33. Tarkov, M. Optimization of mapping graphs of parallel programs onto graphs of distributed computer systems by recurrent neural network, Recurrent Neural Networks and Soft Computing, Dr. Mahmoud ElHefnawi (Ed.), In-Tech, 2012.<\/p>\n<\/div>\n

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32. Zhou, Z.; Duan, Y.; Li, P.; Pi, Y.; Yi, S. Cultivated land prediction based on least square support vector machines, Application Research of Computers, V. 29, n. 3, p. 873-876, 2012.<\/p>\n<\/div>\n

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31. Meng, X.; Shen, Z.; Yue, Y.; Pi, Y.; Tan, W. An Improvement to the Coordination Method of Ant Colony Algorithm, cdciem, p. 114-117, 2012 International Conference on Computer Distributed Control and Intelligent Enviromental Monitoring, 2012.<\/p>\n<\/div>\n

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30. Li, S.W.; Wang, J.Q.; Zeng, J.W. Uncertain linguistic information objectives\u00a0preference VRPTW of ant colony algorithm, Application Research of Computers, v. 29, n. 3, p. 869-876, 2012.<\/p>\n<\/div>\n

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29. Li, S.W.; Wang, J.Q.; Zeng, J.W. VRPTW problem solving multi-objective fuzzy preference ant colony algorithm, Application Research of Computers, v. 28, n. 12, p. 4495-4499, 2011.<\/p>\n<\/div>\n

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28. Tao, P.; Yueyu,L.; Wengui, C. Matrix Circle Intelligent Algorithm for the Traveling Salesman Problem. Industrial Engineering Journal, v. 14, n. 5, p. 89-91, 2011.<\/p>\n<\/div>\n

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27. Zhang, Y.; Wu, L.; Wang, S.;Wei, G.; Yan, J.; Zhu, Q. Improved ant colony algorithm based on membership cloud models, Computer Engineering and Applications, v. 47, n. 14, p. 46-55, 2011.<\/p>\n<\/div>\n

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26. Marinakis, Y.; Marinaki, M.; Dounias, G. Honey bees mating optimization algorithm for the Euclidean traveling salesman problem. Information Sciences, v. 181, n. 20, 2011.<\/p>\n<\/div>\n

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25. Zhang, X.; Bai, Q.; Yun, X. A new hybrid artificial bee colony algorithm for the traveling salesman problem. IEEE 3rd International Conference on Communication Software and Networks (ICCSN). p. 155-159, 2011.<\/p>\n<\/div>\n

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24. Tarkov, M.S. Construction of Hamiltonian cycles by recurrent neural networks in graphs of distributed computer systems. Numerical Analysis and Applications, v. 3, n. 4, p. 381-388, 2010.<\/p>\n<\/div>\n

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23. Chen, W.G.; Li, J.D.; Xiang, X.L.; Pan, T. A Single-parameter intelligent algorithm for the traveling salesman problem. 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), v. 3, p. 318-320, 2010.<\/p>\n<\/div>\n

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22. Dawson, M.R.W.; Dupuis, B.; Wilson, M. From Bricks toBrains: The Embodied Cognitive Science of LEGO Robots. Published by AU Press, Athabasca University, 2010.<\/p>\n<\/div>\n

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21. Wang, J.; Luo, C. Adaptive neighborhood method & Genetic Algorithm for solving Travelling Salesman Problem.Computer Engineering and Applications, v. 46, n. 27, p. 20-24, 2010.<\/p>\n<\/div>\n

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20. Wong, L.P.; Low, M.Y.H.; Chong, C.S. Bee colony optimization with local search for traveling salesman problem. International Journal on Artificial Intelligence Tools, v. 19, n. 3, p. 305-334, 2010.<\/p>\n<\/div>\n

