Population Learning Algorithm for the Resource-Constrained Project Scheduling

The paper proposes applying the population-learning algorithm to solving both the single-mode and the multi-mode resource-constrained pro-ject scheduling problems (denoted as RCPSP and MRCPSP, respectively) with makespan minimization as an objective function. The paper contains problem formulation and a description of the proposed implementation of the population learning algorithm (PLA). To validate the approach a computational experiment has been carried out. It has involved 1440 instances of the RCPSP and 3842 instances of the MRCPSP obtained from the available benchmark data sets. Results of the experiment show that the proposed PLA implementation is an effective tool for solving the resource-constrained project scheduling problems. In case of the RCPSP instances the algorithm in a single run limited to 50000 solutions generated has produced results close to the results of the best known algorithms as compared with average deviation from critical path. In case of the MRCPSP instances the proposed algorithm in a single run has produced solutions with mean relative error value below 1.6% as compared with optimal or best known solutions for benchmark problems.

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