Pro-Reactive Approach for Project Scheduling Under Unpredictable Disruptions.

Area of Science:

• Operations Research
• Computer Science
• Project Management

Background:

• Traditional project scheduling methods (proactive/reactive) have limitations impacting optimization performance.
• Handling disruptions in multimode resource-constrained project scheduling problems (MM-RCPSPs) remains a challenge.

Purpose of the Study:

• To develop an advanced auto-configured multioperator evolutionary approach with a novel pro-reactive scheme for MM-RCPSPs.
• To minimize project makespan while also maximizing free resources (FRs) and minimizing activity finishing time deviation.

Main Methods:

• An auto-configured multioperator evolutionary approach incorporating a new pro-reactive scheme.
• Development of a novel operator and two new heuristics to enhance evolutionary algorithm performance.
• Testing and analysis on benchmark MM-RCPSP instances.

Main Results:

• The proposed pro-reactive scheme effectively handles disruptions in MM-RCPSPs.
• The enhanced evolutionary approach demonstrates superior performance compared to existing state-of-the-art algorithms.
• Achieved high-quality solutions in terms of makespan, free resources, and activity finishing time deviation.

Conclusions:

• The novel pro-reactive evolutionary approach offers a significant advancement for MM-RCPSP optimization.
• The methodology provides a robust solution for managing project scheduling disruptions.
• The approach yields better schedule quality than current leading algorithms.