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 630 articles , 2 standard articles )

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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. Cafaro, Diego C.; Cerdá, Jaime: Short-term operational planning of refined products pipelines (2017)
  3. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  4. Edelev, Alexey; Sidorov, Ivan: Combinatorial modeling approach to find rational ways of energy development with regard to energy security requirements (2017)
  5. 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)
  6. Hart, William E.; Laird, Carl D.; Watson, Jean-Paul; Woodruff, David L.; Hackebeil, Gabriel A.; Nicholson, Bethany L.; Siirola, John D.: Pyomo -- optimization modeling in Python (2017)
  7. Lasdon, Leon; Shirzadi, Shawn; Ziegel, Eric: Implementing CRM models for improved oil recovery in large oil fields (2017)
  8. Lima, Ricardo M.; Grossmann, Ignacio E.: On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study (2017)
  9. Li, Xiang; Tomasgard, Asgeir; Barton, Paul I.: Natural gas production network infrastructure development under uncertainty (2017)
  10. Miralinaghi, Mohammad; Keskin, Burcu B.; Lou, Yingyan; Roshandeh, Arash M.: Capacitated refueling station location problem with traffic deviations over multiple time periods (2017)
  11. Puranik, Yash; Sahinidis, Nikolaos V.: Bounds tightening based on optimality conditions for nonconvex box-constrained optimization (2017)
  12. Wang, Ximing; Pardalos, Panos M.: A modified active set algorithm for transportation discrete network design bi-level problem (2017)
  13. Wood, Simon N.: Generalized additive models. An introduction with R. (2017)
  14. Aggarwal, Abha; Khan, Imran: Solving multi-objective fuzzy matrix games via multi-objective linear programming approach. (2016)
  15. 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)
  16. Boland, Natashia; Kalinowski, Thomas; Rigterink, Fabian: New multi-commodity flow formulations for the pooling problem (2016)
  17. Borraz-Sánchez, Conrado; Bent, Russell; Backhaus, Scott; Hijazi, Hassan; Van Hentenryck, Pascal: Convex relaxations for gas expansion planning (2016)
  18. Brás, Carmo; Eichfelder, Gabriele; Júdice, Joaquim: Copositivity tests based on the linear complementarity problem (2016)
  19. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  20. Grimstad, Bjarne; Sandnes, Anders: Global optimization with spline constraints: a new branch-and-bound method based on B-splines (2016)

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