Current world trade is based and supported in a strong and healthy supply chain, where logistics play a key role in producing and providing key assets and goods to keep societies and economies going. Current geopolitical and sanitary challenges faced in the entire world have made even more critical the role of logistics and increased demands for tuning transport function to keep the supply chain up and running. The challenge is only increasing and growing for the future, thus tackling transport optimization provides both business and social value. Optimization problems are ubiquitous and they present a challenge due to its complexity, where they're typically NP-hard problems. Quantum Computing is a developing field, and the Quantum Annealing approach has proven to be quite effective in its applicability and usefulness to tackle optimization problems. In this work we treat the Vehicle Routing Problem, which is also a variation of a famous optimization problem known as the Traveling Salesman Problem. We aim to tackle the vehicle optimization problem from the last mile logistic scenario application, with a perspective from the classical and quantum approaches, and providing a solution which combines both, also known as hybrid solution. Finally, we provide the results of the analysis and proposal for the consideration of applications in a near term business case scenario.
Current world trade is based and supported in a strong and healthy supply chain, where logistics play a key role in producing and providing key assets and goods to keep societies and economies going. Current geopolitical and sanitary challenges faced in the entire world have made even more critical the role of logistics and increased demands for tuning transport function to keep the supply chain up and running. The challenge is only increasing and growing for the future, thus tackling transport optimization provides both business and social value. Optimization problems are ubiquitous and they present a challenge due to its complexity, where they're typically NP-hard problems. Quantum Computing is a developing field, and the Quantum Annealing approach has proven to be quite effective in its applicability and usefulness to tackle optimization problems. In this work we treat the Vehicle Routing Problem, which is also a variation of a famous optimization problem known as the Traveling Salesman Problem. We aim to tackle the vehicle optimization problem from the last mile logistic scenario application, with a perspective from the classical and quantum approaches, and providing a solution which combines both, also known as hybrid solution. Finally, we provide the results of the analysis and proposal for the consideration of applications in a near term business case scenario.
In the ever-evolving landscape of global trade and supply chain management, logistics optimization stands as a critical challenge. This study takes on the Vehicle Routing Problem (VRP), a variant of the Traveling Salesman Problem (TSP), by proposing a novel hybrid solution that seamlessly combines classical and quantum computing methodologies. Through a comprehensive analysis of our approach, including algorithm selection, data collection, and computational processes, we provide in-depth insights into the efficiency, and effectiveness of our hybrid solution compared to traditional methods. The results after analysis of 14 datasets highlight the advantages and limitations of this approach, demonstrating its potential to address NP-hard problems and contribute significantly to the field of optimization algorithms in logistics. This research offers promising contributions to the advancement of logistics optimization techniques and their potential implications for enhancing supply chain efficiency.
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.
In the current competitive industrial environment, the study of NP-hard issues is becoming more and more significant. In logistics, the Vehicle Routing Problem (VRP) is one of the popular challenges, especially in cold chain logistics, which requires the management of perishable goods. This problem involves identifying the optimal paths for vehicles responsible for transporting goods to different locations. Due to its combinatorial nature and the complexity of finding optimal solutions, it is identified as an NP-hard problem that requires an unreasonable time frame to solve. Recently, quantum computing has proven to be a promising approach for addressing some of the complex challenges. Among quantum methodologies, quantum annealing (QA) has demonstrated the potential to accelerate computation times in solving optimization problems. By leveraging the principles of quantum tunneling and superposition, QA can explore solution spaces more efficiently. Therefore, this study explores the application of QA to address VRP in cold chain logistics, aiming to develop solutions that minimize travel distances during deliveries. The case study demonstrates that QA can achieve, on average, at least 85% of optimal performance while providing approximately 95% faster run times compared to classical genetic algorithms (GA). This contributes to greater efficiency in cold chain logistics operations.
Abstract The quantum supply chain transforms logistics and supply chain management by harnessing quantum technologies to tackle longstanding challenges in optimization, resilience, and real-time decision-making. Through qubits and superposition, quantum computers deliver exponential speedups for complex problems including route optimization, unit load device (ULD) and pallet configuration, disruption management, and large-scale predictive modeling. This paper investigates the integration of quantum computing—especially quantum annealing—into supply chain operations. It highlights practical applications such as rapid multi-source data analysis, dynamic alternative routing during disruptions, and greater efficiency in high-velocity logistics. Grounded in established resilience frameworks and emerging technologies, the research illustrates how quantum solutions can significantly cut costs, boost adaptability, and strengthen global supply chain robustness. Drawing from studies on resilience strategies, automotive battery advancements, and intelligent transportation systems, this work positions quantum computing as a vital enabler for next-generation logistics while discussing opportunities, limitations, and the shift from theoretical promise to real-world implementation. Keywords Quantum Computing, Quantum Supply Chain; Logistics Optimization; Quantum Annealing; Supply Chain Resilience; Hybrid Quantum-Classical Algorithms; Unit Load Device Optimization; Disruption Management Quantum Routing.
This research addresses the Vehicle Routing Problem (VRP) with uncertain data and proposes a novel approach using quantum computing techniques. The problem involves optimizing vehicle routes considering uncertain customer demands, time windows, and vehicle capacities. We formulate the problem mathematically and develop an algorithmic framework to tackle it. The approach incorporates multiple scenarios based on the uncertainty distribution and selects the one with the minimum cost to optimize the vehicle routes. Through a numerical example, we demonstrate the effectiveness of the proposed approach in generating optimal routes that minimize the total distance traveled by the vehicles. The results highlight the solution quality, adaptability to uncertainty, and potential benefits in terms of cost reduction and resource utilization. While the computational efficiency of quantum computing approaches is a consideration, this research provides a promising direction for addressing uncertain optimization problems in logistics and transportation. Future research should focus on scalability and refinement of the algorithm to further enhance its applicability in real-world scenarios.
Logistics involves managing how resources are acquired, stored, and transported to their final destinations along the supply chain in a cost-effectively manner. Logistics problems are NP-hard combinational optimization problems involving searching for the best solution in a large solution space. Recent studies have demonstrated the capability of quantum methods to solve such combinatorial optimization problems. Consequently, this paper presents the application of quantum computing methods for two real-world logistics problems, namely, a) Employee transport route optimization and b) Agri-tech logistics optimization. The main goal of this paper is to present the evaluation of the results of the experiments executed on quantum gate-based variational algorithms and quantum annealers. Given the current limitation of quantum hardware, we had to simplify the problem and use case specific decomposition techniques such as distance based clustering to reduce the search space and size of the problem before executing them on quantum computers. The method has been elaborated in this paper. The results were benchmarked with classical exact solvers.