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

Area of Science:

• Operations Research
• Management Science
• Decision Analysis

Background:

• Tangible asset acquisition is crucial for company growth.
• Traditional investment decision models often lack flexibility in handling uncertainty.
• Existing methods may not fully capture the range of optimal outcomes.

Purpose of the Study:

• To propose a novel hybrid model for tangible asset acquisition.
• To integrate linear programming with fuzzy numbers for investment decisions.
• To provide decision-makers with optimal value ranges for asset acquisition.

Main Methods:

• A hybrid model combining linear programming (graphical method, primal simplex algorithm) and fuzzy numbers.
• Representing decision variables, objective function coefficients, and constraints as triangular fuzzy numbers.
• Solving the primal simplex algorithm with fuzzy coefficients to obtain fuzzy variables.

Main Results:

• The hybrid model generates results in the form of fuzzy variables, representing optimal intervals.
• Fuzzy variables allow for the determination of objective function value ranges.
• The model provides decision-makers with flexible optimal solutions within defined constraints.

Conclusions:

• The proposed hybrid model enhances tangible asset acquisition decisions by incorporating uncertainty.
• Fuzzy variables offer a more comprehensive understanding of optimal investment outcomes compared to crisp variables.
• The model supports informed decision-making by presenting results as value ranges, balancing optimization and constraint satisfaction.