Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector
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 enhanc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7589 |
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| author | Martina De Giovanni Mariangela Lazoi Romeo Bandinelli Virginia Fani |
| author_facet | Martina De Giovanni Mariangela Lazoi Romeo Bandinelli Virginia Fani |
| author_sort | Martina De Giovanni |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3589ac49237446c8a6956493c93bac23 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3589ac49237446c8a6956493c93bac232025-08-20T02:35:50ZengMDPI AGApplied Sciences2076-34172025-07-011513758910.3390/app15137589Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion SectorMartina De Giovanni0Mariangela Lazoi1Romeo Bandinelli2Virginia Fani3Department of Innovation Engineering, University of Salento, S.P. 6 Lecce-Monteroni, 73100 Lecce, ItalyDepartment of Innovation Engineering, University of Salento, S.P. 6 Lecce-Monteroni, 73100 Lecce, ItalyDepartment of Industrial Engineering, University of Florence, Viale Morgagni 40/44, 50134 Florence, ItalyDepartment of Industrial Engineering, University of Florence, Viale Morgagni 40/44, 50134 Florence, ItalyIn 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.https://www.mdpi.com/2076-3417/15/13/7589optimizationadvanced planning systemcyber–physical systemsmetaheuristic algorithmsmachine learninghuman-centered manufacturing |
| spellingShingle | Martina De Giovanni Mariangela Lazoi Romeo Bandinelli Virginia Fani Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector Applied Sciences optimization advanced planning system cyber–physical systems metaheuristic algorithms machine learning human-centered manufacturing |
| title | Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector |
| title_full | Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector |
| title_fullStr | Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector |
| title_full_unstemmed | Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector |
| title_short | Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector |
| title_sort | advanced planning systems in production planning control an ethical and sustainable perspective in fashion sector |
| topic | optimization advanced planning system cyber–physical systems metaheuristic algorithms machine learning human-centered manufacturing |
| url | https://www.mdpi.com/2076-3417/15/13/7589 |
| work_keys_str_mv | AT martinadegiovanni advancedplanningsystemsinproductionplanningcontrolanethicalandsustainableperspectiveinfashionsector AT mariangelalazoi advancedplanningsystemsinproductionplanningcontrolanethicalandsustainableperspectiveinfashionsector AT romeobandinelli advancedplanningsystemsinproductionplanningcontrolanethicalandsustainableperspectiveinfashionsector AT virginiafani advancedplanningsystemsinproductionplanningcontrolanethicalandsustainableperspectiveinfashionsector |