Dynamic Global Constraints: A First View

TL;DR

This paper discusses the importance of global constraints in solving large combinatorial problems and introduces a dynamic version of the alldifferent constraint to handle evolving problem instances.

Contribution

It presents the concept of dynamic global constraints and specifically introduces a dynamic version of the alldifferent constraint for advanced planning and scheduling.

Findings

Global constraints improve filtering efficiency in combinatorial problems.

Dynamic global constraints can adapt to changing problem variables.

A dynamic alldifferent constraint is proposed for flexible problem modeling.

Abstract

Global constraints proved themselves to be an efficient tool for modelling and solving large-scale real-life combinatorial problems. They encapsulate a set of binary constraints and using global reasoning about this set they filter the domains of involved variables better than arc consistency among the set of binary constraints. Moreover, global constraints exploit semantic information to achieve more efficient filtering than generalised consistency algorithms for n-ary constraints. Continued expansion of constraint programming (CP) to various application areas brings new challenges for design of global constraints. In particular, application of CP to advanced planning and scheduling (APS) requires dynamic additions of new variables and constraints during the process of constraint satisfaction and, thus, it would be helpful if the global constraints could adopt new variables. In the paper, we give a motivation for such dynamic global constraints and we describe a dynamic version of the well-known alldifferent constraint.