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19. Tarkov, M.S. Construction of Hamiltonian cycles of recurrent neural network in toroidal graphs with ribs defects. Scientific Session of Moscow Engineering Physics Institute Part 2: XII All-RussianScientific Conference: Neuroinformatics (\u0422\u0430\u0440\u043a\u043e\u0432, M.C. \u041f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u0435 \u0413\u0430\u043c\u0438\u043b\u044c\u0442\u043e\u043d\u043e\u0432\u044b\u0445 \u0426\u0438\u043a\u043b\u043e\u0432 \u0420\u0435\u043a\u0443\u0440\u0440\u0435\u043d\u0442\u043d\u043e\u0439 \u041d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0439 \u0421\u0435\u0442\u044c\u044e \u0412 \u0422\u043e\u0440\u043e\u0438\u0434\u0430\u043b\u044c\u043d\u044b\u0445\u00a0\u0413\u0440\u0430\u0444\u0430\u0445 \u0421 \u0414\u0435\u0444\u0435\u043a\u0442\u0430\u043c\u0438 \u0420\u0435\u0431\u0435\u0440. \u041d\u0430\u0443\u0447\u043d\u0430\u044f \u0421\u0435\u0441\u0441\u0438\u044f \u041c\u0418\u0424\u0418-2010. \u0427.2\u00a0XII \u0412\u0441\u0435\u0440\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0430\u044f \u041d\u0430\u0443\u0447\u043d\u043e-\u0422\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u041a\u043e\u043d\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u044f.\u00a0\u041d\u0435\u0439\u0440\u043e\u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0430), p. 26-37, 2010.<\/p>\n<\/div>\n

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18. Tarkov, M.S. Construction of Hamiltonian cycles by recurrent neural networks in graphs of distributed computer systems, Siberian Journal of Numerical Mathematics (\u0422\u0430\u0440\u043a\u043e\u0432, M.C. \u041f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u0435 \u0433\u0430\u043c\u0438\u043b\u044c\u0442\u043e\u043d\u043e\u0432\u044b\u0445 \u0446\u0438\u043a\u043b\u043e\u0432 \u0432 \u0433\u0440\u0430\u0444\u0430\u0445
\n\u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0445 \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u0441\u0438\u0441\u0442\u0435\u043c \u0440\u0435\u043a\u0443\u0440\u0440\u0435\u043d\u0442\u043d\u044b\u043c\u0438\u00a0\u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u043c\u0438 \u0441\u0435\u0442\u044f\u043c\u0438, \u0421\u0438\u0431\u0438\u0440\u0441\u043a\u0438\u0439 \u0436\u0443\u0440\u043d\u0430\u043b \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0439\u00a0\u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u043a\u0438), v. 13, n. 4, p. 467-475, 2010.<\/p>\n<\/div>\n

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17. Tarkov, M.S.; Dugarov, G.A. Parallel algorithm for solving traveling salesman problem using Recurrent Neural Networks. Problem Info (\u0422\u0430\u0440\u043a\u043e\u0432, M.C.; \u0414\u0443\u0433\u0430\u0440\u043e\u0432, \u0413. \u0410. \u041f\u0430\u0440\u0430\u043b\u043b\u0435\u043b\u044c\u043d\u044b\u0439 \u0410\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u0420\u0435\u0448\u0435\u043d\u0438\u044f \u0417\u0430\u0434\u0430\u0447\u0438 \u041a\u043e\u043c\u043c\u0438\u0432\u043e\u044f\u0436\u0435\u0440\u0430 C
\n\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0420\u0435\u043a\u0443\u0440\u0440\u0435\u043d\u0442\u043d\u043e\u0439 \u041d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0439 \u0421\u0435\u0442\u0438, \u041f\u0440\u043e\u0431\u043b\u0435\u043c\u044b\u00a0\u0418\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0438), v. 2, n. 6, p. 4-9, 2010.<\/p>\n<\/div>\n

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16. Meng, W.; Xu, W. Application of Partheno-Genetic Algorithm in Traveling Salesman Problem. Logistics Technology, v. 28, n. 11, p. 73-75, 2009.<\/p>\n<\/div>\n

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15. Marinakis, Y. Heuristic and Metaheuristic Algorithms for the Traveling Salesman Problem. Encyclopedia of\u00a0Optimization – Part 8. Floudas, C.A. and Pardalos, P.M.(Eds.), p. 1498-1506, 2009.<\/p>\n<\/div>\n

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14. Zhang, Y.; Wu, L.; Wei, G. Comparison on solving TSP via intelligent algorithm. Computer Engineering and Applications, v. 45, n. 11, p. 11-15, 2009.<\/p>\n<\/div>\n

