Research on Production Scheduling Algorithm Fairness and Corporate Governance Efficiency in Intelligent Manufacturing Environments

Abstract

The rapid development of intelligent manufacturing technologies hasaccelerated the adoption of data-driven production schedulingalgorithms in modern industrial systems. While these algorithmssignificantly improve operational efficiency and resource utilization,concerns regarding algorithmic fairness have emerged, particularly inthe allocation of production resources, task prioritization, workforcescheduling, and decision transparency. This study investigates therelationship between production scheduling algorithm fairness andcorporate governance efficiency within intelligent manufacturingenvironments. A conceptual framework is developed to analyze howfairness principles influence decision-making quality, stakeholder trust,operational coordination, and organizational performance. Usingquantitative analysis and simulation-based evaluation, key fairnessdimensions—including procedural fairness, distributive fairness,transparency, and accountability—are examined alongside governanceindicators such as decision efficiency, resource allocation effectiveness,riskcontrol,andmanagerialresponsiveness.