Efficient priority rules for the stochastic resource-constrained project scheduling problem

In this study we examine the performance of 17 priority rule heuristics and the justification technique on the stochastic resource-constrained project scheduling problem (SRCPSP). Among the 17 priority rules, 12 are selected from the literature that is addressing the deterministic resource-constrained project scheduling problem (RCPSP), and the other 5 are newly designed, based on stochastic information of the SRCPSP. We evaluate the efficiency of the 17 priority rules on the benchmark data set PSPLIB, and analyze the impact of the project characteristics that were used to create this data set. Our computational results on large size instances show that the best priority rule for the RCPSP does not perform best for the SRCPSP. The best priority rule for the SRCPSP performs as well as the best meta-heuristic when the variance of the activity duration is medium, and outperforms all existing algorithms when this variance is high. The validity of justification on the SRCPSP depends on the priority rule and the activity duration variance. The project characteristics network complexity and resource factor do not influence the choice of the best priority rule, but resource strength does. Our research results can aid managers to schedule project activities more efficiently when facing uncertainties.