No abstract

Time, cost, and quality are critical factors that impact the production of intelligent manufacturing enterprises. Achieving optimal values of production parameters is a complex problem known as an NP-hard problem, involving balancing various constraints. To address this issue, a workflow multi-objective optimization algorithm, based on the dynamic virtual staged pruning (DVSP) strategy, was proposed to optimize multi-stage nonlinear production processes. The algorithm establishes a virtual workflow model based on the actual production process and proposes a pruning strategy to eliminate the indirect constraint relationship between tasks. A virtual hierarchical strategy is employed to divide the task node set, and the Pareto optimal service set is calculated through backward iteration in stages. The optimal path is generated through forward scheduling, and the global optimal solution is obtained. The algorithm was compared with the minimum critical path algorithm (MCP) and the partial critical path budget balance scheduling algorithm (PCP-B2). The experimental results demonstrated that the DVSP can improve product quality, reduce production costs, and ensure production stability while completing production tasks. This paper used a pruning strategy and virtual workflow modeling methods to achieve dynamic multi-objective optimization scheduling for nonlinear feedback manufacturing processes.

Work-flow scheduling is for finding the allocation method to achieve optimal resource utilization. In the scheduling process, constraints, such as time, cost and quality, need to be considered. How to balance these parameters is a NP-hard problem, and the nonlinear manufacturing process increases the difficulty of scheduling, so it is necessary to provide an effective heuristic algorithm. Aiming at these problems, a multi-objective nonlinear virtual work-flow model was set up, and a multi-objective staged scheduling optimization algorithm with the objectives of minimizing cost and time and maximizing quality was proposed. The algorithm includes three phases: the virtualization phase abstracts tasks and services into virtual nodes to generate a virtual work-flow model; the virtual scheduling phase divides optimized segments and obtains the solution set through reverse iteration; the generation phase obtains the scheduling path according to the Pareto dominance. The proposed algorithm performed 10.5% better in production quality than the minimum critical path algorithm, reduced the time to meet the time constraint by 9.1% and saves 13.7% more of the cost than the production accuracy maximization algorithm.