Aug 2026· International Journal of Creative and Open Research in Engineering and Management· 0 citations
TL;DR
This paper presents a comprehensive investigation of quantum annealing and hybrid quantum-classical algorithms applied to unit load device (ULD) configuration and disruption management in air cargo and multimodal logistics networks, and provides a practical roadmap for near-term adoption of quantum technologies in high-stakes logistics environments.
Abstract
Quantum computing offers transformative potential for addressing complex combinatorial optimization challenges in logistics and supply chain management. This paper presents a comprehensive investigation of quantum annealing and hybrid quantum-classical algorithms applied to unit load device (ULD) configuration and disruption management in air cargo and multimodal logistics networks. We formulate the ULD loading and placement problem as a Quadratic Unconstrained Binary Optimization (QUBO) model that incorporates weight, volume, center-of-gravity, structural stress, and compatibility constraints. A stratified hybrid architecture spanning physical quantum hardware, algorithmic middleware, and decision-support layers is proposed. Through extensive numerical experiments and comparison with classical solvers, we demonstrate superior payload utilization (up to 96.5%), favorable computational scaling, and rapid disruption recovery. Real-world industry pilots, including quantum-assisted route optimization achieving substantial carbon emission reductions and hybrid solutions at major ports, are analyzed. Results indicate that while noisy intermediate-scale quantum (NISQ) hardware limitations persist, hybrid approaches already deliver measurable gains in operational efficiency, cost reduction, and supply-chain resilience. The work provides a practical roadmap for near-term adoption of quantum technologies in high-stakes logistics environments.
Keywords— Quantum annealing, Unit load device (ULD), QUBO, Logistics optimization, Disruption management, Hybrid quantum-classical algorithms, Air cargo, Supply chain resilience
Optimization of vehicle-to-station assignments under capacity and distance constraints represents a challenging combinatorial problem relevant to automated planning, logistics, and autonomous mobility systems. Classical methods such as Mixed Integer Linear Programming (MILP) or metaheuristics often struggle to scale efficiently with problem dimensionality, motivating the exploration of hybrid quantum-classical paradigms. This paper presents a normalized Quantum Approximate Optimization Algorithm (QAOA) framework tailored for constrained assignment problems, where vehicle-station distances are encoded into a normalized cost Hamiltonian. Capacity violations and unused resources are incorporated through dynamically scaled penalty terms, producing a cost landscape that effectively guides the quantum search process. The proposed pipeline integrates parameter optimization using COBYLA to refine the QAOA angles, ensuring convergence toward low-cost feasible configurations. Experimental simulations in Cirq on a 10-qubit system demonstrate that the normalized QAOA pipeline consistently identifies near-optimal assignments while substantially reducing the combinatorial search space. These results provide empirical evidence for the viability of hybrid QAOA formulations in real-world planning and scheduling scenarios, establishing a foundation for future implementations on noisy intermediatescale quantum (NISQ) hardware.
Jakub Kołota· International Conference on...· 0 citations
Presented in this paper is the theory and practical implementation of quantum annealing for project scheduling and resource-allocation tasks that can be mapped to Ising or Quadratic Unconstrained Binary Optimization (QUBO) models. Quantum annealing is a meta-heuristic that exploits quantum tunnelling to search the energy landscape of combinatorial optimization problems. The Quantum Annealing Process Flowchart is detailed stage by stage. Hybrid quantum-classical workflows that embed annealing inside a Monte Carlo or digital-twin loop are described, together with supporting figures of the energy landscape and the hybrid architecture. Cross-domain analogies with ULD configuration and related combinatorial problems illustrate transferability. The paper concludes with implementation considerations and the current maturity of the technology for construction and logistics applications.
Keywords: quantum annealing implementation; Ising model; QUBO; combinatorial optimization; project scheduling; hybrid quantum-classical; energy landscape; digital twin; Monte Carlo; supply-chain resilience; ULD configuration
U. Kanade· International Journal of Cre...· 0 citations
Explored in this paper is the theory and practical implementation of quantum annealing for project scheduling and resource-allocation tasks that can be mapped to Ising or Quadratic Unconstrained Binary Optimization (QUBO) models. Quantum annealing is a meta-heuristic that exploits quantum tunnelling to search for the energy landscape of combinatorial optimization problems. The Quantum Annealing Process Flowchart is detailed stage by stage. Hybrid quantum-classical workflows that embed annealing inside a Monte Carlo or digital-twin loop are described, together with supporting figures of the energy landscape and the hybrid architecture. Cross-domain analogies with ULD configuration and related combinatorial problems illustrate transferability. The paper concludes with implementation considerations and the current maturity of the technology for construction and logistics applications.
Keywords: quantum annealing implementation; Ising model; QUBO; combinatorial optimization; project scheduling; hybrid quantum-classical; energy landscape; digital twin; Monte Carlo; supply-chain resilience; ULD configuration
A. G· International Journal of Cre...· 0 citations
A transportation-grounded constraint-aware Quantum Alternating Operator Ansatz (QAOA+) framework that embeds feasibility-preserving logic directly into the search operator, and shows that constraint-handling architecture strongly influences feasible-route sampling, convergence behavior, and probability concentration over low-cost feasible routes.
Improving the efficiency, safety, and speed of large-scale Automated Guided Vehicle (AGV) systems is crucial for enhancing the productivity of logistics warehouses. Recent advancements in quantum annealing devices have demonstrated the potential to optimize AGV routing effectively. However, applying quantum annealing to complex and large-scale AGV routing problems remains challenging due to insufficient consideration of operational constraints and the limited number of qubits available in current quantum annealers. To address these challenges, this study proposes a new formulation for large-scale AGV routing by introducing a priority constant to reduce delays caused by constraint violations. Additionally, we develop a system architecture that employs multiple FPGAs to accelerate Simulated Quantum Annealing (SQA), thereby overcoming the computational inefficiencies of classical SQA implementations. The proposed architecture is designed to solve large-scale optimization problems involving tens of thousands of variables. Each FPGA board can process problems with up to 50,176 variables, and a five-FPGA parallel configuration achieves significantly faster processing speeds. The performance of the proposed system is validated using a commercial AGV Operating System (AOS), including experiments with real AGVs in a small-scale environment and simulations involving up to 1,000 AGVs. Experimental results demonstrate that the proposed SQA accelerator achieves faster processing speeds and higher-quality solutions compared to existing SQA solvers, confirming its effectiveness for large-scale AGV route optimization.
T. Quang, Kosuke Matsuyama, Keisuke Shimizu et al.· IEEE Access· 0 citations
Resource constrained project scheduling and related allocation tasks can be expressed as Quadratic Unconstrained Binary Optimization (QUBO) models and submitted to quantum annealers. This paper develops the QUBO formulation in detail: choice of binary variables, linear and quadratic objective terms, conversion of precedence and resource constraints into penalty functions, and calibration of penalty weights. The resulting Hamiltonian is embedded onto quantum hardware, annealed, and post-processed. Hybrid classical–quantum workflows that wrap the QUBO solver inside Monte Carlo risk analysis or a digital twin are examined. Supporting process flowcharts and energy-landscape figures illustrate every stage. The treatment remains grounded in construction and logistics applications while remaining transferable to other combinatorial domains such as ULD configuration.
Keywords: QUBO formulation; quantum annealing; Ising model; penalty methods; resource-constrained project scheduling; hybrid quantum-classical; minor embedding; Monte Carlo; digital twin.
U. Kanade· International Journal of Cre...· 0 citations