An Investigation to Manufacturing Analytical Services Composition using the Analytical Target Cascading Method.

Area of Science:

• Engineering
• Computer Science
• Operations Research

Background:

• Cloud computing enables modular services for complex systems.
• Manufacturing system design and performance improvement involve complex optimization problems.
• Independent optimization of sub-problems can lead to suboptimal system-level solutions.

Purpose of the Study:

• To investigate Analytical Target Cascading (ATC) for coordinating optimization of loosely-coupled sub-problems.
• To demonstrate ATC's capability in achieving system-level optimal solutions.
• To explore ATC's scalability and ease of problem formulation management.

Main Methods:

• Formulating sub-problems as modular analytical services.
• Employing Analytical Target Cascading (ATC) for inter-service coordination.
• Utilizing distributed and modular execution for optimization.

Main Results:

• ATC effectively coordinates the optimization of modular services.
• The method yields system-level optimal solutions.
• ATC facilitates scalable, distributed problem-solving.

Conclusions:

• Analytical Target Cascading (ATC) is a promising technique for manufacturing system optimization.
• ATC enables optimal solutions by coordinating modular services.
• The approach supports scalable, distributed execution and simplifies problem management.