Abstract
Optimization of business processes and policies in a car rental company is an ongoing topic. Especially that each of these companies operates in slightly different local conditions and external environments. Testing new optimization algorithms is best done with simulation methods. A comprehensive rental simulation model can be built, for example, using the SimEvents library of the Matlab/Simulink environment. The paper focuses on the problem of preparing a sequence of customer requests in the short-term car rental system, necessary to carry out simulations in the SimeEvents environment. It was assumed that these data may come from the real world, or they may also be artificial data. A method of generating artificial sequences of customer requests and the structure of input data necessary to carry out this process have been proposed. The use of machine learning to build models that transform real data in such a way that it can be randomized was also tested.
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