The application of genetic algorithms to lot streaming in a job-shop scheduling problem

A new approach using genetic algorithms (GAs) is proposed to determine lot streaming (LS) conditions in a job-shop scheduling problem (JSP). LS refers to a situation that a job (lot) can be split into a number of smaller jobs (sub-lots) so that successive operations of the same job can be overlapped. Consequently, the completion time of the whole job can be shortened. By applying the proposed approach called LSGAVS, two sub-problems are solved simultaneously using GAs. The first problem is called the LS problem in which the LS conditions are determined and the second problem is called JSP after the LS conditions have been determined. Based on timeliness approach, a number of test problems will be studied to investigate the optimum the LS conditions such that all jobs can be finished close to their due dates in a job-shop environment. Computational results suggest that the proposed model, LSGAVS, works well with different objective measures and good solutions can be obtained with reasonable computational effort.