Genetic algorithms to solve a single machine multiple orders per job scheduling problem

This research is motivated by a scheduling problem found in 300-mm semiconductor wafer fabrication facilities (wafer fabs). Front opening unified pods (FOUPs) are used to transfer wafers in wafer fabs. The number of FOUPs is kept limited because of the potential overload of the automated material handling system (AMHS). Different orders are grouped into one FOUP because orders of an individual customer very often fill only a portion of a FOUP. We study the case of lot processing and single item processing. The total weighted completion time objective is considered. In this paper, we propose a grouping genetic algorithm (GGA) to form the content of the FOUPs and sequence them. The GGA is hybridized with local search. Furthermore, we also study a random key genetic algorithm (RKGA) to sequence the orders and assign the orders to FOUPs by a heuristic. We compare the performance of the two GAs based on randomly generated problem instances with simple heuristics and other GAs from the literature. It turns out that GGA only slightly outperforms the previous genetic GAs but it is faster when a lot processing environment is considered. The RKGA behaves similar to the best performing GAs described in the literature with respect to solution quality and computing time.