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13. Wang, J. Solving TSP Via Chaotic PSO and HNN Algorithm, Computer Knowledge And Technology, v. 5, n. 13, p. 22-24, 2009.<\/p>\n<\/div>\n

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12. Hammer, B.; Schrauwen, B.; Steil, J.J. Recent advances in efficient learning of recurrent networks, Proceedings of ESANN’2009 – European Symposium on Artificial Neural Networks – Advances in Computational Intelligence and Learning, Bruges (Belgium), D-side public, p. 213-226, 2009.<\/p>\n<\/div>\n

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11. Zhang, J. Natural Computation for the Traveling Salesman Problem, ICICTA Second International Conference on Intelligent Computation Technology and Automation, October 2009 v. 1, p.366-369.<\/p>\n<\/div>\n

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10. Zhang, Y.; Wu, L.; Wu, H. Comparison of neural network and evolutionary algorithm on engineering optimization. Computer Engineering and Applications, v. 45, n. 3, p. 1-6, 2009.<\/p>\n<\/div>\n

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9. Luo, C.Y.; Lu, B.; Liu, F. Neighbour field method for population initialization of TSP. Chongqing Daxue Xuebao\/Journal of Chongqing University, v. 32, n. 11, p. 1311-1315, 2009.<\/p>\n<\/div>\n

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8. Hu, Z.; Ding, Y.; Shao, Q. Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system. Expert Systems with Applications, v. 36, n. 3, p. 5248-5255, 2009.<\/p>\n<\/div>\n

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7. Li, M.; Yi, Z.; Zhu, M. Solving TSP by using Lotka-Volterra neural networks. Neurocomputing, v. 72, n. 16-18, p. 3873-3880, 2009.<\/p>\n<\/div>\n

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6. artigo do Wikipedia sobre o problema do Caixeiro Viajante<\/p>\n

🌎 site<\/a> |\u00a0📥\u00a0pdf<\/a><\/p>\n<\/div>\n

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5. Wong, L.P.; Low, M.Y.H.; Chong, C.S. Bee colony optimization with local search for traveling salesman problem. INDIN 2008: 6th IEEE International Conference on Industrial Informatics, p. 1019-1025, 2008.<\/p>\n<\/div>\n

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4. Zhang, X.; Tang, L. A new hybrid ant colony optimization algorithm for the traveling salesman problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5227, p. 148-155, 2008.<\/p>\n<\/div>\n

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3. Zou, G.; Jia, L. A novel evolutionary algorithm for bi-objective symmetric traveling salesman problem. Journal of Computational Information Systems, v.4, n. 5, p. 2051-2056, 2008.<\/p>\n<\/div>\n

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2. Zhang, X.; Tang, L. An ACO&SS algorithm for traveling salesman problem. Kongzhi yu Juece\/Control and Decision, v. 23, n. 7, p. 762-766, 2008.<\/p>\n<\/div>\n

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1. Zheng, S.; Hou, D.B.; Zhou, Z.K. Ant colony algorithm with dynamic transition probability. Kongzhi yu Juece\/Control and Decision, v. 23, n. 2, p. 225-228, 2008.<\/p>\n<\/div>\n

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trabalhos que citam:<\/b> Siqueira, P. H. (2012). Application of Wang\u2019s Recurrent Neural Network to solve the Transportation problem. IJCSNS, 12(7), 50.<\/p>\n<\/div>\n

1. Xavier, G. M. T., Casta\u00f1eda, F. G., Nava, L. M. F., & Cadenas, J. A. M. (2018). Memristive recurrent neural network. Neurocomputing, 273, 281-295.<\/div>\n
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trabalhos que citam:<\/b> Souza, L. V. ; Siqueira, P. H. Heuristic Methods Applied to the Optimization School Bus Transportation Routes: A Real Case. Lecture Notes in Computer Science, v. 6097, p. 247-256, 2010.<\/p>\n<\/div>\n

