Aggregate Blending Using Genetic Algorithms

Abstract:  Aggregate blending consists of finding the proportions of fractions to form a final blend satisfying predefined specifications. It is a problem which is posed in many ways, and solved by using different techniques. These techniques range from simple graphical methods to advanced computer methods such as nonlinear programming or dynamic programming. In this article, an aggregate‐blending problem is formulated as a multiobjective optimization problem and solved by using genetic algorithms (GAs). It is shown that in this way all existing formulations of an aggregate‐blending problem can be covered and solved. The effectiveness of this new application is demonstrated through numerical examples. The technique is shown to be quite versatile in tackling multiple objectives including cost minimization, and approaching at best a given target curve. Linear and nonlinear cost functions can be dealt with equal ease; additional objectives may be inserted into the problem with no difficulty. The user has the possibility of defining and finding the best solutions with Pareto optimality considerations.