From pattern recognition to remediation management in a closed digital loop architecture for post-war logistics
Keywords:
Pattern recognition, remediation logistics, post-war remediation, emergency situations (ES), closed digital diagnostic-managerial loop, observation systems, geographic information systems, decision support systems, BPMN, critical path method, AR/VR, Human-in-the-LoopSynopsis
The monograph provides an in-depth study of the problem of transition from digital diagnostics to executable management in the tasks of implementing remediation processes in areas affected by emergency events, including those of a military nature, where the effectiveness of recovery measures is largely determined by the quality of logistical coordination, the accessibility of infrastructure facilities, and the speed of managerial decision-making. Remediation in this study is interpreted as a complex, multidimensional process that includes the elimination of the consequences of war-related destruction and various kinds of contamination of soil, water, and air, the restoration of the spatial connectedness of territories, the provision of safe access, as well as the implementation of environmental, infrastructural, and socio-economic measures of post-crisis recovery. Against this background, the necessity is substantiated of transitioning from the fragmented use of digital tools to an integrated management architecture ensuring a closed cycle of “observation – recognition – geospatial diagnostics – decision – execution – verification – adaptation». As a conceptual foundation, the architecture of the Closed Digital Diagnostic Loop for Remediation Logistics is proposed, in which the Pattern Recognition block is not an autonomous analytical module, but acts as a source of diagnostic events forming inputs for geospatial integration, decision support systems, and simultaneous process orchestration. The central element of the architecture is the Decision & Orchestration Core (DSS (Decision Support System) + BPMN (Business Process Model and Notation)/CPM (Critical Path Method)) linkage, which ensures the transformation of the results of recognition and geospatial diagnostics into executable managerial actions in the logistics of remediation operations, including, among other things, task prioritization, resource allocation, as well as schedule planning and control of the sequence of the work being performed. Separately, the monograph reveals the role of AR/VR (Augmented Reality/Virtual Reality) and Human-in-the-Loop mechanisms as an end-to-end HMI layer, which ensure the interpretation of diagnostic results, the coordination of decisions between levels of management, as well as support for the field implementation and verification of measures.
This study has a conceptual-methodological character and is aimed at substantiating the architectural integration of observation technologies, pattern recognition, GIS (Geographic Information System), DSS, BPMN/CPM, and HMI (Human–Machine Interface) mechanisms within a unified diagnostic-managerial loop for the implementation of a complex of multidirectional tasks of post-war remediation logistics. The proposed authorial concept creates a methodological basis for the further formalization of the rules of transition from diagnostic events to managerial actions, the development of domain orchestration profiles, and subsequent scenario-based as well as empirical validation under conditions of post-crisis recovery. The monograph considers transport and logistics as the central applied profile, because it is precisely there that the diagnostics → orchestration → execution linkage is most critical, and also because they are critical subsystems through which the managerial mechanism for eliminating the consequences of emergency events and subsequent remediation is implemented. However, the concept substantiated by the authors has a broader scope of applicability, namely: the elimination of emergency events not only of a post-conflict, but also of a natural as well as technogenic nature in all spheres of the socio-economic system, and the remediation of territories as a whole.
