Help setting optimization problem to include more constraints

Dear R-helpers,

I am struggling with an optimization problem at the moment and decided to
write the list looking for some help. I will use a very small example to
explain what I would like to. Thanks in advance for your help.

We would like to distribute resources from 4 warehouses to 3 destinations.
The costs associated are as follows:

          Destination
From     1    2     3     Total
1          1    3     4      300
2          3    2     3      200
3          2    2     1      200
4          1    3     1      200
Total   350  250  300   900

Thus, shipping one unit from warehouse 1 to destination point 3 costs $4.

Let X_{ij} be the number of units to be shipped from warehouse i to
destinaton j (i = 1, 2, 3, 4; j= 1, 2, 3). If c_{ij} is the cost of
shipping one unit from warehouse  i to destination j, the formulation in R
would be as follows:

require(lpSolve)
f <- matrix(c(1, 3, 4, 3, 2, 3, 2, 2, 1, 1, 3, 1), ncol = 3, byrow = TRUE)
row.rhs <- c(300, 200, 200, 200)
col.rhs <- c(350, 250, 300)
row.signs <- rep("==", length(row.rhs))
col.signs <- rep("==", length(col.rhs))
lp.transport(f, "min", row.signs, row.rhs, col.signs, col.rhs)
D <- lp.transport(f, "min", row.signs, row.rhs, col.signs, col.rhs)
D$solution
#      [,1] [,2] [,3]
#[1,]  300    0    0
#[2,]    0  200    0
#[3,]    0   50  150
#[4,]   50    0  150

Thus, we will ship 300 units from point 1 to destination 1; 200 from point
2 to destination 2 and so on, and the cost of this distribution plan is
$1150. However, I would like to add the following two constraints:

# 1.  weighted sums by column
# w is a vector of known constants, i.e., w = c(1.2, .9, .7, 2.3)
# r is also known, i.e., r = 4
w1*x11 + w2*x21 + w3*x21 + w4*x41   == r    # col 1

w1*x12 + w2*x22 + w3*x32 + w4*x42   == r    # col 2
w1*x13 + w2*x23 + w3*x33 + w4*x43   == r    # col 3

# 2. By column, the number of X's greater than zero should be two or
greater. In this small example, this condition is satisfied, but I would
like to make sure that it is also satisfied in my problem.

# 3. Using lp.transport(), the function to be minimized is linear, i.e., Z
= c11*x11 + c12*x12 + ... + c42*x42 + c43*x43. However, in my case, I am
interested in minimizing the standard deviation of g = c(l1, l2, l3), with

l1 = w1*x11 + w2*x21 + w3*x21 + w4*x41
l2 = w1*x12 + w2*x22 + w3*x32 + w4*x42
l3 = w1*x13 + w2*x23 + w3*x33 + w4*x43

and w as previously described.