Scheduling flow shops using differential evolution algorithm

This paper describes a novel optimization method based on a differential evolution (exploration) algorithm and its applications to solving non-linear programming problems containing integer and discrete variables. The techniques for handling discrete variables are described as well as the techniques needed to handle boundary constraints. In particular, the application of differential evolution algorithm to minimization of makespan, flowtime and tardiness in a flow shop manufacturing system is given in order to illustrate the capabilities and the practical use of the method. Experiments were carried out to compare results from the differential evolution algorithm and the genetic algorithm, which has a reputation for being very powerful. The results obtained have proven satisfactory in solution quality when compared with genetic algorithm. The novel method requires few control variables, is relatively easy to implement and use, effective, and efficient, which makes it an attractive and widely applicable approach for solving practical engineering problems. Future directions in terms of research and applications are given.