# Time-Truncated Group Plan under a Weibull Distribution based on Neutrosophic Statistics
## 2 Design of the Proposed Plan using Neutrosophic Statistics
Similarly, the producer risks are assumed to be \(\alpha=0.1\) and \(\alpha=0.05\), and four values of the consumer's risk, namely \(\beta=0.25,\,0.10,\,0.05,\,0.01\), are used. The experiment termination ratios involved in this determination are \(a=0.5\) and \(a=1\). Then, the optimal parameters are reported in Tables 1-4. We can observe the following trends from tables. 1. In most of the cases, the number of groups required for inspection decreases if the constant '\(a\)' increases from 0.5 to 1. 2. For fixed values of \(\delta_{N}\), \(\alpha\), \(\beta\), \(a\), and \(\mu_{N}/\mu_{N0}\), the number of groups increases when group size decreases. There is no particular change in the number of groups when the mean ratio increases. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \multicolumn{3}{c}{\(a=0.5\)} & \multicolumn{3}{c}{\(a=1.0\)} \\ \hline \(\beta\) & \(\mu_{N}/\mu_{N0}\) & \(g_{N}\) & \(c_{N}\) & \(P_{A_{N}}(p_{N1})\) & \(g_{N}\) & \(c_{N}\) & \(P_{A_{N}}(p_{N1})\) \\ \hline  & 2 & [19; 44] & [6; 7] & [0.919, 0.983] & [5; 7] & [7; 8] & [0.904, 0.951] \\  & 4 & [2; 4] & [3, 4] & [0.905, 0.987] & [1, 3] & [4, 5] & [0.910, 0.957] \\
0.25 & 6 & [1, 3] & [2, 4] & [0.919, 0.999] & [1, 3] & [4, 6] & [0.974, 0.999] \\  & 8 & [1, 3] & [2, 4] & [0.956, 1.000] & [1, 3] & [4, 7] & [0.990, 1.000] \\  & 10 & [1, 3] & [2, 3] & [0.974, 0.998] & [1, 3] & [3, 7] & [0.974, 1.000] \\  & 2 & * & * & * & [26, 28] & [8, 9] & [0.924, 0.969] \\  & 4 & [2, 4] & [3, 4] & [0.905, 0.987] & [1, 3] & [4, 6] & [0.910, 0.992] \\
0.1 & 6 & [2, 4] & [3, 4] & [0.969, 0.998] & [1, 3] & [4, 6] & [0.974, 0.999] \\  & 8 & [2, 4] & [2, 3] & [0.915, 0.994] & [1, 3] & [3, 5] & [0.951, 0.999] \\  & 10 & [2, 4] & [3, 4] & [0.994, 1.000] & [1, 3] & [3, 5] & [0.974, 1.000] \\  & 2 & * & * & * & [34, 36] & [8, 9] & [0.902, 0.960] \\  & 4 & [7, 11] & [4, 5] & [0.935, 0.996] & [3, 5] & [5, 7] & [0.930, 0.998] \\
0.05 & 6 & [3, 6] & [3, 4] & [0.954, 0.997] & [2, 4] & [4, 5] & [0.949, 0.993] \\  & 8 & [2, 4] & [2, 3] & [0.915, 0.994] & [1, 3] & [3, 4] & [0.951, 0.991] \\  & 10 & [2, 4] & [2, 3] & [0.948, 0.998] & [1, 3] & [3, 4] & [0.974, 0.997] \\  & 2 & * & * & * & * & * \\  & 4 & [9, 17] & [4, 5] & [0.917, 0.994] & [4, 6] & [5, 6] & [0.908, 0.984] \\
0.01 & 6 & [5, 8] & [3, 4] & [0.925, 0.996] & [2, 4] & [4, 5] & [0.949, 0.993] \\  & 8 & [5, 8] & [3, 4] & [0.968, 0.999] & [2, 4] & [3, 4] & [0.905, 0.987] \\  & 10 & [3, 5] & [2, 3] & [0.923, 0.997] & [2, 4] & [3, 6] & [0.948, 1.000] \\ \hline \end{tabular}  
*: Plan does not exist.  
\end{table} 
Table 1: Optimal parameters of the proposed GASP under neutrosophic statistics when \(r_{N}=\) [10; 12] and \(\delta_{N}=\) [0.9, 1.1]. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \multicolumn{3}{c}{\(a=0.5\)} & \multicolumn{3}{c}{\(a=1.0\)} \\ \hline \(\beta\) & \(\mu_{N}/\mu_{N0}\) & \(g_{N}\) & \(c_{N}\) & \(P_{A_{N}}(p_{N1})\) & \(g_{N}\) & \(c_{N}\) & \(P_{A_{N}}(p_{N1})\) \\ \hline  & 2 & [5; 11] & [2, 3] & [0.926, 0.988] & [2, 4] & [5, 6] & [0.990, 0.995] \\  & 4 & [2, 4] & [1, 2] &