This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication facilities. With respect to some practical motivated process constraints, like equipment dedication and unequal batchsizes, we model the problem as unrelated parallel batch machines problem with incompatible job families and unequal ready times of the jobs. Our objective is to minimize the total weighted tardiness (TWT) of the jobs. Given that the problem is NPhard, we propose two different solution approaches. The first approach works with a time window-based mixed integer programming (MIP) decomposition. The second approach uses a variable neighbourhood search (VNS). Using randomly generated test instances, we show that the proposed algorithms outperform common dispatching rules that cannot deal with the given constraints effectively. INTRODUCTIONThe planning and optimization of semiconductor manufacturing is a very complex task. Especially, in the field of wafer processing in the front-end a lot of different processing steps have to be performed. These steps are, for example, typical batch tool operations like oven processes and wet-etch processes, or typical cluster tool operations like dry-etch, implant or lithography processes having complex setup strategies. A batching machine allows that several jobs can be processed at the same time. Because of several specific constraints and dependencies, it is challenging to schedule the jobs. Moreover, meeting customer due dates is one of the important manufacturing objectives. Because of the complex nature of the process, the customer due date is set for each operation as operation due date (ODD) (cf. Rose 2003). The task to be solved consists in meeting these due dates for each job within each work center as good as possible with respect to different job priorities.In this paper, we focus on diffusion and oxidation operations which are performed on batch machines, i.e. furnaces. Because of the long processing times of batch tools an effective scheduling of the furnace operation has an huge impact on global manufacturing objectives (cf. Mehta and Uzsoy 1998). Though several jobs can be processed simultaneously on these batch processing machines, the process restrictions require that only jobs belonging to the same family can be processed together at the same time. Further process restrictions specify that not all families can be processed by every machine (equipment dedication) and that machines can have different capacities. These equipment dedication constraints mainly represent different equipment qualifications. Especially for research and development wafer fabs with high product mixtures and constantly new developments, these constraints make the manufacturing control more sophisticated. In addition, the jobs to be processed have different priorities/weights, due dates, and ready times. So, in the case of unequal ready times, it is sometimes advantageous to form a non-full batch while in other situations it is a better strategy to wai...

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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.