Fuzzy Optimization and Decision Making

Topic Information

Dear Colleagues,

We invite high-quality submissions to the topic Fuzzy Optimization and Decision Making, which focuses on addressing the challenges of uncertainty in real-world applications. The scope covers fuzzy modeling, optimization algorithms, and decision-making frameworks, with special attention given to advances arising from the integration of machine learning, data science, and operations research. 

We particularly welcome contributions that demonstrate originality and rigor—either through theoretical development or empirical validation—in applying fuzzy technologies to tackle complex problems in economics, engineering, management, and society. 

Prof. Dr. Hengjie Zhang
Dr. Quanbo Zha
Dr. Jing Xiao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Axioms axioms	1.6	-	2012	21.6 Days	CHF 2400	Submit
Information information	2.9	6.5	2010	18.6 Days	CHF 1800	Submit
Mathematics mathematics	2.2	4.6	2013	18.4 Days	CHF 2600	Submit
Symmetry symmetry	2.2	5.3	2009	17.1 Days	CHF 2400	Submit
AppliedMath appliedmath	0.7	1.1	2021	23.5 Days	CHF 1200	Submit

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20 pages, 1882 KB  

Open AccessArticle

Solving the Interdependence of Weighted Shortest Job First Variables by Applying Fuzzy Cognitive Mapping

by Bryan Nagib Zambrano Manzur, Fabián Andrés Espinoza Bazán, Yamilis Fernandez and Carlos Cruz Corona

Information 2025, 16(11), 944; https://doi.org/10.3390/info16110944 - 30 Oct 2025

Viewed by 478

Abstract

In agile, adaptive, and hybrid project management, the Weighted Shortest Job First (WSJF) technique is increasingly being used to prioritize the most relevant business opportunities for organizations. However, this decision-making process often involves the evaluation of multiple interconnected factors whose interactions can influence [...] Read more.

In agile, adaptive, and hybrid project management, the Weighted Shortest Job First (WSJF) technique is increasingly being used to prioritize the most relevant business opportunities for organizations. However, this decision-making process often involves the evaluation of multiple interconnected factors whose interactions can influence outcomes in unforeseen ways. Traditional decision-making models tend to assume independence between variables for the sake of simplicity and tractability. In real-world contexts, however, variables rarely operate in isolation; their interdependence introduces complexities that challenge the validity, robustness, and practical applicability of conventional decision-making tools. The objective of this research is to address the problem of interdependence among WSJF variables. To achieve this, Fuzzy Cognitive Mapping (FCM) was applied to evaluate the impact and influence of interdependencies during the decision-making process. The findings demonstrate that incorporating FCM into WSJF yields a 76% correlation in prioritization order with the best outcomes, compared to linear WSJF, while revealing a 24% variation that highlights areas for further study. This evidence indicates that accounting for interdependence leads to more efficient and reliable decision-making than traditional approaches. Full article

(This article belongs to the Topic Fuzzy Optimization and Decision Making)

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