Agfdhyk

I have a list of products (i), each with given weight and volume. The optimization goes in two steps, of which I have been unable to solve the second step.

First optimization: minimize number of bins used (solved)

• 
  

  Minimize number of bins used to pack list of products. I have fixed bins with with maximum volume and weight constraints. Python code:

  
  
  

  prob = pp.LpProblem("algorithm",pp.LpMinimize) #pp=pulp solver
  
  
  
  Y_max=10  #bins will not exceed this number
  
  #Y_min = minimum number of bins (calculated)
  
  # j = index of jth bin
  
  # i = index of ith product
  
  
  
  w=weights #list of weights w_i for product i
  
  v=volumes #list of volumes v_i for product i
  
  W=W_max #maximum weight of a bin
  
  V=V_max #maximum volume of a bin
  
  #len(order) = number of products
  
  
  
  x=pp.LpVariable.dicts("x_i,j", ((i, j) for i in range(len(order)) for j in range(Y_max)),0,1,pp.LpBinary) #variable indicating if product i is placed in bin j
  
  y=pp.LpVariable.dicts("y_j", ((j,0) for j in range(Y_max)),0,1,pp.LpBinary) #variable indicating if bin j is used or unused
  
  Y=pp.LpVariable('Y')
  
  
  
  prob +=Y , 'objective function'    
  
  prob += pp.lpSum([y[j,0] for j in range(Y_max)]) == Y, ""
  
  
  
  for i in range(len(order)):
  
      prob += pp.lpSum([x[i,j] for j in range(Y_max)]) == 1,''  #product i can only be placed in 1 bin
  
  
  
  for j in range(Y_max):
  
      prob += pp.lpSum([x[i,j]*w[i] for i in range(len(order))]) <= W*y[j,0],""  #weight constraint
  
  
  
  for j in range(Y_max):
  
      prob += pp.lpSum([x[i,j]*v[i] for i in range(len(order))]) <= V*y[j,0],""  #volume constraint
  
  
  
  for j in range(Y_max):
  
      for i in range(len(order)):
  
          prob += x[i,j] <= y[j,0],"" #products i may not be placed in bin j if bin j is unnused. 
  
  
  
  prob += pp.lpSum([y[j,0] for i in range(len(order))]) >=Y_min,""
  
  pp.LpSolverDefault.msg = 1
  
  prob.solve()`
  
  

  
  

Second optimization: minimize the number of zones that the bin travels to (how to solve in linear optimization?)

• Each product comes from 1 out of two zones (Zone 1 or Zone 2). We have a list of these zones z_i (e.g. Zone 1 <==> 1, Zone 2 <==> 0).




• Under the constraint that the number of bins does not exceed the minimum number of bins (determined in first optimization), can I split the products over the bins such that the number of zones that each bin travels to is minimized?




• Volume and weight constraints of first optimization still apply.

Ideally a bin would never travel to two zones, but in some cases it is forced to do so. (e.g. first optimization leads to 1 bin containing products of zone 1 and zone 2).

Below is one way of doing it - not necessarily the neatest or most efficient. Similar to that suggested by @juvian

If you slowly reduce the volume per bin, you'll find whilst it large you can fit in a small number of bins, with each bin only visiting a single zone. As bins get smaller you're forced to have bins travel to more than one zone, and as they get smaller again you're forced to use more bins.

Notice in the objective function:
prob += Y + pp.lpSum([two_zones[j] for j in range(Y_max)])/(Y_max+1)

We divide our secondary objective (No. of bins which have products from both zones) by the maximum No. of bins + 1. This way we always prioritise the primary objective of number of bins - even if every bin has items from different zones, the second sum can be at most Y_max, so if we divide it by Y_max + 1 we get a value less than 1.0, so would prefer to reduct the number of used bins by 1. This is a common technique when you want to prioritize objectives.

import numpy as np 

import pulp as pp



prob = pp.LpProblem("algorithm",pp.LpMinimize) #pp=pulp solver



Y_max = 5 #bins will not exceed this number

#Y_min = minimum number of bins (calculated)

# j = index of jth bin

# i = index of ith product



# Some dummy data

n_prod = 10



np.random.seed(0)

w = np.random.uniform(2.5, 10, n_prod)  # product weights

v = np.random.uniform(0.1, 1, n_prod) # product volumes

W = 25 #maximum weight of a bin

V = 1.5  #maximum volume of a bin

z = np.random.randint(0, 2, n_prod) # product zones



x=pp.LpVariable.dicts("x_i,j", ((i, j) for i in range(n_prod) for j in range(Y_max)), cat='Binary') #variable indicating if product i is placed in bin j

y=pp.LpVariable.dicts("y_j", range(Y_max), cat='Binary') #variable indicating if bin j is used or unused

Y=pp.LpVariable('Y') # No. bins used

two_zones = pp.LpVariable.dicts("two_zones,j", range(Y_max), cat='Binary')

has_zone_0 = pp.LpVariable.dicts("has_zone_0,j", range(Y_max), cat='Binary')

has_zone_1 = pp.LpVariable.dicts("has_zone_1,j", range(Y_max), cat='Binary')



# Primary objective: minm No. bins, Secondary minimize bins that visit two zones

prob += Y + pp.lpSum([two_zones[j] for j in range(Y_max)])/(Y_max+1), 'objective function'    



prob += pp.lpSum([y[j] for j in range(Y_max)]) == Y, 'set Y to No. bins'



for i in range(n_prod):

    prob += pp.lpSum([x[i,j] for j in range(Y_max)]) == 1,'each product in 1 bin %s' % i



for j in range(Y_max):

    prob += pp.lpSum([x[i,j]*w[i] for i in range(n_prod)]) <= W*y[j], 'weight constraint %s' % j



    prob += pp.lpSum([x[i,j]*v[i] for i in range(n_prod)]) <= V*y[j], 'volume constraint %s' % j



    for i in range(n_prod):

        prob += x[i,j] <= y[j], 'products only placed in used bin  %s_%s' % (j, i)



        prob += has_zone_0[j] >= x[i,j]*(z[i] == 0), 'set has_zone_0 flag %s_%s' % (j, i)

        prob += has_zone_1[j] >= x[i,j]*(z[i] == 1), 'set has_zone_1 flag %s_%s' % (j, i)



    prob += two_zones[j] >= has_zone_0[j] + has_zone_1[j] - 1, 'set two_zones flag %s' % j



prob.solve()



has_zone_0_soln = np.array([has_zone_0[j].varValue for j in range(Y_max)])

has_zone_1_soln = np.array([has_zone_1[j].varValue for j in range(Y_max)])

two_zones_soln = np.array([two_zones[j].varValue for j in range(Y_max)])

y_soln = np.array([y[j].varValue for j in range(Y_max)])



# Print some output:

print("Status:" + str(pp.LpStatus[prob.status]))

print('z: ' + str(z))

print('Y: ' + str(Y.varValue))

print('y_soln: ' + str(y_soln))

print('Objective: ' + str(pp.value(prob.objective)))

print('has_zone_0_soln: ' + str(has_zone_0_soln))

print('has_zone_1_soln: ' + str(has_zone_1_soln))

print('two_zones_soln: ' + str(two_zones_soln))