Open Access
Issue |
RAIRO-Oper. Res.
Volume 55, Number 5, September-October 2021
|
|
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Page(s) | 3021 - 3039 | |
DOI | https://doi.org/10.1051/ro/2021148 | |
Published online | 14 October 2021 |
- A. Ardalan, S. Karimi, O. Poursabzi and B. Naderi, A novel imperialist competitive algorithm for generalized traveling salesman problems. Appl. Soft Comput. 26 (2015) 546–555. [CrossRef] [Google Scholar]
- D. Ben-Arieh, G. Gutin, M. Penn, A. Yeo and A. Zverovitch, Transformations of generalized ATSP into ATSP. Oper. Res. Lett. 31 (2003) 357–365. [CrossRef] [Google Scholar]
- B. Bontoux, C. Artigues and D. Feillet, A memetic algorithm with a large neighborhood crossover operator for the generalized traveling salesman problem. Comput. Oper. Res. 37 (2010) 1844–1852. [CrossRef] [Google Scholar]
- C. Chira, C.M. Pintea and D. Dumitrescu, Sensitive ant systems in combinatorial optimization. Proceedings of the International Conference on Knowledge Engineering, Principles and Techniques, KEPT, Cluj-Napoca, Romania (2007) 185–192. [Google Scholar]
- V. Dimitrijevic and Z. Saric, An efficient transformation of the generalized traveling salesman problem into the traveling salesman problem. Geogr. Inf. Sci. 102 (1997) 105–110. [CrossRef] [Google Scholar]
- M. Fischetti, J.J. Salazar-González and P. Toth, A branch-and-cut algorithm for the symmetric generalized traveling salesman problem. Oper. Res. 45 (1997) 378–394. [CrossRef] [Google Scholar]
- M. Fischetti, J.J. Salazar-González and P. Toth, The generalized traveling salesman and orienteering problems. In edited by G. Gutin and A.P. Punnen. Vol 12 of The traveling salesman problem and its variations Combinatorial Optimization. Springer, Boston, MA (2007). [Google Scholar]
- G. Gutin, D. Karapetyan and N. Krasnogor, Memetic algorithm for the generalized asymmetric traveling salesman problem. Stud. Comput. Intell. 129 (2008) 199–210. [Google Scholar]
- K. Helsgaun, Solving the equality generalized traveling salesman problem using the Lin-Kernighan-Helsgaun-algorithm. Math. Program. Comput. 17 (2015) 1–19. [Google Scholar]
- C. Jiang, Z. Wan and Z. Peng, A new efficient hybrid algorithm for large scale multiple traveling salesman problems. Expert Syst. Appl. 139 (2020) 112867. [CrossRef] [Google Scholar]
- D. Karapetyan and G. Gutin, Lin-Kernighan heuristic adaptations for the generalized traveling salesman problem. Eur. J. Oper. Res. 208 (2011) 221–232. [CrossRef] [Google Scholar]
- D. Karapetyan and G. Gutin, Efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem. Eur. J. Oper. Res. 219 (2012) 234–251. [CrossRef] [Google Scholar]
- M.Y. Khachai and E.D. Neznakhina, Approximation schemes for the generalized traveling salesman problem. Proc. Steklov Inst. Math. 299 (2017) 97–105. [CrossRef] [Google Scholar]
- G. Laporte, H. Mercure and Y. Nobert, Generalized traveling salesman problem through n sets of nodes: The asymmetric case. Discrete Appl. Math. 18 (1987) 185–197. [CrossRef] [Google Scholar]
- V.S. Lawrence and M.S. Daskin, A random-key genetic algorithm for the generalized traveling salesman problem. Eur. J. Oper. Res. 174 (2006) 38–53. [CrossRef] [Google Scholar]
- Y. Lien, E. Ma and B.W.S. Wah, Transformation of the generalized traveling salesman problem into the standard traveling salesman problem. Inf. Sci. 74 (1993) 177–189. [CrossRef] [Google Scholar]
- S. Lin and B.W. Kernighan, An effective heuristic Algorithm for the traveling-salesman problem. Oper. Res. 21 (1973) 498–516. [CrossRef] [Google Scholar]
- T. Makarovskikh, A. Panyukov and E. Savitsky, Software development for cutting tool routing problems. Procedia Manuf. 29 (2019) 567–574. [CrossRef] [Google Scholar]
- M. Mestria, New hybrid heuristic algorithm for the clustered traveling salesman problem. Comput. Ind. Eng. 116 (2018) 1–12. [CrossRef] [Google Scholar]
- C. Noon and J.C. Bean, A Lagrangian based approach for the asymmetric generalized traveling salesman problem. Oper. Res. 39 (1991) 623–632. [CrossRef] [Google Scholar]
- A.A. Petunin, E.G. Polishchuk and S.S. Ukolov, On the new algorithm for solving continuous cutting problems. IFAC 52 (2019) 2320–2325. [Google Scholar]
- J. Renaud and F.F. Boctor, An efficient composite heuristic for the symmetric generalized travelling salesman problem. Eur. J. Oper. Res. 108 (1998) 571–584. [CrossRef] [Google Scholar]
- J. Renaud, F.F. Boctor and G. Laporte, A fast composite heuristic for the symmetric traveling salesman problem. INFORMS J. Comput. 8 (1996) 134–143. [CrossRef] [Google Scholar]
- R. Salman, F. Ekstedt and P. Damaschke, Branch-and-bound for the precedence constrained generalized traveling salesman problem. Oper. Res. Lett. 48 (2020) 163–166. [CrossRef] [Google Scholar]
- S.L. Smith and F. Imeson, GLNS: An effective large neighborhood search heuristic for the generalized traveling salesman problem. Comput. Oper. Res. 87 (2017) 1–19. [Google Scholar]
- S.S. Srivastava, S. Kumar, R. Garg and P. Sen, Generalized traveling salesman problem through n sets of nodes. CORS J. 7 (1969) 97–101. [Google Scholar]
- K. Sundar and S. Rathinam, Generalized multiple depot traveling salesmen problem-Polyhedral study and exact algorithm. Comput. Oper. Res. 70 (2016) 39–55. [CrossRef] [Google Scholar]
- J. Yang, X. Shi, M. Marchese and Y. Liang, An ant colony optimization method for generalized TSP problem. Prog. Nat. Sci. 18 (2008) 1417–1422. [CrossRef] [Google Scholar]
- Y. Yuan, D. Cattaruzza, M. Ogier and F. Semet, A branch-and-cut algorithm for the generalized traveling salesman problem with time windows. Oper. Res. Lett. 286 (2020) 849–866. [CrossRef] [Google Scholar]
- X. Zhou and B. Rodrigues, An extension of the Christofides heuristic for the generalized multiple depot multiple traveling salesmen problem. Oper. Res. Lett. 257 (2017) 735–745. [CrossRef] [Google Scholar]
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