E-commerce platforms face ever more complex dimensional SKU portfolios, demand volatility, returns, seasonality and cost pressures. Traditional optimization approaches (integer programming, heuristics, meta-heuristics) increasingly strain under scale, real-time demands and combinatorial explosion. Meanwhile, quantum-inspired optimization leveraging methods such as quantum annealing, coherent Ising machines, variational circuits and QUBO (Quadratic Unconstrained Binary Optimization) mapping offer near-term practical approaches to combinatorial logistics and inventory problems. This paper proposes a comprehensive framework for applying quantum-inspired optimization to e-commerce supply-chain logistics and inventory management: we provide an extended literature review of classical and quantum-inspired methods; we derive full mathematical formulations for logistics/inventory problems and map them into QUBO/quantum-inspired frameworks; we design a benchmarking/implementation methodology; we discuss industry architecture and deployment issues (data pipelines, latency, hybrid classical/quantum-inspired workflows, interpretability, cost benefit); we present use cases and discuss practical adoption in ecommerce. Our findings suggest that while fully fault-tolerant quantum computing remains some years away, quantum-inspired optimization can deliver meaningful improvements in logistics and inventory decisions today, particularly for large-SKU e-commerce, multi-warehouse networks and time-sensitive delivery.
E-commerce platforms face ever more complex dimensional SKU portfolios, demand volatility, returns, seasonality and cost pressures. Traditional optimization approaches (integer programming, heuristics, meta-heuristics) increasingly strain under scale, real-time demands and combinatorial explosion. Meanwhile, quantum-inspired optimization leveraging methods such as quantum annealing, coherent Ising machines, variational circuits and QUBO (Quadratic Unconstrained Binary Optimization) mapping offer near-term practical approaches to combinatorial logistics and inventory problems. This paper proposes a comprehensive framework for applying quantum-inspired optimization to e-commerce supply-chain logistics and inventory management: we provide an extended literature review of classical and quantum-inspired methods; we derive full mathematical formulations for logistics/inventory problems and map them into QUBO/quantum-inspired frameworks; we design a benchmarking/implementation methodology; we discuss industry architecture and deployment issues (data pipelines, latency, hybrid classical/quantum-inspired workflows, interpretability, cost benefit); we present use cases and discuss practical adoption in ecommerce. Our findings suggest that while fully fault-tolerant quantum computing remains some years away, quantum-inspired optimization can deliver meaningful improvements in logistics and inventory decisions today, particularly for large-SKU e-commerce, multi-warehouse networks and time-sensitive delivery.
Quantum computing presents a transformative opportunity for optimizing supply chain and logistics operations by addressing complex combinatorial challenges beyond the capabilities of classical computing. Traditional supply chain optimization relies on heuristic and mathematical models, which struggle with large-scale problems such as vehicle routing, warehouse management, demand forecasting, and last-mile delivery. Quantum computing, leveraging qubits and principles like superposition and entanglement, enables exponential computational power and efficiency. This paper explores quantum computing's applications in logistics, including quantum annealing for route optimization, fleet management, and supply chain network design. Companies like IBM, Google, and D-Wave are advancing quantum algorithms, with hybrid quantum-classical approaches emerging as practical solutions. While large-scale commercial adoption is still in its infancy, research demonstrates promising results in reducing operational costs, improving sustainability, and enhancing decision-making. The study synthesizes over 80 published works on quantum computing's role in logistics, providing insights into emerging technologies, challenges, and future potential. As the field progresses, early adoption and pilot programs will be crucial for businesses to gain a competitive edge in an increasingly complex global supply chain landscape. Quantum computing has the potential to redefine efficiency, cost reduction, and real-time adaptability in logistics and supply chain management.
This paper provides a comprehensive treatment of hybrid Quantum+AI optimization for logistics and supply chains. We begin with a detailed background and related work spanning classical OR, AI/ML, and quantum optimization. We then formalize key problems (VRP, 3D bin packing, scheduling, inventory) and show precise QUBO mappings and QAOA perspectives, followed by a complete hybrid algorithm with annotated pseudocode. We ground the analysis with reproducible figures (freight volatility, schedule reliability) and synthetic yet realistic benchmarks comparing classical heuristics to hybrid quantum-AI refinement. A 10-year financial model quantifies ROI, NPV, IRR, and sensitivities, and a color-coded 2015-2035 timeline plus industry case studies situates adoption phases. We close with a manager-focused discussion, limitations, and a roadmap to 2035. All figures are reproducible from included CSVs, and references include DOI/URLs.
