[2603.29543] Reducing Complexity for Quantum Approaches in Train Load Optimization

Quantum Physics arXiv:2603.29543 (quant-ph) [Submitted on 31 Mar 2026] Title:Reducing Complexity for Quantum Approaches in Train Load Optimization Authors:Zhijie Tang, Albert Nieto-Morales, Arit Kumar Bishwas View a PDF of the paper titled Reducing Complexity for Quantum Approaches in Train Load Optimization, by Zhijie Tang and 2 other authors View PDF HTML (experimental) Abstract:Efficiently planning container loads onto trains is a computationally challenging combinatorial optimization problem, central to logistics and supply chain management. A primary source of this complexity arises from the need to model and reduce rehandle operations-unproductive crane moves required to access blocked containers. Conventional mathematical formulations address this by introducing explicit binary variables and a web of logical constraints for each potential rehandle, resulting in large-scale models that are difficult to solve. This paper presents a fundamental departure from this paradigm. We introduce an innovative and compact mathematical formulation for the Train Load Optimization (TLO) problem where the rehandle cost is calculated implicitly within the objective function. This novel approach helps prevent the need for dedicated rehandle variables and their associated constraints, leading to a dramatic reduction in model size. We provide a formal comparison against a conventional model to analytically demonstrate the significant reduction in the number of variables and constraints....