：Dynamically optimizing the time quality cost of the manufacturing process is an NP problem. This paper proposes a three-layer workflow scheduling model optimization algorithm Three-WSOA for this hot issue. The algorithm first establishes a three-layer isomorphic workflow decision-making model based on the partial order relationship of the manufacturing process, and secondly calculates the degree of freedom of each layer position node according to the proposed recognition rules, and then uses the serial reduction algorithm to process it from back to front to calculate the optimal configuration solution for each layer. Finally, forward scheduling is used to complete the multi-objective optimization of the entire three-layer workflow model. Simulation comparison found that the optimization algorithm can indeed achieve a dynamic balance between production quality and cost within a limited time, and it has a significant optimization effect compared to the traditional single-objective optimization algorithm. Therefore, the algorithm has certain feasibility and effectiveness.

Time, cost, and quality are the main factors affecting industrial production, and balancing various constraints to make the production parameters reach the optimal value is an NP-hard problem. With the progress of process production, the production process of various products is more refined, and in the actual production process, the production process has the characteristics of multi-stage and parallel proceeding, therefore, for the difficult problem of multi-stage and non-linear production process optimization, this paper proposes a workflow multi-objective optimization algorithm ( DVSP) based on dynamic virtual phased pruning strategy, which is divided into three stages, firstly, eliminating the indirect constraint relationship between tasks by pruning strategy and simplifying directed acyclic graph (DAG), secondly, dividing the task node set by virtual hierarchical strategy and generating virtual nodes by using virtualization technology, and finally, calculating the stage in stages by an inverse reductive contracting optimal set of service nodes, forward scheduling generates the optimal path, and the global optimal solution is sought through algorithmic integration. After experimental comparison, the algorithm has a more obvious optimization effect than the traditional critical path algorithm and mass maximum algorithm, which meets the actual production needs.