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

Li LH, Mo R - PLoS ONE (2015)

Bottom Line: The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model.The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value.A case study is given to illustrate its correctness and feasibility.

View Article: PubMed Central - PubMed

Affiliation: Ministry of Education Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China.

ABSTRACT
The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility.

No MeSH data available.


The relative subjective weights.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4587375&req=5

pone.0134343.g006: The relative subjective weights.

Mentions: Step 7: Repeating the steps 1–6 from top to bottom as Fig 2, the relative subjective weights of all nodes to their upper node can be obtained as shown in Fig 6.


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

Li LH, Mo R - PLoS ONE (2015)

The relative subjective weights.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4587375&req=5

pone.0134343.g006: The relative subjective weights.
Mentions: Step 7: Repeating the steps 1–6 from top to bottom as Fig 2, the relative subjective weights of all nodes to their upper node can be obtained as shown in Fig 6.

Bottom Line: The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model.The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value.A case study is given to illustrate its correctness and feasibility.

View Article: PubMed Central - PubMed

Affiliation: Ministry of Education Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China.

ABSTRACT
The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility.

No MeSH data available.