1 Introduction
As one of the important components of a companys production process, the formulation of a supply plan of raw materials plays an important role in ensuring the normal operation of the production and supplying of goods. With the development of modern society and the economy, the control of raw material supply has become more and more automatic. The formulation of a supply plan of raw material is an important issue that needs to be addressed, and the formulation and optimization of a raw material supply planning model can further improve the efficiency and flexibility of raw material supply. In view of the characteristics of the raw material supply production process and the existing problems in the raw material supply planning and scheduling, this paper proposes a mixed model of raw material supply planning for an automatic production of a raw material yard to analyze the different effects of different parameters on the basic performance index of the raw material supply planning, and propose optimization solutions.
2 Research Objectives
The goal of this research is to build a mixed-model algorithm for a raw material yard automatic production to generate a raw material supply planning. Based on the characteristics of the raw material supply and production process and the requirements of different decision makers, this research studies the performance of different parameters of the proposed algorithm and further builds an optimization model of the algorithm to improve the efficiency and flexibility of the supply planning.
3 Literature Review
With the development of modern computer technology and the popularity of artificial intelligence technology, the application of linear programming methods and the optimization model of optimization algorithms have been widely applied to the field of logistics supply planning. In recent years, a number of studies have been conducted on the formulation of supply planning models in the field of supply chain management and logistics management.
For example, Garza-Reyes et al. (2014) studied a multi-item logistics supply planning model under the influence of transportation. The model was formulated by introducing demand distribution, purchase cost and transportation costs, and used a linear programming approach to optimize the total cost of supply planning. Zhu and Cao (2015) proposed a supply planning model for multi-product and multi-period based on fuzzy optimization. This model takes into account the uncertainty of the parameters and aims to optimize the indicators such as the cost of inventory, transportation and purchasing.
4 Research Methodology
In order to form a mixed model of raw material supply planning for an automatic production of a raw material yard, a mixed-method approach was adopted. The mixed-method approach includes both qualitative and quantitative methods for exploring the research topic.
First, a survey of the raw material supply and production process was conducted with stakeholders, including the raw material yard managers and the internal experts. Through interviews, specific parameters and criteria that need to be considered in the formulation of a supply plan were identified.
Based on the analysis of the interview results, a linear programming model was then adopted to efficiently optimize the raw material supply planning. Linear programming optimization is a commonly used mathematical optimization method that can effectively model many complex problems in the supply chain field and can obtain efficient solutions.
Finally, the performance of the linear programming model was evaluated and optimized based on the customer feedback and simulation results.
5 Results and Findings
Based on the survey results of raw material yard and the linear programming model, a mixed model for raw material supply planning for an automatic production was established. This model takes into account the cost of inventory and the customers demand, and optimizes the total cost of supply planning of the raw material yard.
The model was simulated in a simulated environment and the performance indicators, including the total inventory cost, the average customer service ratio and the mean customer waiting time, were compared under different parameters.
The simulation results showed that the delivery time plays an important role in the model. The mean waiting time of customers can be significantly reduced as the delivery time increases. Meanwhile, the average customer service ratio also increased significantly with an increased delivery time. It is found that when the delivery time is close to the mean customers waiting time, the total inventory cost will increase significantly, but the increase of the average customer service ratio and the mean customers waiting time will decrease.
6 Conclusion
The mixed model proposed in this paper provides a better solution for raw material supply planning for an automatic production of a raw material yard. By adjusting the models parameters, the performance of the model can be improved and the efficiency and flexibility of the raw material supply can be increased. The simulation results also showed that the delivery time is an important factor in the optimization of the total cost of raw material supply, and the total inventory cost, the average customer service ratio, and the mean customer waiting time have a significant positive correlation with the delivery time.
In the future, it is necessary to further study the optimization of the parameters of the model through simulation and experimental research to enhance the accuracy and effectiveness of the model. In addition, the study of other factors such as the cost of logistics, the influence of multiple production sites and the optimization of the problem of dynamic change of customer demand should also be carried out to improve the optimization of raw material supply planning and scheduling.
