Fuzzy Solution of Z-Number-Based Multi-Objective Linear Programming Models with Different Membership Functions
Year: 2025
Type: article
Abstract: In real-world decision-making, uncertainty plays a crucial role, especially when dealing with complex, multi-objective optimization problems. Traditional linear programming (LP) models often have the assumption that the data is precise and deterministic. However, this assumption is often not realistic for many applications, as imprecision will always be present. This paper presents a fuzzy solution to Z-Z-number-based multi-objective linear programming (ZMOLP) models. Z-numbers can combine fuzzy logic and Z-numbers to manage uncertainty in (multi-objective) decision dilemmas. A Z-number consists of two parts: a fuzzy number for the uncertainty in the data and a reliability score that indicates the degree of confidence in the data. The two aspects of Z-numbers make them effective for modeling imprecise data in multi-objective positioning. The effectiveness and overall computational efficiency of different fuzzy membership functions (triangular, trapezoidal, Gaussian, etc.) were also explored to understand their impact on optimal solutions. Z-numbers, through fuzzy numbers, provide a flexible and adaptable decision-making solution far superior to traditional methods for managing imprecision that leads to a better representation of uncertainty related to objectives and constraints. To demonstrate the viability of Z-number-based approaches, practical applications were employed to illustrate their usage in healthcare decision-making and engineering optimization.
DOI: 10.14419/83myk890
Authors: Pandit Shinde, Dr. Asit Sen, D. S. Shelar
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414966246
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