SIMULATION MODEL OF ROAD AND RAIL TRANSPORT INTERACTION IN LOGISTICS HUBS CONSIDERING OPERATIONAL RISKS
Abstract
Purpose. The purpose of the study is to develop an object-oriented discrete-event simulation model of a warehouse complex to create a foundational framework for further research and optimization of the interaction between road and rail transport within a metal distribution center. Methods. The study employs object-oriented programming methods, discrete-event simulation modeling, queuing theory, and mathematical statistics methods to identify probability distribution laws and evaluate model adequacy using the non-parametric Van der Waerden test. Results. The article outlines the methodological principles for formalizing and constructing a discrete-event simulation model of metal distribution logistics hubs. The technological features of cargo handling operations in such facilities and the impact of operational risks on process management are examined. A methodology is presented for modeling a warehouse complex as a complex multi-channel stochastic queuing system using an object-oriented approach. The class hierarchy covering loading fronts, crane resources, inventory stocks, and transport flow units is described. A dynamic daily algorithm for calculating service priorities based on a scoring system is formulated to optimize queue handling order and minimize the impact of operational risks on the metal distribution center processes. The principles of identifying probability distribution laws for incoming transport flows and unloading durations across various metal product categories based on time-study observations are detailed. The results of validating the model's mathematical framework using the non-parametric Van der Waerden test are presented. Scientific novelty. The scientific novelty lies in the further development of the conceptual and algorithmic architecture of a discrete-event warehouse simulation model which, unlike existing analytical queuing models and procedural algorithms, simultaneously accounts for the stochastic dynamics of servicing diverse cargo flows, shared crane resource conflicts at loading fronts, and a multi-criteria queue service discipline. Practical value. The practical value resides in establishing a decision-support toolkit for analyzing and optimizing road-rail transport interaction within a metal distribution center while taking operational risks into account.