Decision-Making Algorithm for the Analysis of the Development Process of A New Asphalt Mixture

The paper explores the sensitivity of the Laplace and Hurwicz algorithms to the estimated "performance indicator," evaluated through working alternatives (Ai) and performance scenarios (Sj). This sensitivity underscores the need for accurate data and well-founded assumptions in the decision-making process. The Laplace algorithm identifies the best alternative by maximizing expected performance, assuming equal probability across all scenarios. On the other hand, the Hurwicz algorithm incorporates an optimism coefficient (α), allowing for flexible decision-making by assigning different values to alternatives, particularly in areas like reducing manufacturing and transport costs, and CO2 emissions, per tonne of novel asphalt mixture. The study's key contributions lie in the development of modified decision optimization strategies that consider the varying significance of different alternatives by applying weighted coefficients to each performance scenario. These algorithms utilize a utility function tied to the performance indicators, providing a robust framework for analysing such complex problems. It is shown that the final decision is shaped not only by the chosen algorithm but also by the weighting of performance scenarios, which reflect their relative importance in the decision process.