Having a last decision is getting difficult while working in bigger construction projects. Because decison-makers have to take into consideration multi-objectives such as; time, cost, environment and safety simultaneously. To overcome these problems, we should use multi-objective optimization methods. These methods assist managers while having important decisions. We can evaluate different combinations and the results and we can find our Pareto front with these methods.
These methods need high computational capacity because they try to calculate all possible combinations within upper and lower bounds. Therefore, powerful computers should be uses for these optimizations. In the end, we can see our best results in the Pareto Front values.
We used nsga2() function for the optimization. We also used genetic algorithms and fitness functions. We combined bridge and track values at first during this optimization. The blue line in Figure 1 is our Pareto Front. In Figure 2. the duration and the distance between the interventions are visualised.We can’t get the optimal solution.
Figure 1. Pareto Front Graph
Figure 2. Possible Solutions
Next, we analyzed the combination of bridge and cable car and came up with the following results.where the red line is the optimal solution and the blue line is the non-optimal solution.
It can be seen that the combination of bridge and cable car is the optimal solution.
As can be seen from the third part, the combination of bridges and retaining walls is not environmentally friendly, so it will not be analyzed here.