Investigating Constraint Programming and Hybrid Methods for Real World Industrial Test Laboratory Scheduling
This addresses scheduling inefficiencies for industrial test laboratories, offering incremental improvements over existing methods.
The paper tackled scheduling in industrial test laboratories, a complex real-world problem akin to RCPSP, by developing constraint programming models and a Very Large Neighborhood Search approach, finding feasible solutions for all instances and optimal solutions for several, with VLNS outperforming other methods.
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.