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

Abstract

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.

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

“…For 1|moj(item)|∑ C o , they showed that the total number of jobs in the optimal schedule is the minimum between the number of orders and the number of FOUPs, and the smallest order size first (SOSF) rule is optimal when the number of orders is not greater than the number of FOUPs. Sobeyko and Mönch [48] studied the 1|moj(item)|∑ w o C o and 1|moj(lot)|∑ w o C o . They indicated that the main decision of both problems is job formation since job sequencing can be determined optimally by non-increasing ∑ o∈j w o for moj(lot) and non-increasing ∑ o∈j w o /∑ o∈j a o for moj(item).…”

“…Although some papers seem to use the same metaheuristics, they are quite different because the definition of neighborhood structures and updating schemes are not the same. For example, although both [48,50] applied grouping GA to deal with single-machine MOJ scheduling problems, the crossover, mutation, reinsertion, and mutation procedures are largely not the same. As experiments in [50] showed, the mutation procedure of [48] for tackling ∑ w j C o is not effective for tackling ∑ w j T o .…”

“…For example, although both [48,50] applied grouping GA to deal with single-machine MOJ scheduling problems, the crossover, mutation, reinsertion, and mutation procedures are largely not the same. As experiments in [50] showed, the mutation procedure of [48] for tackling ∑ w j C o is not effective for tackling ∑ w j T o . Hence, to escape from local optima, they designed a distinct mutation procedure.…”

“…Third, due to the increased order-mix and reduced order size, MOJ and interfering job set constraints become common situations in fabs. Currently, most MOJ scheduling problems considered the single-machine environment [47][48][49][50]. Parallel machines [52] and multi-stage shop environments [118] are less investigated.…”