## 4 Results and Discussion  
This study is a simulation study, and the genetic algorithm used was aimed to find the optimal as well as sub-optimal layouts. Depending on the fitness function, different layout redesigns are considered, and for these layout redesigns, the patient demand is varied \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Empirical Data**} & \multicolumn{2}{c}{**Simulation Results**} & \multicolumn{2}{c}{\(p\)**-Value**} \\ \hline  & **Mean** & **SD** & **Mean** & **SD** & \\ \hline Waiting time in minutes & 59.5 & 43.92 & 58.2 & 40.15 & 0.4 \\ Cycle time in minutes & 113.26 & 44.2 & 115.32 & 39.6 & 0.29 \\ \hline \hline \end{tabular} SD: Standard Deviation.  
\end{table} 
Table 1: Simulation model validation in terms of waiting time and cycle time. Figure 4: Complete optimization model. and the performance parameters are calculated. The number of solutions provided by the algorithms differ (See Table 2). The layout redesigns/solutions obtained are as shown in Figure 5 as per the walking steps. Lesser the number of steps in the layout, more is the flexibility required to accommodate the changes in the OPC. The performance or the efficiency of the OPC depends on processing times, waiting times, cycle times, throughput, resource utilization and operational costs. The waiting times and the cycle times also depend on the resource utilization (in this research, it is the space utilization). The current layout with existing pathways and patient demand was evaluated. The current layout requires patients to walk for 295 steps to complete their care and the current patient demand is 2300 patients/day. The approximate layout is depicted (not to the scale) in Figure 6.  
All the layout redesigns obtained from GA were evaluated for the current scenario. The waiting times and cycle times for different layouts were recorded by varying patient demand by +20% and +40% of the current demand. The solutions and their performance parameters are as shown in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Number of Solutions/Lyouts** & **Distance (in Steps) According to the Solutions** \\ \hline Genetic Algorithm & 12 & 305, 211, 251, 260, 296, 228, \\  & & 237, 235, 677, 675, 1101,1099 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distances travelled in the layouts obtained by genetic algorithm. Figure 5: Layout redesigns obtained from Genetic Algorithm. The GA provided 12 solutions and all the redesigns were used in simulating the waiting times and cycle times for different patient demand. It is observed that out of twelve solutions/layout redesigns, six layouts have a greater number of walking steps compared to the existing one (295 steps), and this will ultimately result in more travel time. It is observed that the other solutions have a smaller number of walking steps, and also the changes required while implementing the redesigns is less. There is a tradeoff between the number of walking steps and the flexibility of the OPC to change the layout accordingly. The waiting time and the cycle times for the OPC layout with minimum distance layouts are compared with the same parameters in the existing layout in Table 4. The ANOVA test (\(p<0.05\)) was used for statistical significance analysis. The results show that the physical layout of the outpatient clinic has impact on the workflow management: patient flow, waiting time and cycle time. The waiting time has reduced by 36.3.28% (21.6 min) and the cycle time has reduced by 14.17% (16.06 min) The staff scheduling and the patient scheduling was retained as per current scenario. The optimal solution obtained resulted in reduction in walking distance from 295 to 211. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **With Existing Layout** & **Best Layout with GA** \\ \hline Wait time in minutes & 59.5 & 37.9 \\ Cycle time in minutes & 113.26 & 97.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of wait time and cycle time in existing layout and optimized layout.