Study of Decision-Making Problems in the Production Process

In this paper, the decision strategy under different scenarios is studied for the decision choice problem of manufacturing enterprises in the production process. The study aims to help improve the operational efficiency and agility of enterprises through the specific analysis of the decision-making problem in the production process to provide enterprises with feasible decision support tools, improve production efficiency and resource utilization, and help intelligent manufacturing upgrade. As in the production process, the spare parts used and the synthesized finished product may be defective, and defective products to the hands of the user will have to pay a certain amount of compensation or even reduce the credibility of the enterprise. At the same time, if you choose to test the spare parts and finished products also need to pay a certain amount of testing costs. Therefore, how to better find the most profitable decision has become a major problem. In order to solve these problems, this paper divides the production situation into two categories: single process and two spare parts, and multiple processes and multiple spare parts, and assumes specific values such as product failure rate and spare part cost. In the case of single process and two spare parts, this paper constructs a decision tree model and a cost effectiveness model, and solves them by Monte Carlo (MC) simulation. In the case of multi-process and multi-part production, the production process is too complex and high-dimensional when the MC simulation method requires a large number of samples, which usually shows a lower efficiency, so we use the Slime mold algorithm (SMA) to produce results more efficiently. After experiments with specific numerical settings, it is shown that the proposed model can produce good results in many cases. This not only helps to improve the operational efficiency and agility of enterprises, but also has strategic significance in promoting the digital transformation of the manufacturing industry and realizing sustainable development.