Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector
Source abstract
Abstract
In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Scheduling and Optimization Algorithms · Manufacturing Process and Optimization · Assembly Line Balancing Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Innovation Engineering, University of Salento, S.P. 6 Lecce-Monteroni, 73100 Lecce, Italy
Department of Industrial Engineering, University of Florence, Viale Morgagni 40/44, 50134 Florence, Italy
References (81)
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