Research on Production Decision Making Based on Greedy Algorithm and Shortest Path Model

The aim of this study is to explore production decision optimisation methods based on greedy algorithms and shortest path models, with a special focus on the impact of defective rates on firms and related decision considerations. By analysing the processing strategies such as testing and quality control of production materials, this paper establishes a binomial distribution model, a dynamic programming model and a shortest path model to optimise the enterprise structure. In the study, the binomial distribution model is firstly constructed to assess the defective rate of spare parts by applying the confidence test method in statistics, and the normal approximation is combined with the central limit theorem in order to facilitate the decision-making of sampling and testing. For multi-stage decision-making that deals with more complex production materials, dynamic planning methods are used to clarify the decision variables in each stage, and the shortest path model is combined to identify the lowest-cost path, and the optimal decision combination is finally filtered out. In addition, for the complexity of multiple processes, this paper is based on the idea of greedy algorithm to decompose the overall problem into multiple sub-problems, solving and integrating the optimal solutions of each sub-strategy one by one. The research results show that the proposed method effectively reduces the production decision-making cost and provides a feasible solution for enterprises to achieve cost minimisation and revenue maximisation.