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Recently, semiconductor manufacturing fabs tend to reduce the wafer lot size, down to just a few. Consequently, the wafer recipe or wafer flow pattern changes frequently. For such problems, it is impossible to apply conventional prevalent cyclic scheduling methods that repeat processing of wafers in an identical cyclic tool operation sequence. We therefore consider the noncyclic scheduling problem of single-armed cluster tools that process wafers with different recipes. Our proposed method is to transforms the problem into a multiobjective problem by considering the ready times of the resources as objectives to minimize. Only feasible states are generated based on the initial state of the system. These feasible states form a multiobjective shortest path problem and give us as an upper bound for the number of states, where , and are the number of different wafer recipes, wafers, and processing chambers. We solve this problem with implicit enumeration by making our scheduling decisions based on the Pareto optimal solutions for each state. The experimental results show that the proposed algorithm can quickly solve large sized problems including ones with arbitrary initial tool states, changing recipes, reentrant wafer Ωows, and parallel chambers. Note to Practitioners-The scheduling method developed in this paper is designed for scheduling robots used in semiconductor production. The method is able to efficiently represent the state of the manufacturing system and uses this to find an optimal schedule to maximize productivity. The same approach can be used for other manufacturing systems with discrete events. The main advantage of this method is that it can fast find an optimal schedule based on the current state of the system. A long-time horizon can be used because of the high efficiency of the algorithm with respect to the number of jobs. However, the method is best suited for manufacturing systems with few buffers and limited degrees of freedom, in other w- rds highly interconnected systems.