The General Algebraic Modeling System (GAMS) is specifically designed for modeling linear, nonlinear and mixed integer optimization problems. The system is especially useful with large, complex problems. GAMS is available for use on personal computers, workstations, mainframes and supercomputers. GAMS allows the user to concentrate on the modeling problem by making the setup simple. The system takes care of the time-consuming details of the specific machine and system software implementation. GAMS is especially useful for handling large, complex, one-of-a-kind problems which may require many revisions to establish an accurate model. The system models problems in a highly compact and natural way. The user can change the formulation quickly and easily, can change from one solver to another, and can even convert from linear to nonlinear with little trouble.

References in zbMATH (referenced in 586 articles , 2 standard articles )

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  1. Aggarwal, Abha; Khan, Imran: Solving multi-objective fuzzy matrix games via multi-objective linear programming approach. (2016)
  2. Bastian, Nathaniel D.; Griffin, Paul M.; Spero, Eric; Fulton, Lawrence V.: Multi-criteria logistics modeling for military humanitarian assistance and disaster relief aerial delivery operations (2016)
  3. Brás, Carmo; Eichfelder, Gabriele; Júdice, Joaquim: Copositivity tests based on the linear complementarity problem (2016)
  4. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  5. Grimstad, Bjarne; Sandnes, Anders: Global optimization with spline constraints: a new branch-and-bound method based on B-splines (2016)
  6. Hellemo, Lars; Tomasgard, Asgeir: A generalized global optimization formulation of the pooling problem with processing facilities and composite quality constraints (2016)
  7. Iusem, Alfredo N.; Júdice, Joaquim J.; Sessa, Valentina; Sherali, Hanif D.: On the numerical solution of the quadratic eigenvalue complementarity problem (2016)
  8. Karasakal, Esra; Silav, Ahmet: A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage (2016)
  9. Kimbrough, Steven Orla; Lau, Hoong Chuin: Business analytics for decision making (2016)
  10. Mitra, Sumit; Garcia-Herreros, Pablo; Grossmann, Ignacio E.: A cross-decomposition scheme with integrated primal-dual multi-cuts for two-stage stochastic programming investment planning problems (2016)
  11. Petridis, Konstantinos; Chatzigeorgiou, Alexander; Stiakakis, Emmanouil: A spatiotemporal data envelopment analysis (S-T DEA) approach: the need to assess evolving units (2016)
  12. Philpott, Andy; Ferris, Michael; Wets, Roger: Equilibrium, uncertainty and risk in hydro-thermal electricity systems (2016)
  13. Rose, Daniel; Schmidt, Martin; Steinbach, Marc C.; Willert, Bernhard M.: Computational optimization of gas compressor stations: MINLP models versus continuous reformulations (2016)
  14. Trespalacios, Francisco; Grossmann, Ignacio E.: Cutting plane algorithm for convex generalized disjunctive programs (2016)
  15. Yoda, Kunikazu; Prékopa, András: Convexity and solutions of stochastic multidimensional 0-1 knapsack problems with probabilistic constraints (2016)
  16. Zhu, Linlin; Li, Xiuting; Dong, Jichang: Research on the impact of aging on urban housing demand (2016)
  17. Bagirov, A.M.; Ordin, B.; Ozturk, G.; Xavier, A.E.: An incremental clustering algorithm based on hyperbolic smoothing (2015)
  18. Chagwiza, Godfrey; Jones, Brian C.; Hove-Musekwa, Senelani D.; Mtisi, Sobona: A generalised likelihood uncertainty estimation mixed-integer programming model: application to a water resource distribution network (2015)
  19. Duarte, Belmiro P.M.; Wong, Weng Kee; Atkinson, Anthony C.: A semi-infinite programming based algorithm for determining T-optimum designs for model discrimination (2015)
  20. Honarmand, Masoud; Zakariazadeh, Alireza; Jadid, Shahram: Self-scheduling of electric vehicles in an intelligent parking lot using stochastic optimization (2015)

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