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

This paper studies the problem of tangible assets acquisition within the company by proposing a new hybrid model that uses linear programming and fuzzy numbers. Regarding linear programming, two methods were implemented in the model, namely: the graphical method and the primal simplex algorithm. Thi...

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Vydáno v:Entropy (Basel, Switzerland) Ročník 22; číslo 1; s. 121
Hlavní autoři: Boloș, Marcel-Ioan, Bradea, Ioana-Alexandra, Delcea, Camelia
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2020
MDPI
Témata:
Algorithms
Artificial intelligence
Capital assets
Decision making
fuzzy coefficients and decision variables
Fuzzy sets
fuzzy triangular numbers
graphical method
Graphical methods
investment decisions
Linear programming
Mathematical functions
Mathematical models
Optimization
primal simplex algorithm
Simplex method
situation analysis
tangible assets
ISSN:1099-4300, 1099-4300
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Shrnutí:This paper studies the problem of tangible assets acquisition within the company by proposing a new hybrid model that uses linear programming and fuzzy numbers. Regarding linear programming, two methods were implemented in the model, namely: the graphical method and the primal simplex algorithm. This hybrid model is proposed for solving investment decision problems, based on decision variables, objective function coefficients, and a matrix of constraints, all of them presented in the form of triangular fuzzy numbers. Solving the primal simplex algorithm using fuzzy numbers and coefficients, allowed the results of the linear programming problem to also be in the form of fuzzy variables. The fuzzy variables compared to the crisp variables allow the determination of optimal intervals for which the objective function has values depending on the fuzzy variables. The major advantage of this model is that the results are presented as value ranges that intervene in the decision-making process. Thus, the company’s decision makers can select any of the result values as they satisfy two basic requirements namely: minimizing/maximizing the objective function and satisfying the basic requirements regarding the constraints resulting from the company’s activity. The paper is accompanied by a practical example.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e22010121