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Microeconomic optimisation of scheduled public transport operations has traditionally focused on finding optimal values for the frequency of service, capacity of vehicles, number of lines and distance between stops.
The analytical approach allows for a fast solver, which can then be used for applications such as statistical uncertainty and sensitivity analysis, real-time parameter space exploration, and finding optimal values for design parameters.
The response surface methodology provides a relationship among the parameters which can be utilized in finding optimal values.
To summarize, the combined method outperforms the others with respect to quickly finding optimal values for branches' rates and times.
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This problem could be addressed by implementing fuzzy systems, GA, or other optimization methods, finding optimal parameter values.
In particular, the dynamics of the system includes parameters, i.e., unspecified constants for which we want to observe the effect on the dynamics, with the purpose of finding optimal design values.
Actually projection pursuit classification model is a dimensional reduction technology, with optimizing projection index function and finding optimal projection value as its core problem.
Many real world applications consist of finding optimal inputs (design variables) to the system that yields in desirable values for stochastic outputs (Responses).
While pbest is the best values of each particle and it is utilized for finding optimal solution, gbest is the best solution of the group.
This is achieved by finding optimal splits of some suitable explanatory variables at a given threshold value, such that a defined impurity function is minimised.
Value iteration algorithm is one of the most widely standard iterative methods used for finding optimal or approximately optimal policies in discounted MDPs.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com