A Coarse-Grain Parallel Genetic Algorithm for Flexible Job-Shop Scheduling with Lot Streaming

Lot streaming is a technique of splitting lots into sublots to allow the overlapping of successive operations in a multi-stage manufacturing system. In this research, we present a course-grained parallel genetic algorithm to solve a lot streaming problem in a flexible job-shops environment. We consider routing flexibility, sequence dependent setups, attached or detached nature of setups, machine release dates and lag times in an integrated manner. The proposed parallel genetic algorithm was implemented based on island-model parallelization techniques with different connection topologies. Numerical examples are presented to illustrate the computation behavior of the parallel genetic algorithm.