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Zinc ore supplier evaluation and recommendation method based on nonlinear adaptive online transfer learning

  • * Corresponding author: Yonggang Li

    * Corresponding author: Yonggang Li 

The first author is supported by NNSFC grant 61973321

Abstract Full Text(HTML) Figure(16) / Table(1) Related Papers Cited by
  • Purchasing decisions determine the purchasing cost, which is the largest section of the production cost of zinc smelting enterprise(ZSE). An excellent supplier recommendation is significant for ZSE to reduce the cost. However, during the supplier recommendation process, the nonlinear demand feature of purchasing department varies with the production environment, and there are wrong samples that can affect the supplier recommendation effect. To handle these problems, the recommendation strategy based on a multiple-layer perceptron adaptive online transfer learning algorithm(AOTLMLP) are proposed. In this method, the original prediction function is modified based on MLP nonlinear projective function and adaptive loss function, which enables the AOTLMLP algorithm to tackle the nonlinear classification problems and efficiently follow the demand change of purchasing department, thereby improving the result of the recommendation. The performance of the AOTLMO algorithm is evaluated through a common dataset and a purchasing dataset from a zinc smelter that generated by a supplier evaluation model. It can be assumed that AOTLMLP can ignore the influence of wrong samples and provide an effective recommendation confronting the characteristic of zinc ore purchasing.

    Mathematics Subject Classification: Primary: 68W27; Secondary: 90B06.


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  • Figure 1.  The framework of evaluation criterias for zinc ore suppliers

    Figure 2.  The requirement change problem in supplier recommendation

    Figure 3.  The structure of nonlinear prediction function based on MLP

    Figure 4.  The update steps of algorithm

    Figure 5.  Cumulative trainning error rate for different $ \beta $, considering IMAGE dataset

    Figure 6.  Cumulative trainning error rate for different $ \beta $, considering Purchasing dataset

    Figure 7.  Cumulative trainning error rate for different layer parameter, considering IMAGE dataset

    Figure 8.  Cumulative trainning error rate for different layer parameter, considering Purchasing dataset

    Figure 9.  Cumulative trainning error rate for different $ \eta $, considering training purchasing dataset

    Figure 10.  Cumulative trainning error rate for different $ \eta $, considering testing purchasing dataset

    Figure 11.  Recommendation accuracy for different $ \eta $, for purchasing demand change

    Figure 12.  Cumulative trainning error rate for different $ \varphi $, considering training purchasing dataset

    Figure 13.  Cumulative trainning error rate for different $ \varphi $, considering testing purchasing dataset

    Figure 14.  Recommendation accuracy for different $ \varphi $, for purchasing demand change

    Figure 15.  The convergence behaviors for different learning strategy, considering purchasing dataset

    Figure 16.  The performance of four algorithms, considering purchasing demand change

    Table 1.  Symbol reference table

    Symbol Paraphrase
    $ {{\bf{x}}_t} $ Supplier feature vector
    $ {y_t} $ Recommendation outcome
    $ {\bf{v}} $ Linear previous demand feature vector
    $ {{\bf{w}}_t} $ Linear present demand feature vector(time-varying)
    $ {{\bf{v}}_\phi } $ Nonlinear previous demand feature matrix
    $ {{\bf{w}}_{\phi t}} $ Nonlinear present demand feature matrix(time-varying)
    $ {{\bf{z}}_t} $ The hidden layer node vector(time-varying)
    $ {{\bf{z}}_{(j)t}} $ The jth hidden layer node vector(time-varying)
    $ {{\bf{r}}_t} $ The weight vector for ReLU units(time-varying)
    $ {{\bf{r}}_{(j)t}} $ The jth layer weight vector for ReLU units(time-varying)
    $ \beta $ The restriction parameter
    $ \varphi $ The preference parameter
    $ \eta $ The transfer speed rate
     | Show Table
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