Planning and optimization models in ready-made concrete production and logistics

Abstract

This study addresses a multi-criteria decision support problem for the effective management of ready-mix concrete production and delivery planning. The research is conducted considering dynamic market demand, large-scale logistics challenges, and the need for production infrastructure development. The urgency of this work stems from the expected sharp increase in concrete demand due to the reconstruction of destroyed housing, infrastructure, and industrial facilities, in the context of the post-war reconstruction of Ukraine. This surge in demand is likely to exceed the current production capacity of Ukrainian concrete plants. Therefore, these enterprises’ strategic priority is to enhance productivity while maintaining product quality. This study aims to develop a comprehensive framework of optimization and simulation models to support decision-making across a network of concrete plants and construction sites. The main objectives of this study are as follows: (1) to create a systematic representation of logistics processes in concrete production and distribution; (2) to develop a planning and optimization model for the ready-mix concrete supply chain; (3) to design an infrastructure optimization model for the production and distribution network; (4) to build a simulation model for analyzing production and logistics processes; and (5) to perform experiments to evaluate different system operation modes. As a result, several optimization models have been developed. These include a supply chain planning model, a sales network development model, and a coordination model for managing decisions across multiple plants. Additionally, a simulation model was designed to analyze the production and logistics processes. This model can be used to evaluate the efficiency of production and delivery strategies, identify bottlenecks, forecast plant performance under changing conditions, and support decisions to reduce downtime for both plants and customers. Conclusions. The scientific novelty of this research lies in the development of an integrated framework of optimization and simulation models that support production and logistics planning under uncertainty. These models account for production constraints, stochastic demand, variable delivery routes, mix composition, and time limitations. The framework also integrates economic indicators into a dynamic model, enabling real-time assessment of the impact of cost structures, raw material and transport expenses, and other parameters on overall enterprise profitability.