Heuristic Algorithms for Generalized Vehicle Routing Problem (H-GVRP)

The Generalized Vehicle Routing Problem (GVRP) represents a pivotal advancement in Operations Research, serving as a cornerstone in the optimization of modern logistics and transportation systems. As an extension of the classical Vehicle Routing Problem (VRP), the GVRP introduces customer clustering, allowing a single vehicle to efficiently serve multiple clients within predefined groups. This feature significantly improves route efficiency and reduces operational costs. To address the inherent complexity of the GVRP, this study explores a suite of heuristic and metaheuristic algorithms, including ant colony optimization and genetic algorithms. These approaches are particularly effective in managing heterogeneous fleets with varying capacities and delivery constraints, offering robust and adaptable solutions for dynamic logistics environments. The integration of Internet of Things (IoT) technologies further enhances route planning by enabling real-time communication between vehicles and management systems, thereby improving operational responsiveness. In response to growing sustainability demands, the GVRP framework has evolved to incorporate environmentally conscious strategies, reinforcing its relevance in contemporary logistics research. This work contributes to the field by proposing innovative algorithmic solutions, validating them through computational experiments, and demonstrating their practical impact on cost reduction and carbon footprint minimization. The GVRP remains a dynamic and influential area of study, driving progress in both optimization techniques and sustainable transportation practices.