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 716 articles , 2 standard articles )

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  1. Schwarz, Hannes; Bertsch, Valentin; Fichtner, Wolf: Two-stage stochastic, large-scale optimization of a decentralized energy system: a case study focusing on solar PV, heat pumps and storage in a residential quarter (2018)
  2. Asimakopoulou, Georgia E.; Vlachos, Andreas G.; Hatziargyriou, Nikos D.: Bilevel model for retail electricity pricing (2017)
  3. 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)
  4. Atabaki, Mohammad Saeid; Mohammadi, Mohammad; Naderi, Bahman: Hybrid genetic algorithm and invasive weed optimization via priority based encoding for location-allocation decisions in a three-stage supply chain (2017)
  5. Bongartz, Dominik; Mitsos, Alexander: Deterministic global optimization of process flowsheets in a reduced space using McCormick relaxations (2017)
  6. Cafaro, Diego C.; Cerdá, Jaime: Short-term operational planning of refined products pipelines (2017)
  7. Djelassi, Hatim; Mitsos, Alexander: A hybrid discretization algorithm with guaranteed feasibility for the global solution of semi-infinite programs (2017)
  8. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  9. Edelev, Alexey; Sidorov, Ivan: Combinatorial modeling approach to find rational ways of energy development with regard to energy security requirements (2017)
  10. 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)
  11. Fumero, Yanina; Corsano, Gabriela; Montagna, Jorge M.: An MILP model for planning of batch plants operating in a campaign-mode (2017)
  12. Geißler, Björn; Morsi, Antonio; Schewe, Lars; Schmidt, Martin: Penalty alternating direction methods for mixed-integer optimization: a new view on feasibility pumps (2017)
  13. 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)
  14. Harwood, Stuart M.; Barton, Paul I.: How to solve a design centering problem (2017)
  15. Heirung, Tor Aksel N.; Ydstie, B.Erik; Foss, Bjarne: Dual adaptive model predictive control (2017)
  16. Kallrath, Josef: Packing ellipsoids into volume-minimizing rectangular boxes (2017)
  17. Kersting, Kristian; Mladenov, Martin; Tokmakov, Pavel: Relational linear programming (2017)
  18. Lasdon, Leon; Shirzadi, Shawn; Ziegel, Eric: Implementing CRM models for improved oil recovery in large oil fields (2017)
  19. Leimbach, Marian; Schultes, Anselm; Baumstark, Lavinia; Giannousakis, Anastasis; Luderer, Gunnar: Solution algorithms for regional interactions in large-scale integrated assessment models of climate change (2017)
  20. Lima, Ricardo M.; Grossmann, Ignacio E.: On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study (2017)

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