Liang Gao1, Lili Cheng2, Yanxiang Wang3, Zhenwei Ma4
1College of Mathematics and Computer Science, Yan’an University, Yan’an, China
2School of Economics and Management, Yan’an University, Yan’an, China
3Medical School of Yan’an University, Yan’an University, Yan’an, China
4School of Marxism, Yan’an University, Yan’an, China
This paper analyzes the problem of ordering and transporting raw materials for production in an enterprise, and uses a linear programming model to solve the following problems: (1) The problem that suppliers cannot supply goods according to demand is solved. We use the TOPSIS evaluation model to weight each supplier according to different indicators to derive the importance value of suppliers and select 50 suppliers that meet the requirements. We analyze the data and calculate the minimum number of suppliers to supply raw materials to meet the demand of production, and construct a linear programming model to calculate the number of suppliers with the lowest cost under the condition of guaranteed capacity. (2) To solve the problem of certain loss of raw materials during transportation, we first consider the ordering quantity, price, transportation loss in transit, and storage price of raw materials; then we analyze the data and construct a multi-objective planning model, and find the final ordering plan and transit plan through a large amount of data analysis; finally, we analyze the feasibility of the plan. (3) In order to maximize the production capacity of the enterprise, the maximum supply quantity of the supplier is obtained through analysis, and the ordering scheme is determined by combining the supplier's past performance; the transshipment scheme is determined according to the ordering scheme and the transshipment loss rate.
Ordering Scheme, Transit Scheme, Planning Model, TOPSIS Method
Liang Gao, Lili Cheng, Yanxiang Wang, Zhenwei Ma. Mathematical Model for Ordering and Transporting Raw Materials for Production Companies. Academic Journal of Mathematical Sciences (2022) Vol. 3, Issue 2: 48-56. https://doi.org/10.25236/AJMS.2022.030208.
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