Exploring Vehicle Scheduling in Web 3.0-based Vehicular Networks with Time Window Constraints

The Vehicle Scheduling Problem (VSP), rooted in the Traveling Salesman Problem (TSP), poses a significant challenge in modern logistics, particularly within networks linked to manufacturing facilities. These logistics systems often grapple with increased shipping costs and encounter challenges in aligning service times with customer availability. In the context of Web 3.0, which emphasizes decentralized and interconnected systems, there is a growing need to optimize vehicle scheduling to meet the demands of this new paradigm.In response, this study introduces a comprehensive vehicle scheduling model that strategically incorporates both time windows and vehicle load constraints, taking into account the unique characteristics of Web 3.0 logistics networks. To address this model, we employ genetic algorithm methodologies, providing a detailed examination and explanation of the algorithmic processes involved. The genetic algorithm undergoes 100 iterations, refining the solution iteratively. Implementation of the model is executed using the MATLAB toolbox. Simulation computations are conducted, revealing that setting time windows and deadweight defaults to zero minimizes the overall path traversing all service points. This outcome validates the efficacy of the algorithm in mitigating challenges akin to those faced by traveling agents in Web 3.0 logistics scenarios.This research holds practical significance, offering insights that can be applied to optimize logistics routes in real-world production scenarios within the Web 3.0 landscape. The study's findings contribute to the judicious selection and enhancement of logistics operations, addressing cost concerns and refining service quality. The integration of time windows and load constraints proves pivotal in achieving operational efficiency and customer satisfaction within the Web 3.0 context.