NP
N. Petrov
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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 general purpose 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.
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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 general purpose 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.