Robust optimization for multi-project scheduling via the critical chain method.

Area of Science:

• Operations Research
• Project Management
• Computational Intelligence

Background:

• Critical chain method (CCM) enhances single-project scheduling robustness.
• Applying CCM to multi-project scheduling faces challenges in robustness measurement and algorithm flexibility.

Purpose of the Study:

• To adjust the critical chain multi-project scheduling model for improved robustness.
• To propose a novel robustness measure considering elasticity within and among sub-projects.
• To design an enhanced discrete differential evolution (DE) algorithm for multi-project scheduling.

Main Methods:

• Incorporated drum buffer and capacity constraint buffer into the CCM.
• Developed a robustness measure assessing time elasticity across sub-projects.
• Designed a discrete DE algorithm with discretized operators and hill-climbing for enhanced local search.

Main Results:

• The enhanced discrete DE algorithm improved robustness by over 3.3% compared to benchmark algorithms.
• The proposed robustness measure enhanced scheduling plan stability.
• Reduced buffer consumption and overflow in multi-project scheduling.

Conclusions:

• The adjusted CCM with the new robustness measure and discrete DE algorithm effectively addresses multi-project scheduling challenges.
• The approach significantly improves scheduling robustness and resource management.
• This method offers a more stable and efficient solution for complex project environments.