A fair comparison of representations, operators and algorithms for the sugarcane harvest problem

This paper instantiate the sugarcane harvest problem as a multiple knapsack problem incorporating logistic data in its formulation. Different combinations of data representations, genetic operators and multi-objective (MO) evolutionary algorithms to solve the problem are evaluated. The proposed approach produced results that considered aspects such as output quality (i.e. relevance to decision maker), solutions spread and run-time. Tests carried out have used real data from two sugarcane mills. Finally, a MO interpretation of generated results is also suggested.