Review of paper 1

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1. Introduction Design variables are important to be conducted the appropriate experiment analyzing and getting the accurate values for integer, discrete, zero-one (binary), and continuous variables. The researchers should classify design factors before the experiment is conducted. In literature, there are several factors such as quantitative, qualitative, discrete, continuous, zero-one (binary), non-zero-one (non-binary), controlled and uncontrolled variables (Sanchez & Wan, 2009). Quantitative variables get numerical values. On the other hand, qualitative variables do not get numerical values, and they classify the values of the variables. Discrete variables can get only determined separated values that should be a non-negative integer, and it is possible that discrete variables have with some upper bounds. In contrast, continuous variables can get any real value between the ranges. Zero-one (binary) variables get just two variables 0 or 1 such as defective or non-defective products. On the contrary, non-zero-one (non-binary) variables can get more than two values. If all factors in the experiment are handled they are called controlled variables. Otherwise, they are called non-controlled variables. The mixed-integer nonlinear programming (MINLP) models can use some variables that can be integer, discrete, zero-one (binary) and continuous. In this study, we make mention of all the variables for the appropriate rotatable central composite design (CCD) using the MINLP model that is why classifying the variables are important to this research paper. Box & Wilson (1951) firstly introduced response surface methodology (RSM), and Box & Hunter (1957) further developed RSM. After that, RSM was developed by Box & Draper (1987), Khuri ... ... middle of paper ... ... al. (2011) gives a mixed integer programming (MIP) method which is useful for constructing orthogonal designs. Kohli and Singh (2011) investigated the experiment plan that was based on the rotatable property using the central composite design (CCD). Kohli and Singh (2011) design outcomes indicated that their proposed mathematical models could explain the performance indicators with the factors limits being examined. Vieira et al. (2013) gave a mixed integer programming formulation that lets us to make efficient, almost orthogonal, almost balanced designs for mixed factor problems. These problems call nearly orthogonal-and-balanced (NOAB) designs (Vieira et al., 2013). Generic nonlinear programming (NLP) problems hold continuous or integer variables, but mechanical design optimizations usually include continuous, binary, discrete and integer variables (Garg, 2014).

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