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 37 articles , 1 standard article )

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  1. Avella, P.; Boccia, M.; Mattia, S.; Rossi, F.: Weak flow cover inequalities for the capacitated facility location problem (2021)
  2. Molnár-Szipai, Richárd; Varga, Anita: Integrating combinatorial algorithms into a linear programming solver (2019)
  3. Kersting, Kristian; Mladenov, Martin; Tokmakov, Pavel: Relational linear programming (2017)
  4. Kilincli Taskiran, Gamze; Zhang, Xinhui: Mathematical models and solution approach for cross-training staff scheduling at call centers (2017)
  5. Lutter, Pascal; Werners, Brigitte: Order acceptance for motorail transportation with uncertain parameters (2015)
  6. Sauvey, Christophe; Sauer, Nathalie; Trabelsi, Wajdi: Job-shop scheduling with mixed blocking constraints between operations (2015)
  7. Cai, Yiwei; Hasenbein, John J.; Kutanoglu, Erhan; Liao, Melody: Single-machine multiple-recipe predictive maintenance (2013)
  8. Gualandi, Stefano; Malucelli, Federico: Constraint programming-based column generation (2013)
  9. Cai, Yiwei; Kutanoglu, Erhan; Hasenbein, John; Qin, Joe: Single-machine scheduling with advanced process control constraints (2012)
  10. Gunnerud, V.; Foss, Bjarne A.; McKinnon, Ken I. M.; Nygreen, Bjørn: Oil production optimization solved by piecewise linearization in a branch & price framework (2012)
  11. Heipcke, Susanne: Xpress-Mosel. Multi-solver, multi-problem, multi-model, multi-node modeling and problem solving (2012)
  12. Kallrath, Josef (ed.): Algebraic modeling systems. Modeling and solving real world optimization problems. (2012)
  13. Magos, D.; Mourtos, I.; Appa, G.: A polyhedral approach to the \textitalldifferentsystem (2012)
  14. Watson, Jean-Paul; Woodruff, David L.; Hart, William E.: PySP: modeling and solving stochastic programs in Python (2012)
  15. Sztrik, J.; Kim, C. S.: Tool supported performability investigations of heterogeneous finite-source retrial queues (2010)
  16. Yunes, Tallys; Aron, Ionuţ D.; Hooker, J. N.: An integrated solver for optimization problems (2010)
  17. Akartunalı, Kerem; Miller, Andrew J.: A heuristic approach for big bucket multi-level production planning problems (2009)
  18. Colombo, Marco; Grothey, Andreas; Hogg, Jonathan; Woodsend, Kristian; Gondzio, Jacek: A structure-conveying modelling language for mathematical and stochastic programming (2009)
  19. Grothey, Andreas; Hogg, Jonathan; Woodsend, Kristian; Colombo, Marco; Gondzio, Jacek: A structure conveying parallelizable modeling language for mathematical programming (2009)
  20. Gualandi, Stefano; Malucelli, Federico: Constraint programming-based column generation (2009)

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