Development of an experimental plan for the truck maintenance process at recycling points

Abstract

The paper discusses the development of a plan for experimental modeling of the process of servicing trucks at cargo handling points, aimed at improving logistics system efficiency and reducing vehicle downtime. The main focus is on simulating vehicle arrival and servicing dynamics under stochastic conditions, considering the principle of relative priority in their order of service. Using empirical data, the laws governing the probability distribution of random variables that characterize freight transport arrival times and service durations were identified. It was found that the arrival process follows the Poisson distribution, and service duration follows the exponential distribution, confirmed by statistical verification using Pearson's criterion.

The paper supports the idea that queuing theory and simulation modeling methods are useful for evaluating the efficiency of cargo processing points. A methodological approach to stochastic modeling of the service process is proposed, based on algorithms for randomly generating arrival intervals and service durations within the Microsoft Excel environment using functions that implement Poisson and exponential distributions. The model incorporates the principle of relative priority, enabling adaptive changes in service sequences based on current operating conditions and economic considerations.

Study results indicate that implementing multi-channel service systems (with one to three stations) can significantly decrease average queue length and waiting times, while increasing system stability under varying traffic levels. This approach allows for predicting how long a vehicle stays in the system, identifying time losses, and providing recommendations for optimal resource allocation at service points. The findings can also be used for further mathematical formalization of logistics performance indicators and applied to other transport networks.

Overall, this research advances analytical and predictive methods in transport logistics, offering opportunities to reduce freight transport downtime and enhance the economic stability of cargo handling systems in current conditions.

Key words: truck, modeling, freight transportation, processing point, queue, relative priority, waiting time.