The Supply Chain optimization is a classic sample in linear programming. The Microsoft Solver Foundation comes with an example on solving this problem in Excel.
The data are categorized and neatly listed out in one worksheet, while the Model pane provided interface for constraints settings. Unlike in the standard Excel Solver, the modelling is a bit more complex, but the interface helped by providing an organized and consistent user interface in Excel.
The Julia language released version 1.0 earlier this month (August 8th 2018). Julia is aimed at statistics, computation, and data science with capabilities of numerical analysis in a high level dynamic programming language. JuliaBox provides online workspace to test drive this exciting tool, as shown below an LU decomposition example.
In R, the function constrOptim provides a set of optimization routines to solve linear inequality constraint problems conveniently. Different algorithms are available, for example, to use the Nelder-Mead algorithm, just set the input parameter gradient function to null.
The Microsoft Solver Foundation (MSF) provides a rich set of optimization library and a modelling framework, all in an easy to use GUI through Excel. To make it developer friendly, MSF made available its library through API implemented in several programming languages.
Using MSF in Excel is very straight forward, thanks to its well designed user interface. For example, in the classic shipping optimization example, the model inputs are grouped in parameters, constraints, and goals, and are presented in side tabs.
Comparison of Microsoft Solver Foundation and the built-in Excel solver can be found in a previous installment.