AN EXPERT SYSTEM THAT SOLVES THE SUGARCANE HARVEST PROGRAMMING PROBLEM: A CASE STUDY IN A MEXICAN SUGAR MILL

This article addresses the problem of sugarcane harvest scheduling generated by human experts between subjective and opposite opinions. Based on the knowledge and experience of sugarcane farmers, and field laboratory data, we present a two-module Expert System (ES) that generates sugarcane harvest schedules to maximize factory efficiency: (a) A Mamdani inference engine with IF-THEN type rules, which determines the level of sugarcane harvest priority and (b) a bi-objective mathematical model solved via an Evolutionary Algorithm (EA) to generate different scenarios that maximize the supply and quality of sugarcane to the sugar mill, and therefore, maximize the operation levels of the factory. This ES is a Decision Support System (DSS) that operations managers of the sugar industry can use to maximize the efficiency of sugar production per ton of sugarcane supplied to a sugar mill. Through a real case study of a Mexican sugar factory, we found proven results of better economic benefits using this innovative DSS.