Exact(8)
We present experimental results on different robotics domains that simulate tasks on stochastic environments.
However, the research on the time-cost trade-off problem seldom concerns stochastic environments.
RL addresses sequential decision problems in initially (possibly) unknown stochastic environments.
The diagnosis system is based on output observers designed in both deterministic and stochastic environments.
As a result, the algorithm can be used to solve GTSP in fuzzy, rough and stochastic environments.
In summary, it is clear that in spite of many contributions to the stock control, there is little consideration regarding production-inventory models in stochastic environments.
Numerical and graphical results are used in a case study to evaluate and compare the optimal solution derived for both deterministic and stochastic environments.
In Section 3, we give some numerical examples and make a comparative analysis of the stability of the model system within deterministic and stochastic environments.
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