如何解决如何在R的OMPR软件包中定义3D变量？
我正在尝试使用R的OMPR软件包解决MILP供应网络优化问题。我需要在模型中定义3D二进制变量（x [i，j，q]），以通过每个SKU（q）将客户（i）映射到DC（j）。有人知道我该怎么做吗？
使用我当前的代码，我得到了
Error - in as.data.frame.default(x[[i]],optional = TRUE) :
cannot coerce class ‘structure("LinearVariableCollection",package = "ompr")’ to a data.frame
我不再遇到此错误，但是模型没有给出正确的输出（已针对excel的开放式求解器进行了验证）。约束4似乎存在一些问题。看来VCustDemand和变量x [i，j，q]之间的乘法运算未按正确顺序进行。
model <- MILPModel() %>%
add_variable(y[j],j = 1:n,type = "binary")%>% #1: if warehouse j is opened
add_variable(x[i,j,q],i = 1:m,j =1:n,q=1:r,type = "binary")%>% #2: if i gets assigned to warehouse j for Product q
add_variable(z[p,j],p = 1:o,type = "binary") %>% # 3 for selection of DC of a specific size
add_variable(aa[l,l = 1:k,type = "integer",lb = 0,ub = 500000) %>% # 4 for plant getting mapped to a warehouse for product q
set_objective(
sum_expr(colwise(Vsec_log[i+(j-1)*m*r+(q-1)*m]) * x[i,q] * colwise(VCustDemand[i+(q-1)*32]),q=1:r) + #Secondary shipments total
sum_expr(colwise(Vfix_fac[p+o*(j-1)]) * z[p,j = 1:n ) + #DC Fixed costs
sum_expr(colwise(VCustDemand[i+(q-1)*32]) * x[i,q] * colwise(Vvar_fac_storage[q+(j-1)*r]),q=1:r ) + #Varilable storage costs
sum_expr(colwise(VCustDemand[i+(q-1)*32]) * x[i,q] * colwise(Vvar_fac_handling[q+(j-1)*r]),q=1:r ) + #DC Variable handling costs
sum_expr(colwise(Vprimary_log[l+(j-1)*k*r+(q-1)*k]) * aa[l,q=1:r),# + #Primary logistics costs
# sum_expr(aa[l,q]*colwise(Vplant_var[l+(q-1)*k]),q = 1:r),#Plant variable costs
sense = "min") %>%
add_constraint(sum_expr(y[j],j = 1:n) == 6)%>% #1. No. of open DCs
add_constraint(sum_expr(x[i,j = 1:n) == 1,q=1:r) %>% #2. Each Customer is mapped to one DCs for 1 SKU
add_constraint((sum_expr(x[i,q=1:r)/999) <= y[j],j = 1:n) %>% #3. Grouping constraint connecting #1 & #2
add_constraint(aa[l,q] >= 0,q = 1:r) %>% #4. Positive outflow from Plant s
add_constraint(sum_expr(aa[l,l = 1:k) == sum_expr(colwise(VCustDemand[i+(q-1)*32]) * x[i,i = 1:m),q = 1:r)%>% #4 plant outflow should be equal to DC inflow for each SKU
add_constraint(sum_expr(aa[l,j = 1:n) <= Vtbl_plant_capacity[l+(q-1)*k],q = 1:r) %>% ##5 Constraint on plant capacity for every SKU
add_constraint(sum_expr(aa[l,q = 1:r) <= Vtbl_Total_plant_capacity[l],l = 1:k) %>%##6 Constraint on overall plant capacity across SKUs
add_constraint(sum_expr(z[p,p = 1:o) == y[j],j = 1:n) %>% #7. Select one facility per capacity size
add_constraint(sum_expr(colwise(Vfix_fac_cap[p]) * z[p,p = 1:o ) >=
sum_expr(colwise(VCustDemand[1:(m*r)]) * x[i,q =1:r),j = 1:n) #8. Total DC outflow should be <= Total DC capacity
'''
If I replace the constraint by
add_constraint(sum_expr(aa[l,l = 1:2) == sum_expr(colwise(VCustDemand[i+(1-1)*32]) * x[i,q = 1) %>%
add_constraint(sum_expr(aa[l,l = 1:2) == sum_expr(colwise(VCustDemand[i+(2-1)*32]) * x[i,q = 2) %>%
add_constraint(sum_expr(aa[l,l = 1:2) == sum_expr(colwise(VCustDemand[i+(3-1)*32]) * x[i,q = 3) %>%
'''
The code seems to work
VCustDemand is a 2D data containing customer demand in the following format -
ProductLine CustomerCity Demand
SKU1 Agra 1755
SKU1 Ahemdabad 7279
SKU1 Delhi 1830
SKU2 Agra 1408
SKU2 Ahemdabad 9111
SKU2 Delhi 4970
SKU3 Agra 1469
SKU3 Ahemdabad 414
SKU3 Delhi 229
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