Mathematical Programming Computation, Volume 5, Issue 3, September 2013

TACO: a toolkit for AMPL control optimization

Christian Kirches, Sven Leyffer

We describe a set of extensions to the AMPL modeling language to conveniently model mixed-integer optimal control problems forODEorDAE dynamic processes. These extensions are realized as AMPL user functions and suffixes and do not require intrusive changes to theAMPL language standard or implementation itself. We describe and provide TACO, a Toolkit for AMPL Control Optimization that reads AMPL files and detects the structure of the optimal control problem. This toolkit is designed to facilitate the coupling of existing optimal control software packages to AMPL.We discuss requirements, capabilities, and the current implementation. Using the example of the multiple shooting code for optimal control MUSCOD-II, a direct and simultaneous method for DAE-constrained optimal control, we demonstrate how the problem information provided by the TACO toolkit is interfaced to the solver. In addition, we show how the MS-MINTOC algorithm for mixed-integer optimal control can be used to efficiently solve mixed-integer optimal control problems modeled in AMPL. We use the AMPL extensions to model three control problem examples and we discuss how those extensions affect the representation of optimal control problems. Solutions to these problems are obtained by usingMUSCOD-II and MS-MINTOC inside the AMPL environment. A collection of further AMPL control models is provided on the web site MUSCOD-II and MS-MINTOC have been made available on the NEOS Server for Optimization, using the TACO toolkit to enable input of AMPL models

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