Authors

Reyansh Mahadevan

Department of Information Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

Nandini Subramanian

Department of Information Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

Advaith Srinivasan

Department of Information Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

Abstract

Healthcare supply chains operate under complex conditions involving limited inventories, fluctuating demand, transportation constraints, emergency requirements, and disruptions that can rapidly alter resource requirements. Efficient capacity allocation is therefore essential for maintaining the availability and timely distribution of medicines, vaccines, medical equipment, and other healthcare resources. Conventional optimization approaches can become computationally demanding when allocation problems involve multiple facilities, competing priorities, uncertain demand, and rapidly changing operational conditions. Quantum-inspired optimization provides an emerging computational approach that uses principles inspired by quantum computation to explore complex solution spaces while operating on conventional computing infrastructure. This study examines the application of quantum-inspired optimization to healthcare supply chain capacity allocation through a structured literature-based analysis. The study integrates research on counterfactual supply-chain allocation, quantum-inspired healthcare optimization, disaster logistics, healthcare resource distribution, supply-chain optimization, and quantum-inspired scheduling. The findings indicate that quantum-inspired methods can provide useful search mechanisms for complex allocation problems, particularly where decisions involve multiple interacting constraints and competing objectives. The proposed framework integrates healthcare demand information, available capacity, inventory levels, transportation constraints, priority requirements, and disruption conditions into a quantum-inspired optimization layer. Counterfactual scenario analysis is incorporated to compare alternative allocation decisions before implementation. The resulting framework provides a conceptual basis for adaptive healthcare supply-chain capacity allocation and supports more responsive resource management under uncertain and disruptive conditions.

Keywords

Quantum-inspired optimization healthcare supply chain capacity allocation resource allocation quantum computing healthcare logistics supply-chain resilience

Citation of this Article

Reyansh Mahadevan, Nandini Subramanian, & Advaith Srinivasan. (2026). Quantum-Inspired Optimization for Healthcare Supply Chain Capacity Allocation. Current Journal of Engineering and Science Research. 3(3), 34-40. Article DOI: https://doi.org/10.47001/CJESR/2026.303006 

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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