Collaborative Multiobjective Decisions for Cyber-Physical Production Systems Under Time-Varying Demands

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.