Investigating Constraint Programming and Hybrid Methods for Real World
  Industrial Test Laboratory Scheduling
  In this paper we deal with a complex real world scheduling problem closely
related to the well-known Resource-Constrained Project Scheduling Problem
(RCPSP). The problem concerns industrial test laboratories in which a large
number of tests has to be performed by qualified personnel using specialised
equipment, while respecting deadlines and other constraints. We present
different constraint programming models and search strategies for this problem.
Furthermore, we propose a Very Large Neighborhood Search approach based on our
CP methods. Our models are evaluated using CP solvers and a MIP solver both on
real-world test laboratory data and on a set of generated instances of
different sizes based on the real-world data. Further, we compare the exact
approaches with VLNS and a Simulated Annealing heuristic. We could find
feasible solutions for all instances and several optimal solutions and we show
that using VLNS we can improve upon the results of the other approaches.
