Tolerancing conducted by design engineers to meet customers' needs is a prerequisite for producing highquality products. Engineers use handbooks to conduct tolerancing. While use of statistical methods for tolerancing is not something new, engineers often use known distributions, including the normal distribution. Yet, if the statistical distribution of the given variable is unknown, a new statistical method will be employed to design tolerance. In this paper, we use generalized lambda distribution for design and analyses component tolerance. We use percentile method (PM) to estimate the distribution parameters. The findings indicated that, when the distribution of the component data is unknown, the proposed method can be used to expedite the design of component tolerance. Moreover, in the case of assembled sets, more extensive tolerance for each component with the same target performance can be utilized.
Data envelopment analysis (DEA) is a useful mathematical tool for evaluating the performance of production units and ranking their relative efficiency. In many real-world applications, production units belong to several separate groups and also consist of several sub-units. In this paper, we introduce a new method of evaluating group efficiency of two-stage production systems. To this end, some new DEA models are introduced for evaluating and ranking groups of production systems based on the average and weakest group performance criteria. Some numerical examples, including an empirical application in the banking industry, are also provided for illustration.
In standard data envelopment analysis (DEA), it is assumed that inputs of a specific production period are used to generate outputs of the same period. However, in some practical examples, time-lag effects exist between inputs and outputs. The inputs of one period are used to generate outputs for several periods, or inputs of several periods are used to create outputs for one period. In this paper, we present some new DEA models for performance assessment of network production systems with time-lag effects. An empirical application in the horticulture sector in Iran shows the usefulness and capabilities of our proposed approach.
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