TSPLIB

TSPLIB is a library of sample instances for the TSP (and related problem) from various sources and of various types. Instances of the following problem classes are available. Symmetric traveling salesman problem (TSP) Hamiltonian cycle problem (HCP) Asymmetric traveling salesman problem (ATSP) Sequential ordering problem (SOP) Capacitated vehicle routing problem (CVRP)


References in zbMATH (referenced in 657 articles , 1 standard article )

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  1. Archetti, C.; Feillet, D.; Mor, A.; Speranza, M. G.: Dynamic traveling salesman problem with stochastic release dates (2020)
  2. Baniasadi, Pouya; Foumani, Mehdi; Smith-Miles, Kate; Ejov, Vladimir: A transformation technique for the clustered generalized traveling salesman problem with applications to logistics (2020)
  3. Cacchiani, Valentina; Contreras-Bolton, Carlos; Toth, Paolo: Models and algorithms for the traveling salesman problem with time-dependent service times (2020)
  4. Carlos Gavidia-Calderon; César Beltrán Castañon: Isula: A java framework for ant colony algorithms (2020) not zbMATH
  5. Drezner, Zvi; Drezner, Taly Dawn: Biologically inspired parent selection in genetic algorithms (2020)
  6. Fachini, Ramon Faganello; Armentano, Vinícius Amaral: Exact and heuristic dynamic programming algorithms for the traveling salesman problem with flexible time windows (2020)
  7. Gokbayrak, Kagan; Avci, Harun: A voltage drop limited decentralized electric power distribution network (2020)
  8. Lancia, Giuseppe; Vidoni, Paolo: Finding the largest triangle in a graph in expected quadratic time (2020)
  9. Qiao, Wen-Bao; Créput, Jean-Charles: Multiple (k -)opt evaluation multiple (k -)opt moves with GPU high performance local search to large-scale traveling salesman problems (2020)
  10. Salman, Raad; Ekstedt, Fredrik; Damaschke, Peter: Branch-and-bound for the precedence constrained generalized traveling salesman problem (2020)
  11. Waissi, Gary R.; Kaushal, Pragya: A polynomial matrix processing heuristic algorithm for finding high quality feasible solutions for the TSP (2020)
  12. Arnold, Florian; Gendreau, Michel; Sörensen, Kenneth: Efficiently solving very large-scale routing problems (2019)
  13. Bouzbita, Safae; El Afia, Abdellatif; Faizi, Rdouan: Hidden Markov model classifier for the adaptive ACS-TSP pheromone parameters (2019)
  14. Brazil, Marcus; Volz, Marcus; Zachariasen, Martin; Ras, Charl; Thomas, Doreen: Computing minimum 2-edge-connected Steiner networks in the Euclidean plane (2019)
  15. Dhein, Guilherme; Zanetti, Marcelo Serrano; Bassi de Araújo, Olinto César; Cardoso, Ghendy Jr.: Minimizing dispersion in multiple drone routing (2019)
  16. Nowak, Maciek; Hewitt, Mike; Bachour, Hussam: Mileage bands in freight transportation (2019)
  17. Rossit, Diego Gabriel; Vigo, Daniele; Tohmé, Fernando; Frutos, Mariano: Visual attractiveness in routing problems: a review (2019)
  18. Sahai, Tuhin; Ziessler, Adrian; Klus, Stefan; Dellnitz, Michael: Continuous relaxations for the traveling salesman problem (2019)
  19. Salii, Yaroslav: Revisiting dynamic programming for precedence-constrained traveling salesman problem and its time-dependent generalization (2019)
  20. Salles da Cunha, Alexandre; Lucena, Abilio: Modeling and solving the angular constrained minimum spanning tree problem (2019)

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Further publications can be found at: http://comopt.ifi.uni-heidelberg.de/publications/index.html