The Paari School of Business at SRM University-AP is organising a Research Seminar titled “Resilient Fleet-Aware Genetic Evolution For Uav-Based Healthcare Transportation Under Dynamic Disruptions”. The session will feature Dr Tejinder Singh Lakhwani, Assistant Professor of Operations and Supply Chain Management at TAPMI School of Business, Manipal University Jaipur, as the expert speaker.
Dr Lakhwani holds a PhD in Decision Sciences and Operations from IIT Jodhpur, where he received the C. V. Raman Gold Medal for Best PhD Thesis (2026). His research focuses on AI-enabled healthcare logistics, drone delivery systems, and IoT-enabled supply chains, with publications across leading peer-reviewed journals and editorial service for BMC Health Services Research.
The session is expected to benefit students, researchers, and faculty members interested in healthcare logistics, drone-assisted delivery systems, metaheuristic optimization, and resilient supply chain management.
Abstract
Efficient, resilient, and safety-aware healthcare transportation systems are critical for the timely delivery of life-saving medical supplies during emergencies, natural hazards, and disruption-prone operating conditions such as floods, storms, and infrastructure failures. However, existing UAV-enabled healthcare logistics frameworks often lack integrated real-time re-optimization, low-latency operational responsiveness, and healthcare-aware decision support under dynamic transportation disruptions. This paper proposes Fleet-Aware Genetic Evolution (FAGE), a real-time adaptive routing and decision-support framework that combines Genetic Algorithm (GA) based optimization with feedback-driven parameter adaptation and Pareto-based multi-objective optimization for UAV-enabled medical transportation. The proposed framework integrates Internet of Things (IoT) sensing with Mobile Edge Computing (MEC) to enable low-latency monitoring, event-triggered route adaptation, and disruption-resilient operational decision-making in intelligent healthcare transportation environments. To support reliable and sustainable emergency logistics operations, FAGE incorporates healthcare-aware operational indicators related to delivery urgency, battery degradation, environmental impact, and cold-chain reliability. The framework is evaluated using benchmark routing datasets and dynamic hospital-delivery simulations involving up to 100 healthcare locations under varying disruption intensities, emergency demand surges, and environmental uncertainties. Comparative results against Adaptive ACO, Hybrid VNS, FSTSP-D, and mixed-integer programming (MIP) approaches demonstrate that FAGE achieves lower delivery time and energy consumption while improving emergency delivery success and cold-chain compliance. In several scenarios, delivery time and energy consumption improved by up to 18% and 20%, respectively. Additional sensitivity analysis further demonstrates the robustness, scalability, and operational adaptability of the proposed framework under dynamic and extreme-event-driven transportation conditions. The results highlight the potential of FAGE for intelligent, disruption-resilient, and safety-aware healthcare transportation and emergency logistics operations.
Key Details
- Date: November 18, 2026 (Wednesday)
- Time: 03:00 PM to 04:00 PM
- Venue: Level 5, Tiered Classroom, Homi J Bhabha Block
- Topic: Resilient Fleet-Aware Genetic Evolution For Uav-Based Healthcare Transportation Under Dynamic Disruptions
- Speaker: Dr Tejinder Singh Lakhwani (Assistant Professor of Operations and Supply Chain Management, TAPMI School of Business, Manipal University Jaipur)