Description

Problem:
In the era of Industry 4.0, smart manufacturing systems are revolutionizing production processes through the integration of advanced digital technologies. One promising approach to enhance these systems is the use of Digital Twins (DTs), which are virtual replicas of physical assets and systems that enable real-time monitoring, diagnostics, and prognostics. However, the complexity of modern manufacturing environments poses significant challenges in accurately modeling and simulating these systems. Traditional methods often fall short in capturing the dynamic interactions and behaviors of various components within these systems. Agent-based modeling and simulation (ABMS) offers a potential solution by representing individual entities and their interactions within the manufacturing system. Integrating ABMS with DTs could provide a more comprehensive and adaptive approach to modeling and optimizing smart manufacturing systems. Despite its potential, this integration remains underexplored, necessitating a thorough investigation into its feasibility and effectiveness.

Goal:

• Literature Review: Conduct an extensive literature review to identify and analyze current methodologies, tools, and frameworks used for Digital Twins and Agent-based Modeling and Simulation in smart manufacturing systems.
• Identify Challenges and Gaps: Highlight the main challenges and gaps in existing research and applications related to the integration of ABMS within DTs for smart manufacturing systems.

• Framework Proposal: Propose a conceptual framework for integrating ABMS into DTs, outlining key components, interactions, and potential benefits for smart manufacturing systems.

• Future Directions: Suggest future research directions and potential improvements for the integration of ABMS within DTs, aiming to enhance the efficiency, adaptability, and resilience of smart manufacturing systems.

Required Skills and Knowledge: 

• Digital Twin Technology: Knowledge of creating and using digital twins for system modeling and simulation. 
• Familiarity with general modeling and simulation methodologies, including discrete-event simulation, system dynamics, and other relevant approaches.
• Smart Manufacturing Systems: Familiarity with the components, technologies, and processes involved in smart manufacturing, including IoT, automation, and data analytics.