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    This study introduces a new pro-reactive approach for project scheduling disruptions, improving optimization for multimode resource-constrained project scheduling problems (MM-RCPSPs) and delivering superior schedule quality.

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    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.