GAMS

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 616 articles , 2 standard articles )

Showing results 1 to 20 of 616.
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  1. Atabaki, Mohammad Saeid; Mohammadi, Mohammad: A genetic algorithm for integrated lot sizing and supplier selection with defective items and storage and supplier capacity constraints (2017)
  2. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  3. El Hamzaoui, Youness; Bassam, Ali; Abatal, Mohamed; Rodríguez, José A.; Duarte-Villaseñor, Miguel A.; Escobedo, Lizbeth; Puga, Sergio A.: Flexibility in biopharmaceutical manufacturing using particle swarm algorithms and genetic algorithms (2017)
  4. Lima, Ricardo M.; Grossmann, Ignacio E.: On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study (2017)
  5. Puranik, Yash; Sahinidis, Nikolaos V.: Bounds tightening based on optimality conditions for nonconvex box-constrained optimization (2017)
  6. Wang, Ximing; Pardalos, Panos M.: A modified active set algorithm for transportation discrete network design bi-level problem (2017)
  7. Aggarwal, Abha; Khan, Imran: Solving multi-objective fuzzy matrix games via multi-objective linear programming approach. (2016)
  8. 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)
  9. Boland, Natashia; Kalinowski, Thomas; Rigterink, Fabian: New multi-commodity flow formulations for the pooling problem (2016)
  10. Borraz-Sánchez, Conrado; Bent, Russell; Backhaus, Scott; Hijazi, Hassan; Van Hentenryck, Pascal: Convex relaxations for gas expansion planning (2016)
  11. Brás, Carmo; Eichfelder, Gabriele; Júdice, Joaquim: Copositivity tests based on the linear complementarity problem (2016)
  12. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  13. Grimstad, Bjarne; Sandnes, Anders: Global optimization with spline constraints: a new branch-and-bound method based on B-splines (2016)
  14. Hellemo, Lars; Tomasgard, Asgeir: A generalized global optimization formulation of the pooling problem with processing facilities and composite quality constraints (2016)
  15. Hu, T. C.; Kahng, Andrew B.: Linear and integer programming made easy (2016)
  16. Iusem, Alfredo N.; Júdice, Joaquim J.; Sessa, Valentina; Sherali, Hanif D.: On the numerical solution of the quadratic eigenvalue complementarity problem (2016)
  17. Karasakal, Esra; Silav, Ahmet: A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage (2016)
  18. Karasakal, Orhan: Minisum and maximin aerial surveillance over disjoint rectangles (2016)
  19. Kimbrough, Steven Orla; Lau, Hoong Chuin: Business analytics for decision making (2016)
  20. Mínguez, R.; García-Bertrand, R.: Robust transmission network expansion planning in energy systems: improving computational performance (2016)

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Further publications can be found at: http://www.gams.com/presentations/index.htm