References
Pantiris, P., Pallis, P. L., Chountalas, P. T., Dasaklis, T. K. (2025). Enhancing Coordination and Decision Making in Humanitarian Logistics Through Artificial Intelligence: A Grounded Theory Approach. Logistics, 9 (3), 113. https://doi.org/10.3390/logistics9030113
Patnala, P. K., Regehr, J. D., Mehran, B., Regoui, C. (2024). Resilience for freight transportation systems to disruptive events: a review of concepts and metrics. Canadian Journal of Civil Engineering, 51 (3), 237–263. https://doi.org/10.1139/cjce-2023-0187
Köstepen, Z. N., Selim, H. (2025). Literature Review on Humanitarian Logistics in Disaster Management: A Risk-Oriented Approach. Sustainability, 17 (21), 9773. https://doi.org/10.3390/su17219773
Kong, J., Zhang, C., Simonovic, S. P. (2023). Resilience and risk-based restoration strategies for critical infrastructure under uncertain disaster scenarios. Sustainable Cities and Society, 92, 104510. https://doi.org/10.1016/j.scs.2023.104510
Plotnikov, O.; Carrillo-Pina, J., Sharov, O. (Eds.) (2025). The Influence of the Fragmentation of the World Economy on the Post-War Reconstruction of Ukraine. The Geoeconomics of the International Monetary Order. Cham: Palgrave Macmillan, 71–91. https://doi.org/10.1007/978-3-031-90851-4_3
Becker, T., Gorodnichenko, Y., Weder di Mauro, B. (2025). How to Rebuild Ukraine: A Synthesis and Critical Review of Policy Proposals. Annual Review of Economics, 17 (1), 293–320. https://doi.org/10.1146/annurev-economics-081324-093012
Nivievskyi, O., Goriunov, D., Nagurney, A., Dvornichenko, D., Kopytsia, I., Kirchner, R. et al. (2025). Recovery, Resilience and Resources. Ukrainian Analytical Digest, 14. https://doi.org/10.3929/ethz-c-000784202
Cherniavskyi, B. (2024). Challenges of Successful Remediation in Ukraine after the End of Military Activities in the Context of European Integration. Economic development and policies: realities and prospects. European integration, convergence and cohesion. Sofia: ERI at BAS, 103–107.
Petrukha, N., Fedirko, N., Piatnychuk, I., Lyashenko, P., Plakhotnii, D. (2025). Economic Rebuilding Frameworks in Post-War States: Takeaways for Ukraine. International Journal of Economic Sciences, 14 (1), 196–210. https://doi.org/10.31181/ijes1412025196
Chukwuebuka Ahuchogu, M., Jawad, A. B., Hamidi, I. A., Jayasundar, S., Howard, E. (2025). Real-Time Image-Based Data Processing and its Applications in Managerial Decision-Making and Risk Analysis. Eksplorium, 46 (1), 1552–1565. https://doi.org/10.52783/eksplorium.181
Mondal, G., Dhanaraj, R. K., Banerjee, C.; Bhattacharya, A., Dutta, S., Yang, X. S., Bose, S. (Eds.) (2026). The Future of Risk Detection: Integrating RS, GIS, and AI for Holistic Hazard Awareness. Proceedings of International Conference on Computational Intelligence and Information Retrieval. Cham: Springer, 507–522. https://doi.org/10.1007/978-3-032-02790-0_36
Cimini, C., Lagorio, A., Cavalieri, S., Riedel, O., Pereira, C. E., Wang, J. (2022). Human-technology integration in smart manufacturing and logistics: current trends and future research directions. Computers & Industrial Engineering, 169, 108261. https://doi.org/10.1016/j.cie.2022.108261
Moazzeni, S., Sgarbossa, F. (2025). Collaborative Logistics and Digital Technologies in Rural Contexts: A Systematic Review and a Decision Aid Model for Logistics Decision-Makers. SSRN. https://doi.org/10.2139/ssrn.5236703
Rahman, M. A. (2025). Review of Applied Science and Technology. SSRN. https://doi.org/10.2139/ssrn.5360313
Rahnemoonfar, M., Chowdhury, T., Murphy, R. (2023). RescueNet: A High Resolution UAV Semantic Segmentation Dataset for Natural Disaster Damage Assessment. Scientific Data, 10 (1). https://doi.org/10.1038/s41597-023-02799-4
