'LpAffineExpression' object has no attribute 'solve' in pulp Lp problem- Optimization

Question

import pulp as p
import numpy as np
a1=np.array([1000,2000,3000,7000,8000,13000,223000,32000,35000,369000,38000,3885000])

x=p.LpVariable('x', lowBound=5000, cat='Continuous')
y=p.LpVariable('y', lowBound=8000,cat='Continuous')

Lp_prob=(((y-x)*1.3+x)*0.014428)+((a1-y)*1.5*0.014428)
Lp_prob.solve()

I try to do linear programming in pulp. But I have 'LpAffineExpression' object has no attribute 'solve' error.

How can I fix it? Thanks.

Answer 1

I would suggest to first study this example: https://www.coin-or.org/PuLP/CaseStudies/a_blending_problem.html. It has all the ingredients to cover your example.

So a working model can look like:

import pulp as p
import numpy as np
 
a1=np.array([1000,2000,3000,7000,8000,13000,223000,32000,35000,369000,38000,3885000])

x=p.LpVariable('x', lowBound=5000, cat='Continuous')
y=p.LpVariable('y', lowBound=8000,cat='Continuous')

Lp_prob = p.LpProblem("This_Example_Works",p.LpMaximize)
Lp_prob += (((y-x)*1.3+x)*0.014428)+((a1-y)*1.5*0.014428)
Lp_prob.solve()
print("Status:", p.LpStatus[Lp_prob.status])

Note that PuLP interprets this as:

MAXIMIZE
-0.0043284000000000005*x + -0.24094759999999996*y + 99899.472
VARIABLES
5000 <= x Continuous
8000 <= y Continuous

The very strange construct a1-y is interpreted here as sum(a1-y)=sum(a1)-n*y where n=a1.size. I would suggest not to use NumPy arrays this way in a PuLP model.