CRFS: A Decision Conflict Resolution Model Based on Human–Machine Coordination in Equipment Manufacturing

Equipment manufacturing industry plays a pivotal role in a nation's economy, national development, and technological innovation. Traditional equipment manufacturing enterprises often face the problem of untimely information sharing and inconsistent focus of various departments in the process of operation, which causes difficulties in analysis and conflict in decision-making. Considering the subjectivity of interdepartmental decisions, reinforcement learning is introduced to analyze the decisions of various departments and get the decisions that best meet the needs of the enterprise. In addition, due to the instability of the internal and external environment of the enterprise, the decision obtained only by the machine usually cannot meet the development requirements of the enterprise, so human–machine coordination is used to adapt to the environmental changes. Therefore, a decision conflict resolution for equipment manufacturing enterprises based on human–machine coordination is proposed in this article. The resolution of conflicts in the context of deep human–computer collaborative decision-making is achieved through the incorporation of expert knowledge twice within the framework of reinforcement learning, thereby expressing preferences for both production factors and decision model structures. The experiments indicate that our model achieves superior performance in resolving decision conflicts.