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The Weight of Resources

In: Data-Driven Project Management with Python

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  • Mario Vanhoucke

    (University of Ghent, Faculty of Economics and Business)

Abstract

This chapter extends the simulation of projects of the previous chapter to resource constraints, providing a structured approach to understanding schedule risk in more realistic project environments. In Sect. 5.1, the challenges of incorporating limited resources into project simulations are introduced, highlighting how resource conflicts complicate schedule propagation and critical activity identification. Section 5.2 presents Experiments 7 and 8, detailing the Monte Carlo simulation framework adapted for resource-constrained project schedules, including baseline schedule construction, activity uncertainty modeling, scheduling policies, and critical chain detection. Section 5.3 summarizes the key findings, showing how resource-constrained simulations confirm some insights from resource-free models while revealing new dynamics in activity sensitivity, resource utilization, and project control. Finally, Sect. 5.4 briefly discusses a calibration approach that uses past project data to improve the realism of simulations and touches on the potential benefits of artificial intelligence for enhancing schedule risk analysis and predictive accuracy.

Suggested Citation

  • Mario Vanhoucke, 2026. "The Weight of Resources," Management for Professionals, in: Data-Driven Project Management with Python, chapter 5, pages 69-85, Springer.
    • Handle: RePEc:spr:mgmchp:978-3-032-24556-4_5
    DOI: 10.1007/978-3-032-24556-4_5
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