CODEWITHSUNDEEP
pythonoptimizationpulp
user6016731
user6016731

Reputation: 382

PULP optimization solution undefined

I was trying to optimize the following problem using python pulp

import pulp
# Instantiate our problem class
model = pulp.LpProblem("Cost minimising problem", pulp.LpMinimize)

W = pulp.LpVariable('W', cat='Integer')
X = pulp.LpVariable('X', cat='Integer')
Y = pulp.LpVariable('Y', cat='Integer')
Z = pulp.LpVariable('Z', cat='Integer')

# Objective function
model += 1.33 * W + 1.76 * X + 1.46 * Y + 0.79 * Z,"Cost"

# Constraints
model += W + X + Y + Z == 1

 model += W >= 0.1
 model += W <= 0.75

 model += X >= 0.1
 model += X <= 0.85

 model += Y >= 0.1
 model += Y <= 0.65

 model += Z >= 0.1
 model += Z <= 0.40


# Solve our problem
model.solve()
pulp.LpStatus[model.status]

'Undefined'

The solution turns out to be undefined. Am I making a mistake in problem formulation or missing out something ?

Upvotes: 1

Views: 604

Answers (1)

kabdulla
kabdulla

Reputation: 5429

When I implement the same code I get the result 'Infeasible'.

This makes sense as your variables W, X, Y, Z all have to be integers, but you then bound them to be more than 0.1, and less than another number which is less than 1.

There are no integers inbetween 0.1 and 0.XX, so there is no feasible solution.

Upvotes: 1

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