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ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

Solver Independent Modelling of Combinatorial and Optimization Problems

Reza Rafeh*

Department of Computer Engineering, Islamic Azad University, Malayer Branch, Malayer, Iran

*Corresponding Author:
Dr. Reza Rafeh
Department of Computer Engineering
Islamic Azad University
Malayer Branch, Malayer, Iran
E-mail: [email protected] yahoo.com

Received: October 24, 2010; Accepted: November 09, 2010; Published: November 11, 2010

Citation: Rafeh R (2010) Solver Independent Modelling of Combinatorial and Optimization Problems. J Comput Sci Syst Biol 3:086-088. doi: 10.4172/jcsb.1000063

Copyright: © 2010 Rafeh R. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

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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