Industrial engineering increasingly depends on solving large-scale optimization problems involving scheduling, routing, resource allocation, inventory control, energy management, and supply-chain coordination. Classical exact methods often become computationally expensive when problem size, uncertainty, and combinatorial complexity grow rapidly. Although practical quantum computers are still developing, quantum-inspired computing approaches have emerged as powerful alternatives that adapt principles from quantum mechanics to run on classical hardware. This paper investigates quantum-inspired computing approaches for complex optimization problems in industrial engineering. It examines methodologies including simulated annealing, tensor-network optimization, Ising formulations, quantum- inspired evolutionary algorithms, digital annealers, and hybrid classical-intelligent heuristics. Applications in production scheduling, facility layout, vehicle routing, maintenance planning, portfolio optimization, warehouse operations, and smart manufacturing are analyzed. Particular attention is given to solution quality, scalability, stochastic decision-making, real-time adaptability, and integration with Industry 4.0 data systems. Benefits include faster near-optimal solutions, improved operational efficiency, reduced energy consumption, and better responsiveness under uncertainty. Major challenges include formulation difficulty, parameter tuning, benchmarking inconsistency, interpretability, and transition barriers within traditional industrial workflows. A future roadmap is proposed involving hybrid quantum-classical ecosystems, AI- guided optimization engines, digital twins, and autonomous decision platforms. The paper concludes that quantum-inspired computing can deliver practical industrial value today by bridging the gap between conventional optimization and future quantum advantage, especially in environments where speed, flexibility, and complexity management are essential.
Quantum computing offers transformative potential to solve complex optimisation problems in transportation and logistics, particularly those that involve large combinatorial decision spaces such as vehicle routing, traffic control, and supply chain design. Despite theoretical promise and growing empirical interest, its adoption remains limited. This systematic literature review synthesises fifteen peer-reviewed studies published between 2015 and 2025, examining the application of quantum and quantum-inspired methods to transport optimisation. The review identifies five key problem domains (vehicle routing, factory scheduling, network design, traffic operations, and energy management) and categorises the quantum techniques used, including quantum annealing, variational circuits, and digital annealers. Although several studies demonstrate performance gains over classical heuristics, most rely on synthetic datasets, lack statistical robustness, and omit critical operational metrics such as energy consumption and queue latency. Four cross-cutting barriers are identified: hardware limitations, data availability, energy inefficiency, and organisational readiness. The review identifies limited real-world deployment, a lack of standardised benchmarks, and scarce cost–benefit evaluations, highlighting key areas where further empirical work is needed. It concludes with a structured research agenda aimed at bridging the gap between laboratory demonstrations and practical implementation, emphasising the need for pilot trials, open datasets, robust experimental protocols, and interdisciplinary collaboration.
Large-scale logistic networks present a formidable challenge to traditional optimization techniques. The sheer number of variables and constraints involved—including vehicle routing, shipment scheduling, warehouse operations, and inventory management—often leads to computational bottlenecks, hindering efficient network design and operation. This paper investigates the application of quantum-inspired optimization algorithms to address these challenges. Specifically, we explore the potential of algorithms mimicking quantum phenomena, such as quantum annealing and quantum walks, to efficiently solve complex routing and scheduling problems within large logistic networks. Our approach leverages the inherent parallelism and ability of these algorithms to navigate complex solution spaces, offering a promising alternative to classical methods. We demonstrate the feasibility and potential benefits of this strategy through a theoretical framework and illustrative examples, highlighting the advantages of quantum-inspired optimization for improving the performance of large-scale logistic networks.
A multi-objective logistics optimization problem from a real-world supply chain is formulated as a Quadratic Unconstrained Binary Optimization Problem (QUBO) that minimizes cost, emissions, and delivery time, while maintaining target distributions of supplier workshare. The model incorporates realistic constraints, including part dependencies, double sourcing, and multimodal transport. Two hybrid quantum-classical solvers are proposed: a structure-aware informed tree search (IQTS) and a modular bilevel framework (HBS), combining quantum subroutines with classical heuristics. Experimental results on IonQ's Aria-1 hardware demonstrate a methodology to map real-world logistics problems onto emerging combinatorial optimization-specialized hardware, yielding high-quality, Pareto-optimal solutions.