Abstract
Despite the growing economic importance of the fragrance industry, perfume formulation remains largely guided by empirical knowledge and iterative trial-and-error approaches. The structured design of fragrances, typically organised into top, middle, and base notes through the blending of perfume raw materials, is therefore time-consuming, costly, and difficult to generalise. Existing computational approaches have begun to address this challenge, but are commonly limited to small ingredient sets or require extensive sensory data that are not always available. This work proposes a computer-aided optimisation framework for Eau de Parfum formulation that simultaneously maximises perceived olfactory intensity and minimises formulation cost. The resulting optimisation problem is formulated to preserve the structural balance of top, middle, and base notes inherent to the perfume pyramid. Application of the framework to a fruity-floral Eau de Parfum formulation demonstrates a substantial increase in perceived intensity alongside a reduction in total formulation cost of approximately 64%. The proposed approach is intended as a decision-support tool for perfumers, enabling rapid exploration of cost-intensity trade-offs once a candidate ingredient set has been defined. The framework offers a step towards more systematic, data-driven perfume engineering and provides a foundation for future extensions incorporating ingredient selection and sensory constraints.