14. ELLEGOOD, William A. et al. School bus Routing Problem: Contemporary Trends and Research Directions. Omega, 2019.<\/div>\n
13. Campbell, James, et al. Contemporary Trends and Research Directions. 2019.<\/div>\n
12. Reihaneh, Mohammad. Integrated Routing Models for Enhanced Product and Service Delivery. 2018.<\/div>\n
11. REIHANEH, Mohammad; GHONIEM, Ahmed. A multi-start optimization-based heuristic for a food bank distribution problem. Journal of the Operational Research Society, v. 69, n. 5, p. 691-706, 2018.<\/div>\n
10.Alsobky, A., Hrk\u00fat, P., & Miku\u0161ov\u00e1, M. (2017, November). A Smart Application for University Bus Routes Optimization. In First International Conference on Intelligent Transport Systems (pp. 12-20). Springer, Cham.<\/div>\n
9. Siqueira, V. S.; Lima, F. J. E.; Silva, E. N.; Silva, R. V. S.; Rocha, M. L. Implementation of the Metaheuristic GRASP Applied to the School Bus Routing Problem. International Journal of e-Education, e-Business, e-Management and e-Learning, V. 6, N. 2, p. 137-145, 2016.<\/div>\n
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8. Campbell, J. F.; North J. W.; Ellegood, W. A. Modeling Mixed Load School Bus Routing. Quantitative Approaches in Logistics and Supply Chain Management. Springer International Publishing, p. 3-30, 2015.<\/p>\n<\/div>\n

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7. Hu, J.H.; Deng, J.; Huang, Z. Travel path based method for estimating public transit section flow, Application Research of Computers, v. 31, n. 5, p. 1399-1402, 2014.<\/p>\n<\/div>\n

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6. Dang, L.X.; Chen, X.; Kong, Y.F. Review os School Bus Routing Problem: Concept, Model and Optimization Algprithms, Journal of Henan University (Natural Science), v. 43, n. 6, p. 43-50, 2013.<\/p>\n<\/div>\n

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5. Dang, L.X.; Wang, Z.; Liu, Q.S.; Kong, Y.F. Heuristic algorithm for solving mixed load school bus routing problem, Computer Science, v. 40, n. 7, p. 248-256, 2013.<\/p>\n<\/div>\n

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4. Malairajan, R.A.; Ganesh, K.; Muhos, M.; Anbuudayasankar, S.P. Class of resource allocation problems in supply chain – a review, International Journal of Business Innovation and Research, v. 7, n. 1, p. 113-139, 2013.<\/p>\n<\/div>\n

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3. Malairajan, R.A.; Ganesh, K.; Muhos, M.; IskaniusSolak, P. CLING: heuristic to solve integrated resource allocation and routing problem with time window, International Journal of Services and Operations Management, v. 13, n. 2, p. 247-266, 2012.<\/p>\n<\/div>\n

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2. Solak, S. ; Scherrer, C. ; Ghoniem, A. The stop-and-drop problem in nonprofit food distribution networks, Annals of Operations Research, p. 1-20, 2012.<\/p>\n<\/div>\n

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1. Malairajan, J. Models and heuristics for a class of resource allocation problems in supply chain, Tese de doutorado: Anna University, 2011.<\/p>\n<\/div>\n

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trabalhos que citam:<\/b> Siqueira, P.H. ; Steiner, M.T.A.; Scheer, S. Recurrent Neural Networks with the Soft \u2018Winner Takes All\u2019 principle applied to the Traveling Salesman Problem. In: Donald Davendra (Org.). Traveling Salesman Problem, Theory and Applications, Rijeka: InTech Education and Publishing, 2010, v. 1, p. 177-196.<\/p>\n<\/div>\n

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1. Zhoua, Y., Luoa, Q., Chena, H., Hea, A., Wua, J., A discrete invasive weed optimization algorithm for solving traveling salesman problem, Neurocomputing, V. 151, n. 3, p. 1227\u20131236, 2015.<\/p>\n<\/div>\n

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trabalhos que citam:<\/b> Siqueira, P.H. ; Steiner, M.T.A. ; Scheer, S. Recurrent Neural Network with Soft ‘Winner Takes All’ principle for the TSP. In: ICNC 2010 – International Conference on Neural Computation, 2010, Valencia. Proceedings of the International Conference on Fuzzy Computation and International Conference on Neural Computation, 2010. v. 1. p. 265-270.<\/p>\n<\/div>\n

