Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop.

Area of Science:

• Manufacturing Engineering
• Operations Research
• Decision Science

Background:

• Production task queue optimization is crucial for manufacturing resource allocation and scheduling.
• Traditional qualitative methods for queue optimization are often ineffective and difficult to apply.

Purpose of the Study:

• To propose an advanced production task queue optimization method based on multi-attribute evaluation.
• To establish a hierarchical multi-attribute model and develop indicator quantization methods for production tasks.

Main Methods:

• Utilized Criteria Importance Through Intercriteria Correlation (CRITIC) for objective indicator weight calculation.
• Employed BP neural networks and a trapezoid fuzzy scale-rough Analytic Hierarchy Process (AHP) for subjective indicator weight determination.
• Integrated objective and subjective weights using a multi-weight contribution balance model.
• Applied an improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with relative entropy distance for task sequencing.

Main Results:

• Developed a hierarchical multi-attribute model for production task evaluation.
• Successfully integrated objective and subjective weights for a balanced approach.
• Demonstrated the effectiveness of the improved TOPSIS method in optimizing task queues.
• A case study validated the correctness and feasibility of the proposed method.

Conclusions:

• The proposed multi-attribute evaluation method offers a significant improvement for production task queue optimization.
• This approach enhances manufacturing resource allocation and scheduling decisions.
• The method provides a robust and feasible solution for complex production environments.