This article is devoted to the development of a methodology for multicriteria optimization of technological processes based on digital twins. The main problems of modern production are considered, including conflicting goals, requirements for taking into account parameters, and strict technological limitations. The concept of a digital twin is presented as a dynamic virtual copy of a physical object, integrating data from IoT sensors, deterministic models, and an analytics platform. Particular attention is paid to multicriteria optimization methods, including evolutionary algorithms (NSGA-II) and methods for finding compromise solutions based on Pareto optimality. Using the example of optimizing a chemical reactor, the practical implementation of an approach that made it possible to achieve a balance between product yield, energy consumption, and environmental performance is demonstrated. The results of computational experiments are presented, demonstrating the effectiveness of the LPτ-sequence method for global search in multidimensional spaces. Practical recommendations for the phased implementation of the system into production have been developed.
Kravchenko et al. (Wed,) studied this question.