For this reason, many studies consider electricity price and load level as the main uncertain variables in their scheduling [3, 4, 5, 6, 7, 8, 9, 10, 11, 12].
However, in many practical engineering applications, distributions of some uncertain variables may not be precisely known due to lack of sufficient sample data.
It is easy to know that if an uncertain variable (xisimmathcal {L}(a,b)), i.e., it is subject to a linear uncertainty distribution, then it has an expected value (E[xi]=frac{a+b}{2}). Since the independence between uncertain variables is very important while describing many results, we state it formally.
The component contact stress is a random variable because it is usually affected by many uncertain factors in the production and clamping process.
In the context of Space Situational Awareness, however, the information available to a space analyst on many uncertain components is scarce, preventing their appropriate modeling with a random variable and thus their exploitation in a RSO tracking algorithm.
As a matter of fact, there are many problems in practice that can be casted as uncertain variational inequality problems when there exist uncertain variables (vector) such as in electricity supply network [1], user equilibrium [19] and wireless networks [20].
For constraints with fuzzy stochastic variables, uncertain variables can be transformed into pure quantities by integration of uncertain variables.
This paper proposes an uncertain random assignment problem in which random variables coexist with uncertain variables.
There are two uncertain variables on this process.
We restrict ourselves to a low number of uncertain variables.
