DETERMINATION OF AN OPTIMAL SETTING AND PRODUCTION RUN USING TAGUCHI'S LOSS FUNCTION

Author(s):  
T. P. M. PAKKALA ◽  
M. A. RAHIM

This paper considers the problem of selecting an optimal setting of the process mean and an optimal production run for a continuous production process. The process is subject to gradual shifts in the process mean due to occurrences of some random shocks. The product output becomes nonconforming only when the process experiences a certain number of accumulated shocks. The changes in the process mean are assumed to follow a nonhomogeneous Poisson process. A quadratic loss function, which is a general form of Taguchi's loss function, is utilized for developing the economic model in determining an initial resetting process mean and an optimal production run. Some new results are derived and some interesting findings are reported.

Author(s):  
SALIH O. DUFFUAA ◽  
ATIQ W. SIDDIQUI

In this paper a process targeting model for three class screening problem is developed. The model developed, extends the work in the literature by incorporating product uniformity. The product uniformity is introduced via a Taguchi type quadratic loss function. Two cases for the process Targeting are considered. In addition, an illustrative example is presented. Sensitivity analysis is also conducted to study the effect of model parameters on expected profit and optimal process mean.


2013 ◽  
Vol 397-400 ◽  
pp. 2565-2569
Author(s):  
Li Yan Tao ◽  
Zu Da Li ◽  
Man Zhang

Discrete manufacture is a non-continuous production process. Accordingly, factors of production capacity are relatively complex. In order to determine the status of enterprise’s production capacity, this paper selects production capacity evaluation indicators extensively from product output capacity, manufacturing resources capacity, dynamic adaptability, and management coordination capacity. A method based on R-Cluster and Coefficient of Variation is proposed, which uses the combination of quantitative and qualitative analysis to classify factors in different aspects. Besides, Coefficient of Variation is applied to screen out the outcome indicators that have a bigger identification capability. Results and analysis show that 46% of indicators can be used as the final indicator system to reflect 98% of original information, which reduce complexity of evaluation.


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