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2. Sahai, A. K.; Borah, N.; Chattopadhyay, R.; Joseph, S.; Abhilash, S. A bias-correction and downscaling technique for operational extended range forecasts based on self organizing map, Climate Dynamics, v. 48, n. 7-8, \u00a0p. 2437-2451, 2017.<\/p>\n<\/div>\n

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1. Tarkov, M. Optimization of mapping graphs of parallel programs onto graphs of distributed computer systems by recurrent neural network, Recurrent Neural Networks and Soft Computing, Dr. Mahmoud ElHefnawi (Ed.), In-Tech, 2012.<\/p>\n<\/div>\n

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trabalhos que citam:<\/b> Siqueira, P.H. ; Scheer, S. ; Steiner, M.T.A. A New Neural Network Approach to the Traveling Salesman Problem. Lecture Notes in Computer Science, Berlin, v. 3971, n. 1, p. 391-398, 2006.<\/p>\n<\/div>\n

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2. Zhu, R.; Zhong, S. Efficient solution for traveling salesman problem based on neural network, Journal of Computational Information Systems, v. 4, 2008.<\/p>\n<\/div>\n

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1. Benini, F.A.V. Rede Neural Recorrente com Perturba\u00e7\u00e3o Simult\u00e2nea Aplicada no Problema do Caixeiro Viajante. Disserta\u00e7\u00e3o de Mestrado da Escola de Engenharia da USP, 2008.<\/p>\n<\/div>\n

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trabalhos que citam:<\/b> Siqueira, P.H. ; Barboza, \u00c2.O. ; Carnieri, C. ; Steiner, M.T.A. Uma proposta de solu\u00e7\u00e3o para o problema da constru\u00e7\u00e3o de escalas de motoristas e cobradores de \u00f4nibus por meio do algoritmo do matching de peso m\u00e1ximo. Gest\u00e3o e Produ\u00e7\u00e3o (UFSCar), S\u00e3o Carlos, SP, v. 11, n. 2, p. 187-196, 2004.<\/p>\n<\/div>\n

3. Silva, J. L. C.; Arruda, J. B. F.; Silva, B. C. H. A Mathematical Model Used in Repairs for Electric Power Distribution Network. Latin America Transactions, IEEE (Revista IEEE America Latina) v.12, n.6, p.1101-1105, 2014.<\/div>\n
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2. Souza, R.A. Otimiza\u00e7\u00e3o das escalas de trabalho dos atendentes e dimensionamento de um call center receptivo. Tese de doutorado de Engenharia de produ\u00e7\u00e3o da UFSC, 2010.<\/p>\n<\/div>\n

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1. Poltosi, M.R. Elabora\u00e7\u00e3o de escalas de trabalho de t\u00e9cnicos de enfermagem com Busca Tab\u00fa e Algoritmos Gen\u00e9ticos. Disserta\u00e7\u00e3o de Mestrado de Computa\u00e7\u00e3o Aplicada de S\u00e3o Leopoldo, 2007.<\/p>\n<\/div>\n

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trabalhos que citam:<\/b> Siqueira, P.H. Aplica\u00e7\u00e3o do algoritmo do matching no problema da constru\u00e7\u00e3o de escalas de motoristas e cobradores de \u00f4nibus. Disserta\u00e7\u00e3o de Mestrado, Setor de Tecnologia e de Ci\u00eancias Exatas, UFPR, 1999.<\/p>\n<\/div>\n

10. Bonato, J. V. R. Clusteriza\u00e7\u00e3o de dados meteorol\u00f3gicos para compara\u00e7\u00e3o de t\u00e9cnicas de nowcasting. Disserta\u00e7\u00e3o de Mestrado em M\u00e9todos Num\u00e9ricos em Engenharia, UFPR, 2014.<\/div>\n
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9. Silva, G.P.; Gomes, A.C.; Souza, M.J.F. Modelos de Fluxo em Redes para o Problema de Escala de Motoristas de \u00d4nibus Urbano, XXVIII CNMAC: Congresso Nacional de Matem\u00e1tica Aplicada e Computacional, setembro de 2005, S\u00e3o Paulo-SP.<\/p>\n<\/div>\n

