Expected value of the Max of three exponential random variables So the question asks:

Let $X_1,X_2,X_3\sim \operatorname{Exp}(\lambda)$ be independent (exponential) random variables (with $\lambda> 0$).
  (a) Find the probability density function of the random variable $Z = \max \{X_1,X_2,X_3\}$.
  (b) Let $T = X_1+X_2/2+X_3/3$, use moment generating functions to prove $Z\sim T$ (same distribution). Find $E[Z]$ and $\operatorname{Var}[Z]$.

So far I got: 
(a)$F(x) = 1-e^{-\lambda x}$
$F_Z(z) = P (Z \leq z) = P(\max(X_1,X_2,X_3) ≤ z) = P(X_1\leq z, X_2 \leq z, X_3 \leq z)= P(X_1\leq z)P(X_2\leq z) P(X_3\leq z) = (1-e^{-\lambda z})^3$
$f_Z(z) = F_Z'(z) = (1-e^{-\lambda z})^3 =3\lambda e^{-3\lambda z}(e^{\lambda z}-1)^2$
(b) for this part, I did not quite understand what it wanted me to prove actually...
I got: $M_X(t) = λ/(λ-t )$
$M_Z(t) = M_{X_1}(t)M_{X_2}(t) M_{X_3}(t) = [\lambda/(\lambda-t )]  [\lambda /(2(\lambda-t) ]  [\lambda /(3(\lambda-t) ]=  [\lambda/(\lambda-t)]^3/6$
So what does it mean by  proving $Z\sim T$ (same distribution) ?
And for the $E[Z]$ and $\text{Var} [Z]$, I actually tried to do it using the standard method which is $$
E[Z]=\int z\cdot3\lambda e^{-3\lambda z}(e^{\lambda z}-1)^2dz
$$
which becomes super complicated...
So is there a simple way to calculate the $E[Z]$ and $\text{Var} [Z]$ without literally solving the integration? 

(b) Just means use MGFs to show that $Z$ and $T$ have the same distribution.

So is there a simple way to calculate the $E[Z]$ and $\text{Var} [Z]$ without literally solving the integration?

Yes. Intuitively, if you have $3$ light bulbs with lifetimes that are iid exponential $\lambda$, then $Z$ is the time until the last light bulb burns out. In other words, we wait for the first of three to burn out ($Y_3$), then we wait for the first of two to burn out $(Y_2)$, then we finally wait for the last to burn out $(Y_1)$.
Notice (prove for yourself), that the minimum of $n$ iid exponential random variables follows an exponential distribution with mean $1/(n\lambda)$. Hence, we have
$$E[Z] =E[Y_3+Y_2+Y_1] = E[Y_3]+E[Y_2]+E[Y_1] = \frac{1}{3\lambda}+\frac{1}{2\lambda}+\frac{1}{\lambda} = \frac{1}{\lambda}\sum_{k=1}^3\frac{1}{k}.$$
Use independence and argue similarly for the variance.
In fact, this is the exact same thing as the $T$ they describe.;
$$X_1+X_2/2+X_3/3 \overset{d}{=} Y_3+Y_2+Y_1.$$
