Pyomo -- optimization modeling in Python. This book provides a complete and comprehensive guide to Pyomo (Python optimization modeling objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Modeling is a fundamental process in many aspects of scientific research, engineering, and business. This text beautifully illustrates the breadth of the modeling capabilities that are supported by this new software and its handling of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (algebraic modeling language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python’s interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. The text begins with a tutorial on simple linear and integer programming models. Information needed to install and get started with the software is also provided. A detailed reference of Pyomo’s modeling components is illustrated with extensive examples, including a discussion of how to load data from sources like spreadsheets and databases. The final chapters cover advanced topics such as nonlinear models, stochastic models, and scripting examples.

References in zbMATH (referenced in 18 articles , 1 standard article )

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  1. Cai, W.; Singham, D. I.: A principal-agent problem with heterogeneous demand distributions for a carbon capture and storage system (2018)
  2. Mirz, Markus; Razik, Lukas; Dinkelbach, Jan; Tokel, Halil Alper; Alirezaei, Gholamreza; Mathar, Rudolf; Monti, Antonello: A cosimulation architecture for power system, communication, and market in the smart grid (2018)
  3. Chen, Hung-Hsin; Yang, Chang-Biau: Multiperiod portfolio investment using stochastic programming with conditional value at risk (2017)
  4. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  5. Gemine, Quentin; Ernst, Damien; Cornélusse, Bertrand: Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution (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. Kersting, Kristian; Mladenov, Martin; Tokmakov, Pavel: Relational linear programming (2017)
  8. Gade, Dinakar; Hackebeil, Gabriel; Ryan, Sarah M.; Watson, Jean-Paul; Wets, Roger J.-B.; Woodruff, David L.: Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs (2016)
  9. Munoz, F. D.; Hobbs, B. F.; Watson, J.-P.: New bounding and decomposition approaches for MILP investment problems: multi-area transmission and generation planning under policy constraints (2016)
  10. Vojvodic, Goran; Jarrah, Ahmad I.; Morton, David P.: Forward thresholds for operation of pumped-storage stations in the real-time energy market (2016)
  11. Guo, Ge; Hackebeil, Gabriel; Ryan, Sarah M.; Watson, Jean-Paul; Woodruff, David L.: Integration of progressive hedging and dual decomposition in stochastic integer programs (2015)
  12. Lubin, Miles; Dunning, Iain: Computing in operations research using Julia (2015)
  13. Held, Harald; Martinez, Gabriela; Stelzig, Philipp Emanuel: Stochastic programming approach for energy management in electric microgrids (2014)
  14. Word, Daniel P.; Kang, Jia; Akesson, Johan; Laird, Carl D.: Efficient parallel solution of large-scale nonlinear dynamic optimization problems (2014)
  15. Hart, William E.; Laird, Carl; Watson, Jean-Paul; Woodruff, David L.: Pyomo -- optimization modeling in Python (2012)
  16. Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R. R. A.: PyOpt: a python-based object-oriented framework for nonlinear constrained optimization (2012)
  17. Watson, Jean-Paul; Woodruff, David L.; Hart, William E.: PySP: modeling and solving stochastic programs in Python (2012)
  18. Hart, William E.; Watson, Jean-Paul; Woodruff, David L.: Pyomo: modeling and solving mathematical programs in python (2011) ioport