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Scientific Programming
Volume 2016 (2016), Article ID 5102616, 13 pages
Research Article

A Hybrid Programming Framework for Modeling and Solving Constraint Satisfaction and Optimization Problems

Department of Information Systems, Kielce University of Technology, 25-314 Kielce, Poland

Received 2 February 2016; Revised 25 May 2016; Accepted 21 June 2016

Academic Editor: Can Özturan

Copyright © 2016 Paweł Sitek and Jarosław Wikarek. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a hybrid programming framework for modeling and solving of constraint satisfaction problems (CSPs) and constraint optimization problems (COPs). Two paradigms, CLP (constraint logic programming) and MP (mathematical programming), are integrated in the framework. The integration is supplemented with the original method of problem transformation, used in the framework as a presolving method. The transformation substantially reduces the feasible solution space. The framework automatically generates CSP and COP models based on current values of data instances, questions asked by a user, and set of predicates and facts of the problem being modeled, which altogether constitute a knowledge database for the given problem. This dynamic generation of dedicated models, based on the knowledge base, together with the parameters changing externally, for example, the user’s questions, is the implementation of the autonomous search concept. The models are solved using the internal or external solvers integrated with the framework. The architecture of the framework as well as its implementation outline is also included in the paper. The effectiveness of the framework regarding the modeling and solution search is assessed through the illustrative examples relating to scheduling problems with additional constrained resources.