Open Access
MATEC Web Conf.
Volume 100, 2017
13th Global Congress on Manufacturing and Management (GCMM 2016)
Article Number 02025
Number of page(s) 8
Section Part 2: Internet +, Big data and Flexible manufacturing
Published online 08 March 2017
  1. Shim, V. A., Tan, K. C., Cheong, C. Y. et al. A Hybrid Estimation of Distribution Algorithm with Decomposition for Solving the Multiobjective Multiple Traveling Salesman Problem. IEEE transactions on systems, man and cybernetics, Part C. Applications and reviews: A publication of the IEEE Systems, Man, and Cybernetics Society, 2012, 42(5):682–691. [CrossRef] [Google Scholar]
  2. Daibo Liu, Mengshu Hou, Hong Qu et al. A simple model for the multiple traveling salesmen problem with single depot and multiple sink. COMPEL: The international journal for computation and mathematics in electrical and electronic engineering, 2013, 32(2):556–574. [CrossRef] [Google Scholar]
  3. Marco Casazza, Alberto Ceselli, Marc Nunkesser et al. Efficient algorithms for the double traveling salesman problem with multiple stacks. Computers & operations research, 2012, 39(5):1044–1053. [CrossRef] [Google Scholar]
  4. Cheang, B.,Gao, X., Lim, A. et al. Multiple pickup and delivery traveling salesman problem with last-in-first-out loading and distance constraints. European Journal of Operational Research, 2012, 223(1):60–75. [Google Scholar]
  5. Marco Casazza, Alberto Ceselli, Marc Nunkesser et al. Efficient algorithms for the double traveling salesman problem with multiple stacks. Computers & operations research, 2012, 39(5):1044–1053. [CrossRef] [Google Scholar]
  6. Carrabs, F., Cerulli, R., Speranza, M.G. et al. A branch-and-bound algorithm for the double travelling salesman problem with two stacks. Networks: An International Journal, 2013, 61(1):58–75. [Google Scholar]
  7. Estevez-Fernandez A, Borm P, Hamers H et al. On the core of multiple longest traveling salesman games. European Journal of Operational Research, 2006, 174(3):1816–1827. [CrossRef] [Google Scholar]
  8. Elad Kivelevitch, Kelly Cohen, Manish Kumar et al. A Market-based Solution to the Multiple Traveling Salesmen Problem. Journal of Intelligent & Robotic Systems: Theory & Application, 2013, 72(1):21–40. [CrossRef] [Google Scholar]
  9. Angel Felipe, M. Teresa Ortuno, Gregorio Tirado et al. The double traveling salesman problem with multiple stacks: A variable neighborhood search approach. Computers & operations research, 2009, 36(11):2983–2993. [CrossRef] [Google Scholar]
  10. Qu H, Yi Z, Tang H J. A columnar competitive model for solving multi-traveling salesman problem . Chaos, Solitons and Fractals. Chaos, solitions & Fractals, 2007, 31(4):1009–1019. [CrossRef] [Google Scholar]
  11. Laporte G, Nobert Y. A cutting planes algorithm for the m-salesmen problem . Journal of the Operational Research Society, 1980, 31:1017–1023. [CrossRef] [Google Scholar]
  12. Russell R A. An effective heuristic for the m-tour traveling salesman problem with some side conditions . Operations Research, 1977, 25(3):517–524. [CrossRef] [Google Scholar]
  13. Yonggang Peng, Xiaoping Luo, Wei Wei et al. A New Fuzzy Adaptive Simulated Annealing Genetic Algorithm and Its Convergence Analysis and Convergence Rate Estimation. International Journal of Control, Automation, and Systems, 2014, 12(3):670–679. [CrossRef] [Google Scholar]
  14. Hoseini, P., Shayesteh, M.G.. Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing. Digital Signal Processing, 2013, 23(3):879–893. [CrossRef] [Google Scholar]
  15. Min Dai, Dunbing Tang, Adriana Giret et al. Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer Integrated Manufacturing: An International Journal of Manufacturing and Product and Process Development, 2013, 29(5):418–429. [Google Scholar]
  16. Ying Xu, Rong Qu, Renfa Li et al. A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems. Annals of operations research, 2013, 206(Jul.):527–555. [CrossRef] [Google Scholar]
  17. Symone Soares, Carlos Henggeler Antunes, Rui Araujo et al. Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development. Neurocomputing, 2013, 121(Dec.9):498–511. [CrossRef] [Google Scholar]
  18. Xiaorong Xie*, Qirong Jiang Yingduo. Damping multimodal subsynchronous resonance using a static var compensator controller optimized by genetic algorithm and simulated annealing. European transactions on electrical power, 2012, 22(8):1191–1204. [CrossRef] [Google Scholar]
  19. A.F. Crossland, D. Jones, N.S. Wade et al. Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing. International journal of electrical power and energy systems, 2014, 59(Jul.):103–110. [CrossRef] [Google Scholar]
  20. Meei-Yuh Ku, Michael H. Hu, Ming-Jaan Wang et al. Simulated annealing based parallel genetic algorithm for facility layout problem. International journal of production research, 2011, 49(6/8):1801–1812. [CrossRef] [Google Scholar]

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