Albahri, A. S., Khaleel, Y. L., Habeeb, M. A., Ismael, R. D., Hameed, Q. A., Deveci, M. et al. (2024). A systematic review of trustworthy artificial intelligence applications in natural disasters. Computers and Electrical Engineering, 118, 109409. https://doi.org/10.1016/j.compeleceng.2024.109409
Rodrigues da Silva, A., Estima, J., Marques, J., Gamito, I., Serra, A., Moura, L. et al. (2023). A Web GIS Platform to Modeling, Simulate and Analyze Flood Events: The RiverCure Portal. ISPRS International Journal of Geo-Information, 12 (7), 268. https://doi.org/10.3390/ijgi12070268
Betke, H., Seifert, M. (2017). BPMN for Disaster Response Processes – A methodical extension. INFORMATIK 2017, 1311–1324. Available at: https://www.researchgate.net/publication/320556286_BPMN_for_Disaster_Response_Processes-A_methodical_extension
Kramer, S. W., Jenkins, J. L. (2006). Understanding the basics of CPM calculations: what is scheduling software really telling you? PMI® Global Congress 2006–North America. Madrid: Project Management Institute. Available at: https://www.pmi.org/learning/library/basics-cpm-scheduling-software-axon-8170
Hong, C., Park, S., Ju, K., Lee, J. (2024). An Open Disaster Information Platform, Methodology, and Visualization for High-Rise and Complex Facilities. Buildings, 14 (12), 4047. https://doi.org/10.3390/buildings14124047
Motamedi, A., Vaudou, S., Leygonie, R., Forgues, D. (2019). Process re-engineering in owner organizations to improve BIM-based project delivery using requirements management platform. 4th International Conference on Civil and Building Engineering Informatics (ICCBEI). Sendai. Available at: https://www.researchgate.net/publication/339630638_Process_Re-Engineering_in_Owner_Organizations_to_Improve_BIM-Based_Project_Delivery_Using_Requirements_Management_Platform
Khanal, S., Medasetti, U. S., Mashal, M., Savage, B., Khadka, R. (2022). Virtual and Augmented Reality in the Disaster Management Technology: A Literature Review of the Past 11 years. Frontiers in Virtual Reality, 3. https://doi.org/10.3389/frvir.2022.843195
Surtiari, G. A. K., Dalimunthe, S. A., Reksa, A. F. A., Pelupessy, D., Prasojo, A. P. S., Jibiki, Y. et al. (2024). Making Virtual Reality (VR)/Augmented Reality (AR) Possible to Strengthen Disaster Risk Reduction among Communities at Risk of Tsunami. International Journal of Disaster Management, 6 (3), 291–312. https://doi.org/10.24815/ijdm.v6i2.34523
Cherniavskyi, B., Blakyta, H., Susidenko, V., Andreichenko, A., Remyha, Y., Podmazko, O.; Cherniavska, T. (Ed.) (2025). Innovative technologies and digital models in the post-war recovery of the transport and logistics system of Ukraine. Economy in the Era of Digital Transformation: Trends, Opportunities and Perspectives. Tallinn: Scientific Route OÜ, 110–143. https://doi.org/10.21303/978-9908-9706-0-8.ch5
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Text Identifier of this Chapter
Chapter 4
Published
June 11, 2026
Copyright (c) 2026 Tetiana Cherniavska, Bohdan Cherniavskyi, Alla Rusnak, Iryna Nadtochii, Artur Harahulia, Oleksii Bobrovskyi
How to Cite
Cherniavska, T., Cherniavskyi, B., Rusnak, A., Nadtochii, I., Harahulia, A., & Bobrovskyi, O. (2026). From pattern recognition to remediation management in a closed digital loop architecture for post-war logistics. In V. Lukin, S. Kryvenko, F. Li, S. Abramov, V. Abramova, B. Kovalenko, I. Dohtiev, O. Arkhipov, N. Stojanović, B. Bondžulić, P. Mykhalichenko, T. Cherniavska, B. Cherniavskyi, V. Nadtochii, A. Nadtochyi, M. Korkh, Zghurska О., S. Kasian, K. Nakonechna, … I. Kalinichenko, Pattern recognition in surveillance systems and diagnostics (pp. 102-131). Scientific Route OÜ®. https://doi.org/10.21303/978-9908-845-05-0.ch4
