A Proposal for Future Research Agenda on Automated Disruption Management in Intralogistic Systems
This paper presents a systematic literature review on Automated Disruption Management (ADM) in intralogistic systems, analyzing 1.406 papers between 2018 and 2024. The review examines current approaches to managing disruptions in modern intralogistic environments, focusing on system architectures, a...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11030584/ |
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| author | Imanol Olaizola-Arregui Miguel Mediavilla Enrique Onieva |
| author_facet | Imanol Olaizola-Arregui Miguel Mediavilla Enrique Onieva |
| author_sort | Imanol Olaizola-Arregui |
| collection | DOAJ |
| description | This paper presents a systematic literature review on Automated Disruption Management (ADM) in intralogistic systems, analyzing 1.406 papers between 2018 and 2024. The review examines current approaches to managing disruptions in modern intralogistic environments, focusing on system architectures, adaptation capabilities, decision-making methods, and implementation aspects. Through a structured analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we identify several critical gaps in current research, particularly in handling multiple simultaneous disruptions, integrating predictive capabilities with real-time adaptation, and validating theoretical developments in real-world settings. Our findings reveal a significant trend toward Reinforcement Learning (RL) approaches and an observable evolution from traditional Automated Guided Vehicles (AGV) to more flexible Autonomous Mobile Robot (AMR) solutions. Also, our work revealed the need for more integrated approaches that can handle multiple disruption types simultaneously while maintaining system performance, particularly in complex industrial environments. Moreover, the analysis also shows a considerable gap between theoretical development and practical implementation, with very few papers reporting real-world testing results. This review contributes to the field by providing a comprehensive taxonomy of current approaches, identifying critical research gaps, and proposing a specific research agenda in the field for future research. |
| format | Article |
| id | doaj-art-23f46f3fbb074ca8b29ef10a8dbbb7fc |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-23f46f3fbb074ca8b29ef10a8dbbb7fc2025-08-20T02:21:25ZengIEEEIEEE Access2169-35362025-01-011310436810438110.1109/ACCESS.2025.357875711030584A Proposal for Future Research Agenda on Automated Disruption Management in Intralogistic SystemsImanol Olaizola-Arregui0https://orcid.org/0000-0001-8054-2595Miguel Mediavilla1https://orcid.org/0000-0003-2471-0472Enrique Onieva2https://orcid.org/0000-0001-9581-1823Faculty of Engineering, University of Deusto, Bilbao, SpainDepartamento de Operaciones Logístico-Productivas, Mondragon Unibertsitatea, Arrasate, SpainFaculty of Engineering, University of Deusto, Bilbao, SpainThis paper presents a systematic literature review on Automated Disruption Management (ADM) in intralogistic systems, analyzing 1.406 papers between 2018 and 2024. The review examines current approaches to managing disruptions in modern intralogistic environments, focusing on system architectures, adaptation capabilities, decision-making methods, and implementation aspects. Through a structured analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we identify several critical gaps in current research, particularly in handling multiple simultaneous disruptions, integrating predictive capabilities with real-time adaptation, and validating theoretical developments in real-world settings. Our findings reveal a significant trend toward Reinforcement Learning (RL) approaches and an observable evolution from traditional Automated Guided Vehicles (AGV) to more flexible Autonomous Mobile Robot (AMR) solutions. Also, our work revealed the need for more integrated approaches that can handle multiple disruption types simultaneously while maintaining system performance, particularly in complex industrial environments. Moreover, the analysis also shows a considerable gap between theoretical development and practical implementation, with very few papers reporting real-world testing results. This review contributes to the field by providing a comprehensive taxonomy of current approaches, identifying critical research gaps, and proposing a specific research agenda in the field for future research.https://ieeexplore.ieee.org/document/11030584/Intralogisticautomated disruption managementindustry 4.0systematic reviewreinforcement learning |
| spellingShingle | Imanol Olaizola-Arregui Miguel Mediavilla Enrique Onieva A Proposal for Future Research Agenda on Automated Disruption Management in Intralogistic Systems IEEE Access Intralogistic automated disruption management industry 4.0 systematic review reinforcement learning |
| title | A Proposal for Future Research Agenda on Automated Disruption Management in Intralogistic Systems |
| title_full | A Proposal for Future Research Agenda on Automated Disruption Management in Intralogistic Systems |
| title_fullStr | A Proposal for Future Research Agenda on Automated Disruption Management in Intralogistic Systems |
| title_full_unstemmed | A Proposal for Future Research Agenda on Automated Disruption Management in Intralogistic Systems |
| title_short | A Proposal for Future Research Agenda on Automated Disruption Management in Intralogistic Systems |
| title_sort | proposal for future research agenda on automated disruption management in intralogistic systems |
| topic | Intralogistic automated disruption management industry 4.0 systematic review reinforcement learning |
| url | https://ieeexplore.ieee.org/document/11030584/ |
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