A genetic algorithm for one-job m-machine flowshop lot streaming with variable sublots

Abstract

Lot streaming is a technique used to split the processing of lots (batches) into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production makespan. In this technique, a production lot may be split into equal, consistent or variable sublots. Recent literature shows that, when production setup time is considered, significant lead time improvement is possible if variable sublots are used. In this research, however, we noted that lot streaming problems with variable sublots are difficult to solve using off-shelf optimisation packages even for problems of smaller sizes. Thus, efficient solution procedures are needed for solving such problems. In this paper, we develop a hybrid genetic algorithm for a model that appeared in recent literature for one -job m -machine lot streaming problems with variable sublots and setup. Computational results showed that the performance of the proposed genetic algorithm is encouraging.