Abstract
Practical project scheduling problems usually have uncertain data. Considering the fuzzy nature of data in uncertain problems, this paper discusses a Resource-Constrained Project Scheduling Problem (RCPSP). For a robust satisfying project scheduling model, an artificial immune algorithm is modified to solve the scheduling model. For each evolution iteration, population individuals are improved by genetic operators to get the next generation. For the sake of keeping the convergence speed of the algorithm, a simulated-annealing operator is performed at the end of evolution iteration. An extensive experiment was conducted and the computational results show that the Modified Artificial Immune Algorithm (MAIA) is effective and outperforms a Genetic Algorithm (GA) and an Artificial Immune Algorithm (AIA).
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