Xpress-Mosel. Multi-solver, multi-problem, multi-model, multi-node modeling and problem solving. Xpress-Mosel, a commercial product since 2001 (originally developed by Dash Optimization, now FICO), provides a complete environment for developing, testing and deploying optimization applications. Development and analysis of optimization models written with the Mosel language is aided by the graphical environment Xpress-IVE, and tools such as the Mosel debugger and profiler. The Mosel libraries provide the neccessary functionality for a tight integration into existing (C/Java/.NET) applications for model deployment. par This chapter explains the basics of the Mosel language that are required to use the software as a modeling and solution reporting interface to standard matrix-based solvers. It also gives an overview of Mosel’s programming functionality. The open, modular design of Mosel makes it possible to extend the Mosel language according to one’s needs, adding solvers, data connectors, graphics or system functionality. The second part of this chapter presents possibilities for problem decomposition and concurrent solving from a modeling point of view, with example implementations in Mosel that show handling of multiple models, multiple problems within a model, and as a new feature, distributed computation using a heterogeneous network of computers.

References in zbMATH (referenced in 29 articles , 1 standard article )

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  1. Lutter, Pascal; Werners, Brigitte: Order acceptance for motorail transportation with uncertain parameters (2015)
  2. Gualandi, Stefano; Malucelli, Federico: Constraint programming-based column generation (2013)
  3. Cai, Yiwei; Kutanoglu, Erhan; Hasenbein, John; Qin, Joe: Single-machine scheduling with advanced process control constraints (2012)
  4. Gunnerud, V.; Foss, Bjarne A.; McKinnon, Ken I.M.; Nygreen, Bjørn: Oil production optimization solved by piecewise linearization in a branch & price framework (2012)
  5. Heipcke, Susanne: Xpress-Mosel. Multi-solver, multi-problem, multi-model, multi-node modeling and problem solving (2012)
  6. Kallrath, Josef (ed.): Algebraic modeling systems. Modeling and solving real world optimization problems. (2012)
  7. Magos, D.; Mourtos, I.; Appa, G.: A polyhedral approach to the \italldifferent system (2012)
  8. Watson, Jean-Paul; Woodruff, David L.; Hart, William E.: PySP: modeling and solving stochastic programs in Python (2012)
  9. Sztrik, J.; Kim, C.S.: Tool supported performability investigations of heterogeneous finite-source retrial queues (2010)
  10. Yunes, Tallys; Aron, Ionuţ D.; Hooker, J.N.: An integrated solver for optimization problems (2010)
  11. Colombo, Marco; Grothey, Andreas; Hogg, Jonathan; Woodsend, Kristian; Gondzio, Jacek: A structure-conveying modelling language for mathematical and stochastic programming (2009)
  12. Grothey, Andreas; Hogg, Jonathan; Woodsend, Kristian; Colombo, Marco; Gondzio, Jacek: A structure conveying parallelizable modeling language for mathematical programming (2009)
  13. Gualandi, Stefano; Malucelli, Federico: Constraint programming-based column generation (2009)
  14. Pochet, Yves; Van Vyve, Mathieu; Wolsey, Laurence A.: LS-LIB: a library of tools for solving production planning problems (2009)
  15. Bonates, T.O.; Hammer, Peter L.; Kogan, A.: Maximum patterns in datasets (2008)
  16. Sadykov, Ruslan: A branch-and-check algorithm for minimizing the weighted number of late jobs on a single machine with release dates (2008)
  17. Teixeira, João C.; Antunes, António P.: A hierarchical location model for public facility planning (2008)
  18. Alexe, Gabriela; Alexe, Sorin; Bonates, Tibérius O.; Kogan, Alexander: Logical analysis of data -- the vision of Peter L. Hammer (2007)
  19. Bockmayr, Alexander; Pisaruk, Nicolai: Detecting infeasibility and generating cuts for mixed integer programming using constraint programming (2006)
  20. Linderoth, Jeffrey T.; Ralphs, Ted K.: Noncommercial software for mixed-integer linear programming (2006)

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