pylexflp 0.1.3

A Python Package for Fuzzy Linear Programming with the Lexicographic Method

PyLexFLP

PyLexFLP is a Python package for solving Fuzzy Linear Programming problems using the Lexicographic Method. This tool is designed to help researchers and practitioners in Operations Research and Fuzzy Optimization to model and solve fuzzy optimization problems effectively.

Features

• Solves fuzzy linear programming models in which the coefficients are arbitrary triangular fuzzy numbers and the decision variables are non-negative triangular fuzzy numbers.
• Utilizes Lexicographic Ranking criteria for ordering triangular fuzzy numbers.
• Built on top of the PuLP optimization framework.

Installation

You can install PyLexFLP from GitHub:

pip install git+https://github.com/bpcanedo/pylexflp.git

Or, from PyPI:

pip install pylexflp

Requirements

• Python >= 3.7
• PuLP >= 2.6
• SciPy >= 1.21

Usage

Here's a simple example of how to use PyLexFLP:

from pylexflp import FLP, TFN_Var, TFN, flpMaximize, getSolver

# Lexicographic ranking method
def f1(x): return (x.al+2*x.am+x.au)/4
def f2(x): return x.am
def f3(x): return x.au-x.al
criteria = [f1, f2, f3]

# The problem
p = FLP(criteria=criteria, sense=flpMaximize)

# Variable definition
x1, x2, I1, I2, I3 = TFN_Var("x1"), TFN_Var("x2"), TFN_Var("I1"), TFN_Var("I2"), TFN_Var("I3")

# Let PyLexFLP know the variables
p += x1
p += x2
p += I1
p += I2
p += I3

# Insert the constraints
p += I1 + TFN(0.15,0.20,0.25)*I1 == x1+I2
p += I2 + TFN(0.10,0.15,0.20)*I2 == x2+I3
p += I1 == TFN(950, 1000, 1050)
p += I3 >= TFN(622, 624, 627)

# The last expression or variable entered becomes the objective function
p += x1 + x2

# Select the linear programming solver
# msg = True if you want verbosity
solver = getSolver("COIN_CMD", msg=False)

# Solve the problem
# status = [1, 1, 1] means the three criteria were successfully optimized
status = p.solve(solver=solver)

# Fuzzy proposition example
## Membership function
def High(x): return max(0, min(1, (x-500)/500))

# Print the solution
solution = f"""
Status: {status}
Solution:
    x1 = {x1}
    x2 = {x2}
    obj = {x1+x2}
    I1 = {I1}
    I2 = {I2}
    I3 = {I3}
    # Fuzzy proposition
    x2 is High = {x2.IS(High)}
"""
print(solution)

Documentation

Detailed documentation and examples can be found on the GitHub repository.

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests to the GitHub repository.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Authors

Boris Pérez-Cañedo

Email: [e-mail]

GitHub: bpcanedo

Eduardo René Concepción-Morales

Email: [e-mail]

José Luis Verdegay

Email: [e-mail]