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Multiple assembly sequences can increase the flexibility of assembly systems and consequently lead to better performance. The relationship between multiple assembly sequences and their impact on the performance of assembly systems are studied. Based on the concept of operation flexibility, this paper presents a flexibility measure to evaluate each operation sequence. A flexibility‐based criterion is proposed to prioritize each operation, which is then used to guide the assembly scheduling. A simulation study demonstrates that when using the criterion, higher system flexibility is achieved and consequently better performance of the assembly systems is obtained.

Dispatching rule‐based scheduling is a kind of dynamic scheduling commonly used in real world applications. Because of the lack of scheduling objective, it cannot optimize the specific performances at which shop managers aim in the current production period. To overcome the limitations of the dispatching rule‐based scheduling, an iterative learning scheduling scheme is proposed in this paper. A scheduling objective function, which reflects the performance criteria in which the shop managers are most interested, is established and used to guide the optimization of the crucial performances. According to the value of the scheduling objective obtained from the last simulation period, the parameters are adjusted so as to decrease the objective during the next simulation period. Experimental results show that the iterative learning scheduling overcomes the limitations of the dispatching rule‐based scheduling and achieves higher performances.

The pressurizer is one of the critical components in a nuclear reactor, whose main function is to maintain the primary system pressure within specific boundaries. The pressurizer is a large vessel that is made of carbon or low‐alloy steel shell, clad internally with a layer of stainless steel. It works with high temperature, high humidity and high pressure. The crack defects that would probably appear in the internal surface of the vessel must be detected and recorded, as well as changes to any existing known cracks. This paper presents an implementation of a mobile robot, which can sense and transmit the image of the internal surface to a remote analysis center, where the image is processed and recorded. A neural network‐based pattern classifier is employed to assist inspectors to detect the flaws that appear in the surface of the vessel. Because the real‐life defects rarely exist in the pressurizer, the training of the network becomes very difficult. A new algorithm is exploited to solve the problem. General considerations about the robot design are also presented.