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8. Kotsko, E.G.S.; Machado, A.L.F.; Santos, E.M. Otimiza\u00e7\u00e3o na aloca\u00e7\u00e3o de professores na constru\u00e7\u00e3o de uma grade hor\u00e1ria escolar. Ambi\u00eancia, v. 1, n. 1, p. 31-45, 2005.<\/p>\n<\/div>\n

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7. Silva, G.P.; Gomes, A.C.; Souza, M.J.F. Uma Metodologia Baseada em Emparelhamentos Sucessivos Aplicada ao Problema de Programa\u00e7\u00e3o de Tripula\u00e7\u00f5es. XXXV SBPO: Simp\u00f3sio Brasileiro de Pesquisa Operaciona,l p. 1037-1046, 2005, Gramado-RS.<\/p>\n<\/div>\n

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6. Paes, F.G. Otimiza\u00e7\u00e3o de rotas para a coleta do lixo dom\u00e9stico: um tratamento GRASP do problema do Carteiro Chin\u00eas Misto (PCCM). Disserta\u00e7\u00e3o do Centro de Ci\u00eancia e Tecnologia da Universidade Estadual do Norte Fluminense, 2004.<\/p>\n<\/div>\n

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5. Souza, M.J.F.; Cardoso, L.X.T.; Silva, G.P.; Rodrigues, M.M.S.; Mapa, S.M.S. Metaheur\u00edsticas Aplicadas ao Problema de Programa\u00e7\u00e3o de Tripula\u00e7\u00f5es no Sistema de Transporte P\u00fablico. TEMA Tend\u00eancias em Matem\u00e1tica Aplicada e Computacional, v. 5, n. 2, p. 357-368, 2004.<\/p>\n<\/div>\n

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4. Mapa, S.M.S. Redu\u00e7\u00e3o de custos da programa\u00e7\u00e3o di\u00e1ria de tripula\u00e7\u00f5es de \u00f4nibus urbano via metaheur\u00edsticas. Monografia de gradua\u00e7\u00e3o em Engenharia de Produ\u00e7\u00e3o da UFOP, 2004.<\/p>\n<\/div>\n

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3. Souza, M.J.F.; Silva, G.P.; Mapa, S.M.S. M\u00e9todos de pesquisa em vizinhan\u00e7a vari\u00e1vel aplicados \u00e0 resolu\u00e7\u00e3o do problema de programa\u00e7\u00e3o di\u00e1ria de tripula\u00e7\u00f5es de \u00f4nibus urbano. XVIII ANPET, Congresso de Pesquisa e Ensino em Transportes, novembro de 2004, Florian\u00f3polis-SC.<\/p>\n<\/div>\n

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2. Souza, M.J.F.; Cardoso, L.X.T.; Silva, G.P. Programa\u00e7\u00e3o de Tripula\u00e7\u00f5es de \u00f4nibus urbano: uma abordagem heur\u00edstica. XXXV SBPO: novembro de 2003, Natal-RN.<\/p>\n<\/div>\n

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1. Souza, M.J.F.; Silva, G.P.; Rodrigues, M.M.S.; Mapa, S.M.S. Um estudo das heur\u00edsticas Simulated Annealing e VNS aplicadas ao problema de programa\u00e7\u00e3o de tripula\u00e7\u00f5es. XXIII Encontro Nacional de Engenharia de Produ\u00e7\u00e3o, outubro de 2003, Ouro Preto-MG.<\/p>\n<\/div>\n

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trabalhos que citam:<\/b> Siqueira, P.H. Uma nova abordagem na Resolu\u00e7\u00e3o do problema do Caixeiro Viajante. Tese de Doutorado, Setores de Tecnologia e de Ci\u00eancias Exatas, UFPR, 2005.<\/p>\n<\/div>\n

