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    Area of Science:

    • Industrial Engineering
    • Control Systems Engineering
    • Operations Research

    Background:

    • Cyber-physical production systems (CPPSs) offer improved responsiveness but face control challenges due to dynamic internal and external environments.
    • Effective decision-making for operation, maintenance, and support in CPPSs is crucial yet complex.
    • Time-varying demands and uncertainties necessitate advanced management strategies.

    Purpose of the Study:

    • To develop a multiobjective optimization approach for managing CPPSs.
    • To create a decision-making framework for collaborative control incorporating reliability-based risk assessment.
    • To address conflicting objectives in operation, maintenance, and support decisions under uncertainty.

    Main Methods:

    • Formulation of a biobjective optimization model using a receding horizon control architecture.
    • Development of an enhanced multiobjective pigeon-inspired optimization algorithm.
    • Integration of reliability-based risk assessment with multiobjective optimization techniques.

    Main Results:

    • The proposed approach enables continuous decision-making in response to uncertainties.
    • Pareto-optimal solutions were generated by co-minimizing production risks and costs.
    • Experimental validation was performed on a real-world subsea production system.

    Conclusions:

    • The multiobjective optimization approach effectively manages complex decisions in CPPSs.
    • The enhanced pigeon-inspired optimization algorithm successfully generated optimal solutions.
    • The framework is validated for practical application in dynamic production environments.