Research on the Impact of Scheduling Efficiency on Production Costs in Pharmaceutical Intelligent Manufacturing Workshops Based on Improved Particle Swarm Optimization Algorithm

Overview

This research addresses workshop scheduling optimization in pharmaceutical intelligent manufacturing through development of a hybrid particle swarm optimization algorithm. The study integrates three algorithmic mechanisms—elite learning, dynamic inertia weight adjustment, and spiral contraction search strategy—to enhance solution quality and convergence performance. A multi-objective optimization model was constructed incorporating pharmaceutical-specific constraints including batch tracing, cleaning validation, and quality inspection requirements, with dual objectives of minimizing completion time and production costs.

Methods and approach

The research employs a hybrid particle swarm optimization approach combining elite learning mechanisms with dynamic inertia weight adjustment and spiral contraction search strategies. A multi-objective optimization model was developed to address pharmaceutical manufacturing constraints specific to batch tracing, cleaning validation, and quality inspection processes. Validation utilized actual production data from a large pharmaceutical manufacturing enterprise. The analytical framework incorporated statistical significance testing and ablation studies to assess algorithm effectiveness and component contributions. Sensitivity analysis was conducted to evaluate algorithm robustness and parameter stability across varying conditions.

Key Findings

Implementation of the hybrid algorithm achieved measurable improvements in manufacturing performance metrics: equipment utilization increased by 20.1%, average flow time decreased by 18.1%, on-time delivery rate reached 91.5%, and total production costs reduced by 6.3%. Cost component analysis revealed energy costs decreased by 14.9% and inventory costs by 36.8%. The analysis identified a differential impact pattern wherein scheduling efficiency produced substantially greater effects on indirect costs compared to direct costs, with comprehensive indirect cost reduction rate of 42.3%. Ablation studies confirmed the effectiveness of the integrated algorithm and the incremental contribution of each component mechanism.

Implications

The research establishes quantitative evidence that scheduling efficiency optimization generates disproportionately significant cost reductions in indirect cost categories relative to direct costs in pharmaceutical manufacturing contexts. This differential impact suggests that manufacturing enterprises should prioritize scheduling optimization as a cost management lever for indirect expense categories including energy consumption and inventory management. The findings provide empirical support for intelligent manufacturing investment decisions in the pharmaceutical sector by demonstrating measurable financial returns alongside operational efficiency gains.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.