Hybrid MINLP-FNS Framework for Solving Large-Scale LIRP in E-Retail Logistics

Abstract

This research proposes a hybrid optimization framework to address the large-scale, multi-echelon Location-Inventory-Routing Problem (LIRP) in e-retail logistics. The proposed method combines a Mixed-Integer Nonlinear Programming (MINLP) model with a tailored Feasible Neighborhood Search (FNS) algorithm to solve complex decision-making problems involving facility location, inventory control, and vehicle routing simultaneously. Distinct from conventional models, the framework is integrated with a Business Intelligence (BI) environment to enable real-time decision support and dynamic data processing. Experimental evaluations were conducted using realistic logistics scenarios involving four distribution echelons. The results show that the hybrid MINLP-FNS approach achieves a total logistics cost reduction of 13.6% and a runtime improvement of 48% compared to the baseline MINLP-only model. It also significantly outperforms traditional GRG-based methods in scalability and computational stability across large datasets. These findings demonstrate that the proposed hybrid framework offers a more effective and scalable solution for complex logistics optimization, while its BI integration ensures practical applicability in real-world operations. This study contributes a novel data-driven framework that advances current research in intelligent supply chain and optimization systems.