Augmenting Constraint Programming Variable Selection with Domain-Specific Heuristics for a Prize-Collecting Scheduling Problem
N. Petrov (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Emir Demirović – Coach (TU Delft - Algorithmics)
Imko Marijnissen – Graduation committee member (TU Delft - Algorithmics)
M.L. Flippo – Mentor (TU Delft - Algorithmics)
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Abstract
This paper investigates the inclusion of domain-specific variable selection heuristics
in Constraint Programming (CP) solvers for the Prize-Collecting Job Sequencing
with One Common and Multiple Secondary Resources (PC-JSOCMSR) problem. We
propose two variable selection heuristics: a greedy variable selection method based on
densities, Highest Density First (HDF), and a modified Variable State Independent
Decaying Sum (VSIDS) initialized with job densities, referred to as VSIDS + Density.
Experimental results on benchmark instance sets reveal that the proposed heuristics
do not outperform the baseline VSIDS heuristic. Overall, they lead to higher conflict
counts and slower convergence. These findings highlight the robustness of generalpurpose
heuristics like VSIDS in diverse problem instances. Future research should
explore other domain-specific heuristics, as the current experiment demonstrates that
the proposed heuristics do not improve performance.