9. Jos\u00e9, C. M. A. Coordena\u00e7\u00e3o \u00f3tima de m\u00faltiplos rob\u00f4s de servi\u00e7o e de recarga em tarefas persistentes. Disserta\u00e7\u00e3o de Mestrado em Engenharia El\u00e9trica, UFJF, 2015.<\/div>\n
8. Bonato, J. V. R. Clusteriza\u00e7\u00e3o de dados meteorol\u00f3gicos para compara\u00e7\u00e3o de t\u00e9cnicas de nowcasting. Disserta\u00e7\u00e3o de Mestrado em M\u00e9todos Num\u00e9ricos em Engenharia, UFPR, 2014.<\/div>\n
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7. Silva, G.L.; Fonseca, A.P. Uma nova abordagem para o problema de roteiriza\u00e7\u00e3o de ve\u00edculos com restri\u00e7\u00f5es operacionais. Anais do XXV ANPET – Congresso de Ensino e Pesquisa em Transportes, 2011.<\/p>\n<\/div>\n

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6. Silva, G.L. Uma nova abordagem para o problema de roteiriza\u00e7\u00e3o de ve\u00edculos com restri\u00e7\u00f5es operacionais. Tese de Doutorado em Transportes da UNB, 2010.<\/p>\n<\/div>\n

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5. Azevedo, R.P.; Santo, Y.B.I. Estudo para otimiza\u00e7\u00e3o de distribui\u00e7\u00e3o de medicamentos de uma farm\u00e1cia atrav\u00e9s de roteiriza\u00e7\u00e3o de ve\u00edculos. In: Renata Melo e Silva de Oliveira (Org.) Engenharia de Produ\u00e7\u00e3o: T\u00f3picos e Aplica\u00e7\u00f5es. EDUEPA, Universidade do Estado do Par\u00e1, 2010.<\/p>\n<\/div>\n

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4. Benini, F.A.V. Rede Neural Recorrente com Perturba\u00e7\u00e3o Simult\u00e2nea Aplicada no Problema do Caixeiro Viajante. Disserta\u00e7\u00e3o de Mestrado da Escola de Engenharia da USP, 2008.<\/p>\n<\/div>\n

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3. Vitor, A. Determina\u00e7\u00e3o do roteamento dos atendimentos de uma empresa de comercializa\u00e7\u00e3o agr\u00edcola – varia\u00e7\u00f5es de solu\u00e7\u00f5es heur\u00edsticas. Disserta\u00e7\u00e3o de mestrado em M\u00e9todos Num\u00e9ricos em Engenharia da UFPR, 2007.<\/p>\n<\/div>\n

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2. Azevedo, S.O. Sistema de agentes polig\u00ednicos para estegan\u00e1lise de imagens digitais. Disserta\u00e7\u00e3o de mestrado em Sistemas e Computa\u00e7\u00e3o da UFRN, 2007.<\/p>\n<\/div>\n

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1. Herrera, B.A.L.M. Combina\u00e7\u00e3o de enxame de part\u00edculas com inspira\u00e7\u00e3o qu\u00e2ntica e m\u00e9todo Linkernighan-Helsgaun aplicada ao problema do Caixeiro viajante Disserta\u00e7\u00e3o de mestrado em Engenharia de Produ\u00e7\u00e3o e Sistemas da PUC-PR, 2007.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

trabalhos que citam: Siqueira, P.H. ; Scheer, S. ; Steiner, M.T.A. A new approach to solve the Traveling Salesman Problem. Neurocomputing (Amsterdam), v. 70, p. 1013-1021, 2007. 53. Saadatmand-Tarzjan, M. (2018). On computational complexity of the constructive-optimizer neural network for… <\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-55","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/exatas.ufpr.br\/degraf_paulo\/wp-json\/wp\/v2\/pages\/55","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/exatas.ufpr.br\/degraf_paulo\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/exatas.ufpr.br\/degraf_paulo\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/exatas.ufpr.br\/degraf_paulo\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/exatas.ufpr.br\/degraf_paulo\/wp-json\/wp\/v2\/comments?post=55"}],"version-history":[{"count":45,"href":"https:\/\/exatas.ufpr.br\/degraf_paulo\/wp-json\/wp\/v2\/pages\/55\/revisions"}],"predecessor-version":[{"id":3376,"href":"https:\/\/exatas.ufpr.br\/degraf_paulo\/wp-json\/wp\/v2\/pages\/55\/revisions\/3376"}],"wp:attachment":[{"href":"https:\/\/exatas.ufpr.br\/degraf_paulo\/wp-json\/wp\/v2\/media?parent=55"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}