EFFICIENT LOGISTICS SCHEDULING USING HYBRID QUANTUMCLASSICAL OPTIMIZATION ALGORITHMS

Abstract

Efficient logistics scheduling is a critical challenge in modern supply chain management due to increasing transportation demands, dynamic delivery requirements, and the need for cost-effective resource utilization. Traditional optimization techniques often face limitations when addressing large-scale combinatorial scheduling problems involving multiple constraints such as vehicle capacity, delivery deadlines, route selection, and operational costs. Recent advancements in quantum computing have introduced new opportunities for solving complex optimization problems by leveraging quantum parallelism and probabilistic search mechanisms. This study proposes a Hybrid Quantum-Classical Optimization Algorithm (HQCOA) for efficient logistics scheduling that combines the computational strengths of quantum optimization techniques with the robustness and scalability of classical scheduling methods. The proposed framework employs a quantum optimization layer to explore promising scheduling solutions while a classical optimization module refines and validates these solutions under practical logistics constraints. The hybrid approach is designed to minimize total transportation cost, delivery time, and resource utilization while maximizing scheduling efficiency and fleet productivity. Experimental evaluations were conducted using benchmark logistics datasets of varying scales. Performance metrics including scheduling accuracy, computational efficiency, delivery completion rate, and operational cost reduction were analyzed and compared with conventional Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and classical Mixed Integer Linear Programming (MILP) approaches. Results demonstrate that the proposed hybrid quantum-classical model achieves superior scheduling performance, reducing transportation costs by up to 18%, decreasing delivery delays by 22%, and improving resource utilization by 15% compared to traditional optimization techniques. Furthermore, the framework exhibits improved scalability when handling large and complex logistics networks. The findings indicate that hybrid quantum-classical optimization represents a promising direction for next-generation intelligent logistics management systems, enabling more efficient, adaptive, and sustainable supply chain operations.