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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 9

Published on: September 2026

FKF–FKL SPECTRAL ANALYSIS AND QUANTUM OPTIMIZATION FOR INTELLIGENT AND RESILIENT LOGISTICS

V. A. Sharma

Department of Mathematics, Smt. Narsamma Arts, Commerce and Science college

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

Modern supply chains require intelligent methods to manage disruptions, uncertain lead times, energy constraints, and complex logistics decisions. This study proposes an integrated framework combining FKF/FKL spectral analysis, digital twins, artificial intelligence, blockchain traceability, and quantum-inspired optimization for resilient and sustainable logistics. FKF/FKL transforms characterize multi-scale lead-time patterns, shifts, and operational perturbations, while digital twins provide synchronized representations of evolving supply-chain states. Quantum annealing and quadratic unconstrained binary optimization (QUBO) support selected problems such as unit-load-device configuration, packing, assignment, and disruption recovery, complemented by classical preprocessing and validation. Energy-aware mobility incorporates battery capacity, solar contribution, and route requirements into logistics decisions. AI enables prediction and risk assessment, while blockchain strengthens provenance and traceability and human-centric controls preserve operational authority. The resulting architecture integrates sensing, spectral analysis, prediction, optimization, verification, and recovery into a unified decision-support cycle for resilient logistics

Keywords— Supply chain resilience; Quantum logistics; Quantum annealing; QUBO optimization; Unit-load-device configuration; FKF transform; FKL transform; Digital twins; Artificial intelligence; Blockchain traceability; Intelligent transportation systems; Sustainable logistics; Energy-aware logistics; Industry 5.0; Disruption management.

How to Cite this Paper

Sharma, V. A. (2026). FKF–FKL Spectral Analysis and Quantum Optimization for Intelligent and Resilient Logistics. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(9), 1-9. https://doi.org/10.55041/ijcope.v2i9.007

Sharma, V.. "FKF–FKL Spectral Analysis and Quantum Optimization for Intelligent and Resilient Logistics." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 9, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i9.007.

Sharma, V.. "FKF–FKL Spectral Analysis and Quantum Optimization for Intelligent and Resilient Logistics." International Journal of Creative and Open Research in Engineering and Management 02, no. 9 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i9.007.

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References


  • SM Harle,“Artificial Intelligence for Supply Chain Risk Prediction and Mitigation: A Systematic Review, Conceptual Framework, and Future Research Agenda,” International Research Journal of Modernization in Engg Technology and Science, Vol. 8, pp. 70–80, July 2026.

  • SM Harle, “Artificial Intelligence-Driven Framework for Village-Level Water Conservation and Groundwater Sustainability,” International Research Journal of Modernization in Engg Tech and Science, Vol. 8, pp. 31–49, July 2026.

  • M. Harle et al., “Artificial Intelligence Approaches for Strength Prediction and Optimization of Composite Concrete Mixtures: A Systematic Review and Future Research Framework,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 8, pp. 81–97, July 2026.

  • M. Harle et al., “Artificial Intelligence-Based Pavement Crack Detection: A Comprehensive Review of Machine Learning and Deep Learning Techniques,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 8, pp. 50–69, July 2026.

  • M. Harle et al., “Digital Twins and Artificial Intelligence for Sustainable Textile Raw Material Processing,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 8, pp. 14–30, July 2026.

  • Harle SM, “Investigation of Mechanical Behavior of Steel Fiber-Reinforced Geopolymer Concrete Under ulti-Axial Stress Conditions,”  Hydraulic and Civil Engineering Technology IX: Proceedings of the 9th International Technical Conference on Frontiers of HCET, Sanya, China, SAGE Publications, 25-27 Sept pp 555-561 , 2024

  • Gulhane, “Digital Twin Technology for Building Resilient and Adaptive Global Supply Chains: A Review,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 6, Issue 12, 2024. DOI: 10.56726/IRJMETS64992. Google Scholar

  • Gulhane, “Artificial Intelligence Applications in Sustainable Logistics and Green Supply Chain Management: A Bibliometric Review,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 6, Issue 12, 2024. DOI: 10.56726/IRJMETS65006. Google Scholar

  • Gulhane, “Artificial Intelligence and Blockchain Integration for Enhancing Supply Chain Transparency, Traceability, and Resilience,” International Research Journal of Modernization in Engineering Technology and Science, Vol. 6, Issue 12, 2024. DOI: 10.56726/IRJMETS65016. Google Scholar

  • Gulhane. (2014). Swipe Controller. International Journal of Research in Engineering and Applied Sciences (IJREAS), 4(12), 1–7. Google Scholar

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  • Published on: Sep 03 2026
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