Application of Genetic Algorithm with a Decomposition Procedure for a Large Scale Flow Shop Scheduling Problem

Abstract

In this paper, an effective design of genetic algorithm is proposed for a large scale flow shop scheduling problem with a long scheduling period. The objective of this problem is to minimize the sum of the tardiness for each product. Because the convergence speed in genetic algorithm is slow for large scale problems, a good solution can not be obtained under the limitation of the computation time. In such a case the size of the search space in genetic algorithm should be decreased. Thus three methods are proposed by introducing a decomposition procedure for solving the large scale scheduling problem. In all of the methods the set of products is decomposed into several groups in the order of the due date. This decomposition procedure is based on an idea that a product with an earlier due date should be processed earlier. As a case study a scheduling problem for an electric wire production process is considered. The effectiveness of the proposed methods is examined by a numerical computation carried out on the basis of real operation data.