Reza Rafeh
Combinatorial optimization problems appear in many real life applications as timetabling, planning and scheduling. However, they are often NP-hard. This means that there is no general and efficient algorithm for solving them. Modern approaches for tackling combinatorial and optimization problems divide the task into two major tasks: modeling and solving. Modelling means finding a proper formulation of the problem while solving means finding the solution of the problem. The most well-known modeling tools are: constraint programming languages, constraint libraries, (mathematical) modelling languages and specification languages. Modelling languages provide the most high-level practical level of modelling for modellers. There are some known solving techniques to tackle such problems of which the most popular ones are: mathematical methods, constraint programming and local search. Each technique has its own advantages and disadvantages and for a given problem it is unclear at the beginning which technique gives us the best result. Current modeling languages are tied to a specific solving technique. In this paper, we show how the modeling language Zinc can automatically map a conceptual model into corresponding low level model suitable for one of the aforementioned solving techniques.
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