The formalization of the process of servicing trucks at processing points with several stations

Abstract

Today, processing points play a crucial role in the operation of supply chains. The efficiency of such points depends on many factors, including the timeliness of logistics operations, the cost characteristics of such services, and the regularity of vehicle arrivals. Adherence to approved cargo processing schedules (plan-markets) and schedules for the joint operation of vehicles and processing points is not always possible due to several objective factors. That is why this study has sparked the authors' interest in exploring this issue. Goal. Development and justification of a formalized approach to assessing truck waiting times at processing points across several service stations, taking into account the stochastic nature of vehicle arrivals and the relative priorities of their service, aimed at minimizing total time losses in supply chains. Methodology. The apparatus of queueing theory with relative priorities, applied to the formalized description of truck arrival and service processes at processing points with several stations. The study is based on analytical modeling of stochastic request flows and on the evaluation of service waiting time characteristics as a function of arrival intensity and priority structure. Results. The analysis revealed that uneven arrival times and stochastic vehicle servicing result in increased queues, downtime, and schedule disruptions. The formalization of measures to minimize uncertainty involved constructing a model of a queuing system, also known as queueing theory, which takes into account the unevenness of the input flow. Originality. The article proposes a formalised approach to calculating the waiting time for motor vehicle service based on relative service priority. Practical value. The results obtained are of practical importance for optimising the operation of logistics terminals and can be implemented at enterprises in the transport and logistics sector. The perspective for further research is to integrate the proposed approach with enterprise process management systems for dynamic forecasting and adjustment of cargo processing schedules in real time.