A novel approach for complex and subjective variable valuation in the COPPE-Cosenza fuzzy model applied to crude oil trading

Numerical algorithms and mathematical methods are regularly used in the commodities and stock markets. However, this so-called quantitative trading has been thus far limited to the paper markets, such as futures, swaps, and options, and excluded a large majority of the physical market. Literature relates successful attempts to use artificial intelligence and neural networks in the physical commodity market. However, the trade of physical oil, so far, does not have a computer program that provides an unquestionable advance that goes beyond the profit margin calculation to include the more complex and partially subjective crude selection factories regularly used by traders. We propose a fuzzy novel approach to deliver a tool to improve the valuations of complex and subjective variables of a well-known hierarchical decision process (the COPPE-Cosenza fuzzy model) now applied to crude oil trade. This work shows that the proposed fuzzy model can be useful for informed decision-making, communication, and management of physical commodities trading. Three European refineries present their monthly crude oil demand in this case study. Offers are successfully evaluated according to numerical and fuzzy factors reflecting physical commodities traders' real-world decision-making process.