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Academic Journal of Business & Management, 2022, 4(5); doi: 10.25236/AJBM.2022.040505.

Multi-objective Optimization Study for Enterprise Supply and Transit Problems


Jianfei Cui1, Yang Cai2, Biao Liang1

Corresponding Author:
Jianfei Cui

1School of Business Administration, Liaoning Technical University, Huludao, Liaoning, 125105, China

2School of Software, Liaoning Technical University, Huludao City, Liaoning, 125105, China


This paper focuses on the ordering and transportation of raw materials for companies. This paper establishes a logistic regression equation to fit the relationship between supply quantity and order quantity based on the supply characteristic index system. In this paper, a multi-objective BP neural network model is established to solve the supply solution with the lowest purchase and storage cost, the highest supplier reliability score, as the objective function. A gray prediction model is built based on the past loss rate data of the forwarder to obtain a prediction formula to predict the loss rate. Finally, a linear programming model is built to solve for the optimal operation solution to meet the demand based on the predicted loss rate.


Logistic Regression; BP Neural Network Model; Gray Prediction

Cite This Paper

Jianfei Cui, Yang Cai, Biao Liang. Multi-objective Optimization Study for Enterprise Supply and Transit Problems. Academic Journal of Business & Management (2022) Vol. 4, Issue 5: 21-24. https://doi.org/10.25236/AJBM.2022.040505.


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