Virtual Factory Framework for Supporting Production Planning and Control

Area of Science:

• Operations Research
• Manufacturing Engineering
• Computer Science

Background:

• Dynamic changes in shop floor events complicate optimal production planning in smart manufacturing systems.
• Effective decision-making requires timely and accurate performance data across various control levels.

Purpose of the Study:

• To describe a virtual factory platform designed for dynamic production planning in smart manufacturing.
• To detail the verification and validation processes for the platform's constituent models.

Main Methods:

• Development of a virtual factory platform integrating engineering tools, simulation, and optimization.
• Implementation of data generation mechanisms for production planning.
• Verification and validation of simulation and optimization models.

Main Results:

• The virtual factory platform successfully generates performance data relevant for production planning.
• A case study on a machine shop demonstrates the platform's practical application.
• The platform supports informed decision-making for dynamic shop floor management.

Conclusions:

• The proposed virtual factory platform is a viable solution for addressing dynamic production planning challenges in smart manufacturing.
• Effective integration of simulation, optimization, and engineering tools enhances manufacturing system performance.
• Verification and validation are crucial for ensuring the reliability of virtual factory models.