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Research on Multi-objective Programming of Raw Material Ordering and Transportation Process based on Genetic Algorithm

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DOI: 10.23977/csic2022.002

Author(s)

Jinming Cao

Corresponding Author

Jinming Cao

ABSTRACT

Based on the discussion of the ordering and transhipment process of suppliers' raw materials, the optimal scheme of three kinds of raw materials in ordering and transportation is studied. This paper uses the fuzzy evaluation method to evaluate the suppliers comprehensively. Then under the condition of ensuring the production demand of the enterprise, the corresponding planning model is established, and the optimal raw material ordering scheme and transfer plan are established to meet the needs of the enterprise. Then the coefficient of variation method is used to eliminate the influence of different dimensions of each index and judge the resolving power of the index. The fuzzy evaluation model is used to analyze the supply characteristics of each supplier quantitatively. On the premise of ensuring the normal production capacity demand of the enterprise, the 50 most important suppliers are selected. Then, the ordering stage and the transfer stage are considered, respectively, and the goal programming model is established, which is considered from three aspects: decision variables, objective function and constraint conditions. Firstly, the 0-1 selection matrix of whether or not to select suppliers is constructed to establish the 0-1 programming model, and the least supplier scheme is obtained, which focus on the cost problem. The cost only includes the raw material unit price factor, establishes the planning model to get the most economical raw material ordering plan in the next 24 weeks, and finally establishes the model to get the transfer plan with the least transportation loss. In the process of data processing, due to a large amount of data, the genetic algorithm is used for optimization, and the simulation results show that the scheme's effect is good.

KEYWORDS

Genetic algorithms, Fuzzy evaluation, Goal programming, Optimal scheme

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