Conference Proceedings
Stochastic MiniZinc
Andrea Rendl, Guido Tack, Peter J Stuckey, B OSullivan
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | SPRINGER INT PUBLISHING AG | Published : 2014
Abstract
Combinatorial optimisation problems often contain uncertainty that has to be taken into account to produce realistic solutions. However, existing modelling systems either do not support uncertainty, or do not support combinatorial features, such as integer variables and non-linear constraints. This paper presents an extension of the MINIZINC modelling language that supports uncertainty. Stochastic MINIZINC enables modellers to express combinatorial stochastic problems at a high level of abstraction, independent of the stochastic solving approach. These models are translated automatically into different solver-level representations. Stochastic MINIZINC provides the first solving technology ag..
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