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Я пытаюсь решить проблему оптимизации сети поставок MILP, используя OMPR-пакет R. Мне нужно определить 3D-двоичную переменную (x[i, j, q]) в модели, чтобы сопоставить клиентов (i) с DC (j) по каждому артикулу (q). Кто-нибудь знает, как я могу это сделать?

С моим текущим кодом я получаю

 Error - in as.data.frame.default(x[[i]], optional = TRUE) : 
cannot coerce class ‘structure("LinearVariableCollection", package = "ompr")’ to a data.frame 
  

Я больше не получаю эту ошибку, но модель не выдает правильный результат (проверено с помощью открытого решателя Excel). Кажется, есть какая-то проблема с ограничением # 4. Похоже, что умножение между VCustDemand и переменной x [i, j, q] происходит не в правильном порядке.

 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, j = 1:n,  type = "binary") %>% # 3 for selection of DC of a specific size
    add_variable(aa[l,j,q], l = 1:k, j =1:n, q=1:r, 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, j, q] * colwise(VCustDemand[i (q-1)*32]) , i = 1:m ,j = 1:n, q=1:r)   #Secondary shipments total
       sum_expr(colwise(Vfix_fac[p o*(j-1)]) * z[p, j] , p = 1:o, j = 1:n )   #DC Fixed costs
        sum_expr(colwise(VCustDemand[i (q-1)*32]) * x[i,j,q] * colwise(Vvar_fac_storage[q (j-1)*r]), i = 1:m , j = 1:n, q=1:r )   #Varilable storage costs
        sum_expr(colwise(VCustDemand[i (q-1)*32]) * x[i,j,q] * colwise(Vvar_fac_handling[q (j-1)*r]), i = 1:m , j = 1:n, q=1:r )    #DC Variable handling costs
        sum_expr(colwise(Vprimary_log[l (j-1)*k*r (q-1)*k]) * aa[l,j,q] , l = 1:k, j = 1:n, q=1:r), #   #Primary logistics costs
#        sum_expr(aa[l,j,q]*colwise(Vplant_var[l (q-1)*k]) , l = 1:k, j = 1:n, 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, q], j = 1:n) == 1, i = 1:m, q=1:r) %>% #2. Each Customer is mapped to one DCs for 1 SKU
    add_constraint((sum_expr(x[i, j, q], i = 1:m, q=1:r)/999) <= y[j], j = 1:n) %>% #3. Grouping constraint connecting #1 amp; #2
    add_constraint(aa[l,j,q] >= 0, l = 1:k, j = 1:n, q = 1:r) %>% #4. Positive outflow from Plant s
    add_constraint(sum_expr(aa[l,j,q], l = 1:k) == sum_expr(colwise(VCustDemand[i (q-1)*32]) * x[i,j,q], i = 1:m), j = 1:n, q = 1:r)%>% #4 plant outflow should be equal to DC inflow for each SKU
    add_constraint(sum_expr(aa[l,j,q], j = 1:n) <= Vtbl_plant_capacity[l (q-1)*k], l = 1:k, q = 1:r) %>%  ##5 Constraint on plant capacity for every SKU
    add_constraint(sum_expr(aa[l,j,q], j = 1:n, 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,j], 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,j], p = 1:o ) >=
                     sum_expr(colwise(VCustDemand[1:(m*r)]) * x[i,j,q], i = 1:m, 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,j,q], l = 1:2) == sum_expr(colwise(VCustDemand[i (1-1)*32]) * x[i,j,q], i = 1:m), j = 1:n, q = 1)   %>%
    add_constraint(sum_expr(aa[l,j,q], l = 1:2) == sum_expr(colwise(VCustDemand[i (2-1)*32]) * x[i,j,q], i = 1:m), j = 1:n, q = 2)   %>%
    add_constraint(sum_expr(aa[l,j,q], l = 1:2) == sum_expr(colwise(VCustDemand[i (3-1)*32]) * x[i,j,q], i = 1:m), j = 1:n, 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