عنوان مقاله [English]
In recent years, applications of quality characteristics, which take different values in time, have been widely used. These quality characteristics are called time oriented quality characteristics, and can be defined for different types of quality characteristic; such as, nominal the best (NTB), the larger the better (LTB) and the smaller the better (STB). For these quality characteristics, a target profile is defined and the goal is to adjust the control factors of the product in order to have a profile as close to the target profile as possible. The main application of this type of quality characteristic is in pharmaceutical industries. The best design for drug components is searched based on the properties of the drug, such as drug release in the human body, which must follow the target profile. Based on the input data, different approaches may be used. When the entire set of data is available and the relationship between a quality characteristic and the independent variables is defined, approaches such as minimizing the total mean square error, minimizing the sum of quality loss function and defective costs, desirability function, and etc. may be used to optimize control factor settings. However, in some situations with less available information or complex relationships, these approaches cannot be applied, and the only way is to compare the profiles obtained from different design parameters with the target profile to select the appropriate profile and the control factor designs. The most applicable indices for comparing the profiles of time oriented quality characteristics are f1 and f2, which were introduced in pharmaceutical papers for comparing the profiles of drug release with the target profile. These indices are applied for NTB quality characteristics and do not have any application to the LTB and STB quality characteristics. In this research, a new approach for selecting the appropriate profile among existing profiles for LTB and STB quality characteristics is introduced. The proposed method is evaluated by numerical examples. The results show the high ability of the suggested approach in selecting the appropriate profile, based on user policies.