The application of lot streaming to assembly job shop under resource constraints

Assembly job shop problem (AJSP) is an extension of classical job shop problem (JSP). AJSP first starts with a JSP and appends an assembly stage after job completion. Lot Streaming (LS) technique is defined as the process of splitting lots into sub-lots such that successive operation can be overlapped. In this paper, the previous study of LS to AJSP will be extended by introducing resource constraints. To reduce the computational effort, we propose a new Genetic Algorithm (GA) approach which is the modification of the algorithm in our previous paper. A number of test problems are conducted to examine the performance of the new GA approach. Moreover, the single GA approach will be compared with a single Particle Swarm Optimization (PSO) approach. Computational results suggest that the new algorithm can outperform the previous one and the PSO approach with respect to the objective function.