Methods for Uncertainty Reduction in Complex Logistics Systems in Resource Allocation Across Infrastructure Facilities

The article describes the principles for developing a mathematical framework for decision-making un-der uncertainty. To address this problem, the principle of maximum uncertainty is applied, with Shan-non entropy chosen as the measure of uncertainty. Maximum uncertainty is achieved using Fishburn's estimates, which propose a distribution of a priori probabilities as a quantitative assessment of their degree of preference. A drawback of this approach is that the indicator values of the system's evalua-tion functional are a formal quantity. The subjectivity in applying Fishburn's estimates is reduced by using a developed modification thereof, which focuses on determining an effective solution over the entire possible space of the solution set.