Research on Multi-Criteria Decision Making Based on Greedy Algorithm and Genetic Algorithm

The purpose of this paper is to explore how to construct a multi-criteria decision-making model using greedy algorithm and genetic algorithm to optimize the decision-making problem in the production process of enterprises. For the assembly process of parts in actual production, this paper proposes an optimization strategy based on multi-criteria decision-making, which combines the greedy algorithm and genetic algorithm to solve the decision-making problem at each stage. First, for the problem of multi-process production process, the complex multi-process problem is transformed into multiple single-process sub-problems for step-by-step solution. Then, in each stage, the maximum profit value of each process is calculated by greedy algorithm and the optimal solution is obtained by loop iteration. In addition, the results of the greedy algorithm are further optimized by combining the genetic algorithm, and the consistency and feasibility of the two algorithms in the decision-making results are verified. Finally, the paper optimizes the decision-making model and proposes the optimal strategy through multiple simulations and normality tests [1], which further improves the overall efficiency of the production system. The mathematical problem of multi-process quality inspection explored in this paper is inherently complex, involving multiple influences and correlations between processes, making it difficult to trace the root cause of quality. The modeling process is challenged by the variety and volume of data and the need to iteratively adjust the model and capture correlations between processes. The complexity and resource consumption of dealing with large amounts of data, as well as the uncertainty and risk factors involved in computational analysis, make it difficult to optimize and control, requiring a balance between quality, cost, and efficiency. The research employs a variety of technological tools and methods to cope with these difficulties, such as data mining, machine learning, and optimization algorithms, in order to achieve integrated control and optimization of multi-process production processes. The results of the study provide theoretical basis and methodological support for the application of enterprises in complex production decision-making problems.