The OptQuest Engine provides a tool to facilitate the development of applications that require the optimization of complex systems that may or may not utilize simulation. It provides user-friendly software to customers who may have limited knowledge of how optimization works yet who need access to the sophisticated optimization techniques.

References in zbMATH (referenced in 12 articles )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Lucidi, Stefano; Maurici, Massimo; Paulon, Luca; Rinaldi, Francesco; Roma, Massimo: A derivative-free approach for a simulation-based optimization problem in healthcare (2016)
  2. Ajdari, Ali; Mahlooji, Hashem: An adaptive exploration-exploitation algorithm for constructing metamodels in random simulation using a novel sequential experimental design (2014)
  3. Nikolaidis, Yiannis (ed.): Quality management in reverse logistics. A broad look on quality issues and their interaction with closed-loop supply chains (2013)
  4. Charnes, John: Financial modeling with Crystal Ball and Excel (2012)
  5. Bekker, James; Aldrich, Chris: The cross-entropy method in multi-objective optimisation: an assessment (2011)
  6. Lejeune, Miguel A.; Margot, François: Optimization for simulation: LAD accelerator (2011)
  7. Kleijnen, Jack P. C.: Design and analysis of computational experiments: overview (2010)
  8. Kleijnen, Jack P. C.; van Beers, Wim; van Nieuwenhuyse, Inneke: Constrained optimization in expensive simulation: novel approach (2010)
  9. Xu, Jie; Nelson, Barry L.; Hong, Jeff L.: Industrial strength COMPASS: a comprehensive algorithm and software for optimization via simulation (2010)
  10. Bettonvil, Bert; Del Castillo, Enrique; Kleijnen, Jack P. C.: Statistical testing of optimality conditions in multiresponse simulation-based optimization (2009)
  11. Kleijnen, Jack P. C.; Wan, Jie: Optimization of simulated systems: OptQuest and alternatives (2007) ioport
  12. Thiriez, Hervé: Improved OR education through the use of spreadsheet models (2001)