Quantum Powered Employee Transport and Agri-Logistics Optimization
Rahul Rana, Rohit Thingbaijam, Janani Seshadri, Pranav Shah, Aniket Sinha, Sudhakara Poojary · 2022
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.