Title: A genetic algorithm for one-job m-machine flowshop lot streaming with variable sublots
Authors: Fantahun M. Defersha, Mingyuan Chen
Addresses: Department of Mechanical and Industrial Engineering, Concordia University, 1455 de Maisonneuve West, Montreal, Quebec, H3G 1M8, Canada. ' Department of Mechanical and Industrial Engineering, Concordia University, 1455 de Maisonneuve West, Montreal, Quebec, H3G 1M8, Canada
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.
Keywords: flow shops; lot streaming; variable sublots; genetic algorithms.
International Journal of Operational Research, 2011 Vol.10 No.4, pp.458 - 468
Published online: 14 Feb 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article