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 865 articles , 2 standard articles )

Showing results 1 to 20 of 865.
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  1. Haywood, Adam B.; Lunday, Brian J.; Robbins, Matthew J.; Pachter, Meir N.: The weighted intruder path covering problem (2022)
  2. Karakostas, Panagiotis; Sifaleras, Angelo; Georgiadis, Michael C.: Variable neighborhood search-based solution methods for the pollution location-inventory-routing problem (2022)
  3. Alpaslan Takan, Melis; Kasimbeyli, Refail: Multiobjective mathematical models and solution approaches for heterogeneous fixed fleet vehicle routing problems (2021)
  4. Badri, Seyed Amin; Daghbandan, Allahyar; Aghabeiginiyay, Fatalaki Zahra; Mirzazadeh, Mohammad: Flow shop scheduling under time-of-use electricity tariffs using fuzzy multi-objective linear programming approach (2021)
  5. Bordón, Maximiliano R.; Montagna, Jorge M.; Corsano, Gabriela: Solution approaches for solving the log transportation problem (2021)
  6. Defalque, Cristiane Maria; da Silva, Aneirson Francisco; Silva Marins, Fernando Augusto: Goal programming model applied to waste paper logistics processes (2021)
  7. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  8. Eichfelder, Gabriele; Klamroth, Kathrin; Niebling, Julia: Nonconvex constrained optimization by a filtering branch and bound (2021)
  9. Francesco Ceccon, Ruth Misener: Solving the pooling problem at scale with extensible solver GALINI (2021) arXiv
  10. Guo, Shaoyan; Xu, Huifu; Zhang, Liwei: Existence and approximation of continuous Bayesian Nash equilibria in games with continuous type and action spaces (2021)
  11. Khodayifar, Salman: Minimum cost multicommodity network flow problem in time-varying networks: by decomposition principle (2021)
  12. Lohmann, Timo; Bussieck, Michael R.; Westermann, Lutz; Rebennack, Steffen: High-performance prototyping of decomposition methods in GAMS (2021)
  13. Mahajan, Ashutosh; Leyffer, Sven; Linderoth, Jeff; Luedtke, James; Munson, Todd: Minotaur: a mixed-integer nonlinear optimization toolkit (2021)
  14. Mavrotas, George; Makryvelios, Evangelos: Combining multiple criteria analysis, mathematical programming and Monte Carlo simulation to tackle uncertainty in research and development project portfolio selection: a case study from Greece (2021)
  15. Sadrnia, Abdolhossein; Sani, Amirreza Payandeh; Langarudi, Najme Roghani: Sustainable closed-loop supply chain network optimization for construction machinery recovering (2021)
  16. Sheng Dai, Yu-Hsueh Fang, Chia-Yen Lee, Timo Kuosmanen: pyStoNED: A Python Package for Convex Regression and Frontier Estimation (2021) arXiv
  17. Singh, Bismark; Knueven, Bernard: Lagrangian relaxation based heuristics for a chance-constrained optimization model of a hybrid solar-battery storage system (2021)
  18. Wolsey, Laurence A.: Integer programming (2021)
  19. Xie, Jianhui; Xie, Qiwei; Li, Yongjun; Liang, Liang: Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique (2021)
  20. Ashimov, Abdykappar A.; Borovskiy, Yuriy V.; Novikov, Dmitry A.; Sultanov, Bahyt T.; Onalbekov, Mukhit A.: Macroeconomic analysis and parametric control of a regional economic union (2020)

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