Goal Programming Models with Linear and Exponential Fuzzy Preference Relations

• Main Authors: Mohammad Faisal Khan, Md. Gulzarul Hasan, Abdul Quddoos, Armin Fügenschuh, Syed Suhaib Hasan
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Symmetry
• Subjects: fuzzy programming; goal programming; preference relation; non-linear membership function; exponential membership function
• Online Access: https://www.mdpi.com/2073-8994/12/6/934

Goal programming (GP) is a powerful method to solve multi-objective programming problems. In GP the preferential weights are incorporated in different ways into the achievement function. The problem becomes more complicated if the preferences are imprecise in nature, for example `Goal A is slightly or moderately or significantly important than Goal B’. Considering such type of problems, this paper proposes standard goal programming models for multi-objective decision-making, where fuzzy linguistic preference relations are incorporated to model the relative importance of the goals. In the existing literature, only methods with linear preference relations are available. As per our knowledge, nonlinearity was not considered previously in preference relations. We formulated fuzzy preference relations as exponential membership functions. The grades or achievement function is described as an exponential membership function and is used for grading levels of preference toward uncertainty. A nonlinear membership function may lead to a better representation of the achievement level than a linear one. Our proposed models can be a useful tool for different types of real life applications, where exponential nonlinearity in goal preferences exists. Finally, a numerical example is presented and analyzed through multiple cases to validate and compare the proposed models. A distance measure function is also developed and used to compare proposed models. It is found that, for the numerical example, models with exponential membership functions perform better than models with linear membership functions. The proposed models will help decision makers analyze and plan real life problems more realistically.