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Academic Journal of Business & Management, 2022, 4(2); doi: 10.25236/AJBM.2022.040214.

Capital Turnover Model Based on Genetic Algorithm and Multi-objective Programming

Author(s)

Yiming Zhao, Yunjing Zhang, Yuxiang Zhang

Corresponding Author:
Yiming Zhao
Affiliation(s)

Qingdao Institute of Software, School of Computer Science and Technology, China University of Petroleum, Qingdao, China

Abstract

A capital turnover model is established and three main problems are solved. First, select the suppliers that guarantee the production of the enterprise at least as much as needed. Second, develop the most economical material ordering scheme and conduct the scheme analysis. Third, develop the lowest loss material transfer scheme and conduct the scheme analysis. All three problems are planning problems. First, we construct a 0-1 planning model. Then, based on the principle of the lowest total purchase price, we build an objective planning model for the ordering scheme, obtain the most economical ordering scheme by genetic algorithm, and compare it with the least economical scheme and random scheme. Moreover, following the principle of lowest transit loss, we build an objective planning model of transportation scheme based on the most economical ordering solution and compare and analyze with the solution with the highest loss and random solution.

Keywords

0-1 Planning; Genetic Algorithm; Goal Planning

Cite This Paper

Yiming Zhao, Yunjing Zhang, Yuxiang Zhang. Capital Turnover Model Based on Genetic Algorithm and Multi-objective Programming. Academic Journal of Business & Management (2022) Vol. 4, Issue 2: 80-83. https://doi.org/10.25236/AJBM.2022.040214.

References

[1] Hu YH. Improvement of TOPSIS method for comprehensive evaluation[J]. Mathematical Practice and Understanding, 322002, (4): 572-575.

[2] Ni Jiupai, Li Ping, Wei Chaofu, et al. Evaluation of regional land development potential based on AHP and entropy weighting method [J]. Journal of Agricultural Engineering, 2009, 025(005): 202-209.

[3] Ji Genlin. A review of genetic algorithm research [J]. Computer Applications and Software, 212004, (2): 69-73.