Background Artificial intelligence (AI) holds the promise of supporting nurses' clinical
decision-making in complex care situations or conducting tasks that are remote from direct …

Many real-life optimization problems frequently contain one or more constraints or objectives
for which there are no explicit formulae. If however data on feasible and/or infeasible states …

Nowadays, the automotive industry requires an increased use of intelligent systems to
endure. In this paper, we present a new solution for automated task scheduling to help …

The Nurse Scheduling Problem (NSP) is a well-known NP-hard combinatorial optimization
problem. Solving the NSP involves assigning feasible shift patterns to nurses, satisfying hard …

Constraint programming (CP) is used widely for solving real-world problems. However,
designing these models require substantial expertise. In this paper, we tackle this problem …

Constraint acquisition (CA) is a method for learning users' concepts by representing them as
a conjunction of constraints. While this approach works well for many combinatorial …

Abstract The Nurse Scheduling Problem (NSP) is one of the challenging combinatorial
optimization problems encountered in the healthcare sector. Solving the NSP consists in …

Solving combinatorial optimization problems involve satisfying a set of hard constraints
while optimizing some objectives. In this context, exact or approximate methods can be …

This research proposes a hybrid machine learning metaheuristic approach to create nurse
rosters in a Dutch hospital. The machine learning models predict what person is going to …

Domain-specific constraint patterns are introduced, which form the counterpart to design
patterns in software engineering for the constraint programming setting. These patterns …