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Introduction and Key BenefitsWelcome to Frontline Systems' RASON^{TM} modeling language. RASON is a mini-language you can use to quickly and easily create and solve optimization and simulation/risk analysis models. RASON is compatible with Windows and Linux desktops and servers but is especially useful if you are building Web or mobile applications. RASON stands for Restful Analytic Solver Object Notation. It offers many benefits compared to using a traditional modeling language, using Excel to create analytic models or writing analytic models in a programming language. If you have ever used a modeling language to build an optimization or simulation model, you'll find the RASON language to be simple but powerful and expressive and integrating RASON models into a larger application, especially a web or mobile app, is much easier than with other modeling languages. If you have used Excel for optimization or simulation, you'll find that it's easy to translate Excel models into RASON models, that your knowledge of Excel formulas and functions is immediately usable, but that RASON models can be more flexibly "bound" to data from a variety of sources. If you've ever programmed the Solver SDK Platform in a language such as .NET or C++, you'll quickly find that using the RASON tools is much faster/more productive than writing models entirely in code. This is true especially if you are using JavaScript and you are familiar with AJAX and REST API's. You'll find it's exceptionally easy to embed RASON models in your code - since RASON is JSON - and to solve them using Frontline's RASON server. This server, which exposes a simple REST API, is free for small models and experimentation, yet scalable to handle very large, compute-intensive analytic models. Problems you can solve with the RASON server include linear programming and mixed-integer programming problems, quadratic programming and second-order cone problems, nonlinear and global optimization problems, problems requiring genetic algorithm and tabu search methods - from small to very large (LP/MIP models with millions of variables). You can also solve Monte Carlo simulation / risk analysis problems, and create and solve models with uncertainty, using simulation optimization, robust optimization, and stochastic programming methods. |