Optimizing Hub Location for Military Equipment: A Robust Mathematical Model for Uncertainty and Meta-Heuristic Approaches

Abstract

This research presents a robust mathematical model for optimizing hub location for military equipment, addressing the inherent uncertainties associated with logistical operations in defense contexts. The model aims to minimize transportation costs and enhance the efficiency of equipment distribution while considering various uncertainties, such as demand fluctuations, transportation delays, and operational constraints. To solve this complex optimization problem, we employ advanced meta-heuristic algorithms, including Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), which are designed to navigate the solution space effectively and provide high-quality solutions within reasonable computational time. The performance of the proposed model is evaluated through a series of simulations, demonstrating its effectiveness in identifying optimal hub locations that ensure timely and cost-effective delivery of military equipment. The first objective is to minimize costs, the second objective is to maximize the fulfillment of demands, and the third objective is to minimize congestion on the routes. Taking into account the parameters in the state of uncertainty, the mathematical model is modeled in a robust state and a robust counterpart model of the problem is proposed. In order to solve the problem on a small scale, the exact weighted sum method (WSM) is used in GAMS software.  The findings highlight the model's potential to improve logistical decision-making in military operations, ultimately contributing to enhanced operational readiness and resource allocation. This study serves as a foundational framework for future research in military logistics optimization under uncertainty.