5.2 Performance Optimization Of Different Subsystem In Selected Industries?
5.1.2 Performance Optimization of Different Subsystem in Selected Industries
The failure and repair parameters of each subsystem highly influence the performance of various subsystems/components of industries. In the present work, G.A. is proposed to synchronize the failure and repair parameters of all subsystems for achieving optimal availability. To use G.A. for solving the given problems, the chromosomes are to be coded in real structures. The system parameters are mapped in between the specific bound (lower, upper), where lower bounds depict the minimum value of system parameters and upper bounds show the maximum values. The system has constraint parameters (failure and repair parameters). These parameters are optimized keeping in view the fitness function …show more content…
The population size for this simulation is kept constant and is 100. The simulation results are presented in Table 5.1. The analysis clearly depicts that the availability of the rice milling system is maximum that is 87.51% at generation size 300.
The corresponding values of failure and repair rates for various subsystems of the system (parameters) are at λ1 = 0.00032, µ1 = 0.0068, λ2 = 0.001, µ2 = 0.005, λ3 = 0.001513, µ3 = 0.031, λ4 = 0.00302, µ4 = 0.035, λ5= 0.025, µ5 = 0.05, λ6 = 0.005, µ6 = 0.038, λ7 = 0.005077, µ7 = 0.019, which is the optimized value of these parameters for the maximum availability. The simulation is performed again by varying the population size from 10 to 100 in a step size of 10 and keeping the generation size constant at 100.The optimum value of system performance is 86.90% and for which the best possible combination of failure and repair rate is λ1 = 0.00033, µ1 = 0.0067, λ2 = 0.00106, µ2 = 0.005, λ3 = 0.003, µ3 = 0.030, λ4 = 0.02659, µ4 = 0.03499, λ5= 0.00502, µ5 = 0.049, λ6 = 0.005, µ6 = 0.0379, λ7 = 0.005227, µ7 = 0.0172 as shown in Table
The failure and repair parameters of each subsystem highly influence the performance of various subsystems/components of industries. In the present work, G.A. is proposed to synchronize the failure and repair parameters of all subsystems for achieving optimal availability. To use G.A. for solving the given problems, the chromosomes are to be coded in real structures. The system parameters are mapped in between the specific bound (lower, upper), where lower bounds depict the minimum value of system parameters and upper bounds show the maximum values. The system has constraint parameters (failure and repair parameters). These parameters are optimized keeping in view the fitness function …show more content…
The population size for this simulation is kept constant and is 100. The simulation results are presented in Table 5.1. The analysis clearly depicts that the availability of the rice milling system is maximum that is 87.51% at generation size 300.
The corresponding values of failure and repair rates for various subsystems of the system (parameters) are at λ1 = 0.00032, µ1 = 0.0068, λ2 = 0.001, µ2 = 0.005, λ3 = 0.001513, µ3 = 0.031, λ4 = 0.00302, µ4 = 0.035, λ5= 0.025, µ5 = 0.05, λ6 = 0.005, µ6 = 0.038, λ7 = 0.005077, µ7 = 0.019, which is the optimized value of these parameters for the maximum availability. The simulation is performed again by varying the population size from 10 to 100 in a step size of 10 and keeping the generation size constant at 100.The optimum value of system performance is 86.90% and for which the best possible combination of failure and repair rate is λ1 = 0.00033, µ1 = 0.0067, λ2 = 0.00106, µ2 = 0.005, λ3 = 0.003, µ3 = 0.030, λ4 = 0.02659, µ4 = 0.03499, λ5= 0.00502, µ5 = 0.049, λ6 = 0.005, µ6 = 0.0379, λ7 = 0.005227, µ7 = 0.0172 as shown in Table