A machine learning and linear programming aided approach to wine ranking and selection

Abstract

Purpose This paper aims to mitigate the subjective nature of wine rating by introducing statistical and optimization tools for analysis, providing a unique approach not found in existing literature. Design/methodology/approach The research uses an unsupervised machine learning algorithm, k-means, to cluster wines based on their chemical characteristics, followed by the application of the PROMETHEE II multicriteria decision-making model to rank the wines based on their sensorial characteristics and selling price. Lastly, a linear programming model is used to optimize the selection of wines under different scenarios and constraints. Findings The study presents a method to rank wines based on both chemical and sensorial characteristics, providing a more comprehensive assessment than traditional subjective ratings. Clustering wines based on their characteristics and ranking them according to sensorial characteristics provides the user/consumer with meaningful information to be used in an optimization model for wine selection. Practical implications The proposed framework has practical implications for wine enthusiasts, makers, tasters and retailers, offering a systematic approach to ranking and selecting/recommending wines based on both objective and subjective criteria. This approach can influence pricing, consumption and marketing strategies within the wine industry, leading to more informed and precise decision-making. Originality/value The research introduces a novel framework that combines machine learning, decision-making models and linear programming for wine ranking and selection, addressing the limitations of subjective ratings and providing a more objective approach.