Abstract
Real-world combinatorial optimization problems have two main characteristics which make them difficult: they are usually large, and they are not pure, i.e., they involve a heterogeneous set of side constraints. Hence, in most cases, exact approaches cannot be applied to solve real-world problems, whereas incomplete algorithms, and among them Local Search and Metaheuristic methods, have proved to obtain very good results in practice. Moreover, real-world applications typically lead to frequent update/addition of constraints, thus the algorithmic ap-proach requires flexibility, and this flexibility can be guaranteed by Constraint Programming.
In this chapter we review hybrid algorithms combining Local Search and Con-straint Programming using a didactic transportation problem to illustrate the tech-niques.
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Focacci, F., Laburthe, F., Lodi, A. (2004). Local Search and Constraint Programming. In: Milano, M. (eds) Constraint and Integer Programming. Operations Research/Computer Science Interfaces Series, vol 27. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8917-8_9
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