Today's production meets the challenges of an extremely changeable market demands which are characterized by large fluctuations in the variety and quantity of products. It is not enough to produce high quality products at an affordable price but it is necessary to be adaptable to the sudden changes dictated by the market in an economical way. One of the answers to such demands is to set up responsive and cost-effective production systems in a way to ensure a certain level of flexibility and autonomy through simultaneous management of the product life cycle and production processes. Reconfigurable manufacturing systems, as a new paradigm represents a system model that enables just such efficiency due to the possibility of adjusting production capacity and functionality. An important feature of such model is scalability. It provides, unlike other models, a precise adjustment of production capacity, which solves the problem of increasing or decreasing them in a dynamic way. The purpose of this doctoral thesis is to expand the knowledge in the field of research of such production systems with an emphasis on scalability and dynamic implications that cause rapid changes in production capacity. The advantages and disadvantages of current knowledge in the field of production systems research and the possibilities of adapting such systems to new market trends will be analyzed and identified. Attempts will be made to integrate new features provided by reconfigurable production systems, primarily scalability, in order to create a complete solution for the design of production systems. The main goal of the research is to create a dynamic model by applaying a genetic algorithm for designing and calculating the production capacity of an optimal multi-product reconfigurable production system for processing the product family.