Load balancing application of the genetic algorithm in a nonstationary environment

Abstract

There is a wide range of industrial activities involving load balancing problems and there is currently no general approach to such problems. The genetic algorithm, (Holland 1975), has proved to be successful on optimisation problems and is often used in conjunction with other methods. This paper describes and compares two methods which use the genetic algorithm to balance the load of the presses in a sugar beet pressing station. Because the station is a time varying system, possibilities of tracking changing environment has to be considered and an adaptive strategy is needed.The first approach uses the genetic algorithm to optimise an on-line mathematical model of the station and the use of this model to maximize the percentage of dry substances in the pressed pulp produced. The second approach implements the genetic algorithm in direct control of the station with the identical goal of reducing the moisture content of the pressed pulp. This reduction improves energy efficiency of the driers which dry the pulp to be used as animal feed.