Goal Programming Models with Linear and Exponential Fuzzy Preference Relations

Abstract

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.

AdaCB: An Adaptive Gradient Method with Convergence Range Bound of Learning Rate

“…The linear and exponential properties are the common and widely used mechanisms to solve some mathematical problems in practical [36][37][38][39][40]. To tackle the extreme learning rates of Adam during whole training and achieve a smooth transformation from Adam to SGD, the upper bound is designed to be an asymptotical function from the maximum value of convergence range to the global fixed value.…”

From Goal Programming for Continuous Multi-Criteria Optimization to the Target Decision Rule for Mixed Uncertain Problems

“…GP was first applied by Charnes et al [ 1 ]. In subsequent years, this procedure has been extended to fuzzy multi-criteria problems [ 2 , 3 ] or combined with other methods for various applications [ 4 , 5 ].…”

Fuzzy Goal Programming with an Imprecise Intuitionistic Fuzzy Preference Relations

“…In the last few decades, the imprecise preference relation in FGP is applied to numerous real-world applications and theoretically extended by several other authors, such as Petrovic and Aköz [36], Torabi and Moghaddam [37], Khalili-Damghani and Sadi-Nezhad [38], Cheng [33], Díaz-Madroñero et al [39], Sheikhalishahi and Torabi [40], Khan et al [41], Bilbao-Terol et al [42], Bilbao-Terol et al [43] and Arenas-Parra et al [44]. Some well-known research works that has been carried out in FGP has been summarized in Table 1.…”