Multi-Choice Goal Programming with Flexible Fuzzy Goals and Constraints

Ramzannia-Keshteli, G.A.; Nasseri, Seyed Hadi; Ganji, R.M.; Bavandi, Salim

doi:10.1109/cfis.2019.8692163

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

2022

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pathinathan and Ponnivalavan [37] described the PFN, including addition, subtraction, and arithmetic operations. Chang [38] discussed the mixed binary problem with GP; Ramzannia et al [39] presented flexible fuzzy goals (FFGs) and constraints for multichoice goal programming. Haq et al [40] described how to convert a nonlinear programming problem into a binary goal programming (BGP) problem and solve it using the FFG programming approach.…”

Section: Introductionmentioning

confidence: 99%

Flexible fractional transportation problem with multiple goals: a pentagonal fuzzy concept

2022

View full text Add to dashboard Cite

We present the framework of the multiobjective fractional transportation problem in the form of pentagonal fuzzy supply and demand. The ideal transportation model is set up to match the decision makers' preferences in competing for the criteria, and transportation costs, delivery time, degradation, environmental and social concerns are the objectives. We employed flexible fuzzy goal programming to handle the Model's complexity to improve the reasonable compromise. The real-world problem of wind turbine blades is used to validate the superiority and effectiveness of the technique.

show abstract