Linear Programming and Fuzzy Optimization to Substantiate Investment Decisions in Tangible Assets

Area of Science:

• Operations Research
• Decision Science

Background:

• Tangible asset acquisition is crucial for company growth.
• Traditional investment models often lack flexibility in handling uncertainty.

Purpose of the Study:

• To propose a novel hybrid model for tangible asset acquisition.
• To enhance investment decision-making by incorporating fuzzy numbers into linear programming.

Main Methods:

• Implementation of a hybrid model combining linear programming (graphical method, primal simplex algorithm) with fuzzy numbers.
• Representation of decision variables, objective function coefficients, and constraints as triangular fuzzy numbers.

Main Results:

• The hybrid model yields results in the form of fuzzy variables, providing optimal intervals for the objective function.
• Fuzzy variables allow for a range of optimal solutions, unlike crisp variables.

Conclusions:

• The proposed model offers a significant advantage by presenting results as value ranges, aiding decision-makers.
• Decision-makers can choose optimal values within these ranges, balancing objective function optimization and constraint satisfaction.