A. Hill, J. Ticktin, T. Vossen

It's commonly assumed that experience leads to efficiency, yet this is largely unaccounted for in resource-constrained project scheduling. We consider the case that selected activities can be completed within reduced time when scheduled after activities that result in learning of relevant skills. Using constraint programming, we computationally explore the effect of this autonomous learning on optimal makespan and problem difficulty across hundreds of thousands of scenarios. In this large-scale analysis, we evaluate the impact of multiple parameters such as project size, learning frequency, and learning intensity on PSPlib instances. Moreover, we compare different model formulations and lower bounding techniques with respect to their efficiencies.

Keywords: Project Scheduling, resource-constrained


TD1 Scheduling 3
June 10, 2021  2:45 PM
1 - GB Dantzig

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