OMPR - Writing scalable constraints elegently - optimization

Question

Just a brief background:
There are 10 cement plants producing two kinds of products (OPC, PPC) supplying material to d demand locations by road or by rail (which can be loaded in multiples of x tonnes) whose cost matrix is LC (rows - Source, columns - Demand destinations). Since the source is defined as a combination of plant, product, and transport mode while the constraint is at a plant product level - I came up with this hack to combine constraints accordingly
Below is the working code for the same.
library(ompr)
library(ompr.roi)
library(ROI.plugin.glpk)
library(listcomp)
is_connected <- function(i, j) {
LC[i, j] > 0
}
railroutes = grep(":W:",rownames(LC))
d = demand[demand$DPP %in% colnames(LC),"DEMAND"]
rakemultiplier = capacity[capacity$SOURCE %in% rownames(LC),"Multiplier"]
p1OPC = which(grepl(Plants[1],rNames)&grepl("OPC",rNames))
p2OPC = which(grepl(Plants[2],rNames)&grepl("OPC",rNames))
p3OPC = which(grepl(Plants[3],rNames)&grepl("OPC",rNames))
p4OPC = which(grepl(Plants[4],rNames)&grepl("OPC",rNames))
# p5OPC = which(grepl(Plants[5],rNames)&grepl("OPC",rNames))
# p6OPC = which(grepl(Plants[6],rNames)&grepl("OPC",rNames))
p7OPC = which(grepl(Plants[7],rNames)&grepl("OPC",rNames))
p8OPC = which(grepl(Plants[8],rNames)&grepl("OPC",rNames))
p9OPC = which(grepl(Plants[9],rNames)&grepl("OPC",rNames))
p10OPC = which(grepl(Plants[10],rNames)&grepl("OPC",rNames))
p1PPC = which(grepl(Plants[1],rNames)&grepl("PPC",rNames))
p2PPC = which(grepl(Plants[2],rNames)&grepl("PPC",rNames))
p3PPC = which(grepl(Plants[3],rNames)&grepl("PPC",rNames))
p4PPC = which(grepl(Plants[4],rNames)&grepl("PPC",rNames))
p5PPC = which(grepl(Plants[5],rNames)&grepl("PPC",rNames))
p6PPC = which(grepl(Plants[6],rNames)&grepl("PPC",rNames))
p7PPC = which(grepl(Plants[7],rNames)&grepl("PPC",rNames))
p8PPC = which(grepl(Plants[8],rNames)&grepl("PPC",rNames))
p9PPC = which(grepl(Plants[9],rNames)&grepl("PPC",rNames))
p10PPC = which(grepl(Plants[10],rNames)&grepl("PPC",rNames))
set.seed(40)
a = Sys.time()
model <- MIPModel() |>
add_variable(x[i, j], i = 1:nrow(LC), j = 1:ncol(LC), is_connected(i, j), lb = 0, type = "continuous") |>
add_variable(y[i], lb = 0, type = "integer", i = railroutes) |>
add_variable(z1[j],lb = 0,ub = pub,type = "continuous", j = 1:ncol(LC)) |>
add_variable(z2[j],lb = 0,ub = nub,type = "continuous", j = 1:ncol(LC)) |>
add_constraint(sum_over(x[i,j],i = 1:nrow(LC),is_connected(i, j))+z1[j]-z2[j] == d[j],j = 1:ncol(LC)) |>
add_constraint(sum_expr(x[i,j],j = 1:ncol(LC),is_connected(i,j))/rakemultiplier[i] == y[i], i = railroutes) |>
add_constraint(sum_expr(x[i,j],j = 1:ncol(LC),is_connected(i,j)) == y[i]*rakemultiplier[i], i = railroutes) |>
add_constraint(sum_over(x[i,j], i = p1OPC, j = 1:ncol(LC),is_connected(i,j)) <= 120000) |>
add_constraint(sum_over(x[i,j], i = p2OPC, j = 1:ncol(LC),is_connected(i,j)) <= 120000) |>
add_constraint(sum_over(x[i,j], i = p3OPC, j = 1:ncol(LC),is_connected(i,j)) <= 120000) |>
add_constraint(sum_over(x[i,j], i = p4OPC, j = 1:ncol(LC),is_connected(i,j)) <= 120000) |>
# add_constraint(sum_over(x[i,j], i = p5OPC, j = 1:ncol(LC),is_connected(i,j)) <= 0) |>
# add_constraint(sum_over(x[i,j], i = p6OPC, j = 1:ncol(LC),is_connected(i,j)) <= 0) |>
add_constraint(sum_over(x[i,j], i = p7OPC, j = 1:ncol(LC),is_connected(i,j)) <= 175000) |>
add_constraint(sum_over(x[i,j], i = p8OPC, j = 1:ncol(LC),is_connected(i,j)) <= 175000) |>
add_constraint(sum_over(x[i,j], i = p9OPC, j = 1:ncol(LC),is_connected(i,j)) <= 175000) |>
add_constraint(sum_over(x[i,j], i = p10OPC, j = 1:ncol(LC),is_connected(i,j)) <= 175000) |>
add_constraint(sum_over(x[i,j], i = p1PPC, j = 1:ncol(LC),is_connected(i,j)) <= 75000) |>
add_constraint(sum_over(x[i,j], i = p2PPC, j = 1:ncol(LC),is_connected(i,j)) <= 75000) |>
add_constraint(sum_over(x[i,j], i = p3PPC, j = 1:ncol(LC),is_connected(i,j)) <= 75000) |>
add_constraint(sum_over(x[i,j], i = p4PPC, j = 1:ncol(LC),is_connected(i,j)) <= 75000) |>
add_constraint(sum_over(x[i,j], i = p5PPC, j = 1:ncol(LC),is_connected(i,j)) <= 75000) |>
add_constraint(sum_over(x[i,j], i = p6PPC, j = 1:ncol(LC),is_connected(i,j)) <= 150000) |>
add_constraint(sum_over(x[i,j], i = p7PPC, j = 1:ncol(LC),is_connected(i,j)) <= 150000) |>
add_constraint(sum_over(x[i,j], i = p8PPC, j = 1:ncol(LC),is_connected(i,j)) <= 150000) |>
add_constraint(sum_over(x[i,j], i = p9PPC, j = 1:ncol(LC),is_connected(i,j)) <= 150000) |>
add_constraint(sum_over(x[i,j], i = p10PPC, j = 1:ncol(LC),is_connected(i,j)) <= 150000) |>
set_objective(sum_over(x[i,j]*LC[i,j]+z1[j]+z2[j],i = 1:nrow(LC),j = 1:ncol(LC),is_connected(i, j)) ,"min")
model
print(Sys.time() - a)
a = Sys.time()
solution1 = model %>% solve_model(with_ROI(solver = "glpk",verbose=T))
print(Sys.time() - a)
rm(a)
This works perfectly fine, but one thing I am unhappy about is the fact that this code has to be manually changed when the original data is subsetted. Also when there are some plants that don't produce a certain product, I have to comment on the code - i.e the corresponding rows are numerical(0) (check p5opc,p6opc)
Is there a clever way to combine these expressions into something scalable to x plants taking care of the commenting when the corresponding variable is numerical(0) thus making the code more readable?