Published Paper Details:

The aim of this paper is to review and compare the Quantum and classical computing models for Supply chain Management. Full-scale optimization problems of practical significance that can have numerous variables and complex objective functions are almost uniformly incompatible with noisy intermediate-scale quantum (NISQ) gear. As a result, there is an expanding body of research that uses scaled-down versions of operations research-related problems to test quantum algorithms. Instead of using this method, we study a challenge with significant commercial value: multi-truck-vehicle routing for supply chain logistics and the back-order prediction. By employing a hybrid workflow strategy, we are able to get around this problem by iteratively allocating routes for trucks by creating a new instance of a binary optimization problem, one truck at a time. It is impossible to completely implement such a task on any foreseeable quantum hardware or simulator. Each instance has about 2500 quadratic binary variables, which is within NISQ quantum computing's capability. After putting the vehicle paths and order predictions suggested by these runs into a very realistic classical supply network simulation, we further reveal similar performance for the entire supply chain.