Multi-Objective Workflow Optimization Algorithm Based on a Dynamic Virtual Staged Pruning Strategy

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.