Concorde is a computer code for the symmetric traveling salesman problem (TSP) and some related network optimization problems. The code is written in the ANSI C programming language and it is available for academic research use; for other uses, contact William Cook for licensing options. Concorde’s TSP solver has been used to obtain the optimal solutions to 106 of the 110 TSPLIB instances; the largest having 85,900 cities. The Concorde callable library includes over 700 functions permitting users to create specialized codes for TSP-like problems. All Concorde functions are thread-safe for programming in shared-memory parallel environments; the main TSP solver includes code for running over networks of UNIX workstations.

References in zbMATH (referenced in 303 articles , 1 standard article )

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  1. Alvarez, Aldair; Cordeau, Jean-François; Jans, Raf; Munari, Pedro; Morabito, Reinaldo: Formulations, branch-and-cut and a hybrid heuristic algorithm for an inventory routing problem with perishable products (2020)
  2. Khachay, Michael; Neznakhina, Katherine: Complexity and approximability of the Euclidean generalized traveling salesman problem in grid clusters (2020)
  3. Moreno, Alfredo; Munari, Pedro; Alem, Douglas: Decomposition-based algorithms for the crew scheduling and routing problem in road restoration (2020)
  4. Sahai, Tuhin: Dynamical systems theory and algorithms for NP-hard problems (2020)
  5. Badger, Matthew; Naples, Lisa; Vellis, Vyron: Hölder curves and parameterizations in the Analyst’s traveling salesman theorem (2019)
  6. da Silva, Tiago Tiburcio; Chaves, Antônio Augusto; Yanasse, Horacio Hideki; Luna, Henrique Pacca Loureiro: The multicommodity traveling salesman problem with priority prizes: a mathematical model and metaheuristics (2019)
  7. Date, Prasanna; Patton, Robert; Schuman, Catherine; Potok, Thomas: Efficiently embedding QUBO problems on adiabatic quantum computers (2019)
  8. Deng, Hao; Ma, Chao; Shen, Lijun; Yang, Chuanwu: Semi-supervised learning using autodidactic interpolation on sparse representation-based multiple one-dimensional embedding (2019)
  9. Divya, M.: Solving travelling salesman problem using ant systems: a programmer’s approach (2019)
  10. Huang, Zhouchun; Zheng, Qipeng Phil; Pasiliao, Eduardo; Boginski, Vladimir; Zhang, Tao: A cutting plane method for risk-constrained traveling salesman problem with random arc costs (2019)
  11. Kieu, Tien D.: The travelling salesman problem and adiabatic quantum computation: an algorithm (2019)
  12. Liu, Jia-Bao; Daoud, S. N.: Number of spanning trees in the sequence of some graphs (2019)
  13. Maniezzo, Vittorio; Boschetti, Marco A.; Carbonaro, Antonella; Marzolla, Moreno; Strappaveccia, Francesco: Client-side computational optimization (2019)
  14. Nowak, Maciek; Hewitt, Mike; Bachour, Hussam: Mileage bands in freight transportation (2019)
  15. Staněk, Rostislav; Greistorfer, Peter; Ladner, Klaus; Pferschy, Ulrich: Geometric and LP-based heuristics for angular travelling salesman problems in the plane (2019)
  16. Taillard, Éric D.; Helsgaun, Keld: POPMUSIC for the travelling salesman problem (2019)
  17. Toffolo, Túlio A. M.; Vidal, Thibaut; Wauters, Tony: Heuristics for vehicle routing problems: sequence or set optimization? (2019)
  18. Álvarez-Miranda, Eduardo; Luipersbeck, Martin; Sinnl, Markus: Gotta (efficiently) catch them all: Pokémon GO meets orienteering problems (2018)
  19. Avis, David; Jordan, Charles: \textttmplrs: a scalable parallel vertex/facet enumeration code (2018)
  20. Burger, M.; Su, Z.; De Schutter, B.: A node current-based 2-index formulation for the fixed-destination multi-depot travelling salesman problem (2018)

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