Decision Making and Analysis

Novel Russell's approximation method for transportation problem under hesitant bifuzzy environment

2025-11-05T11:23:36+08:00

Transportation problem is one of the significant linear programming problems that emerges in several conditions and gains attention of many researchers. Reducing a commodity's transportation expense to meet demand at destination is the goal of the transportation problem. However, uncertainty plays a vital role in real life, making it challenging for decision-makers to provide precise values for the coefficients related to the transportation problem. Hesitant bifuzzy sets are a notable advancement of fuzzy set theory, as they allow decision-makers to deal with every hesitancy without any restriction. This study aims to introduce a formulation of transportation problem under a hesitant bifuzzy environment. In this study, we have introduced a novel Russel approximation method (RAM) for the hesitant bifuzzy transportation problem, where every parameter is represented by hesitant bifuzzy elements. To show the applicability of the proposed method, a real-life illustration has been taken in this article. Additionally, to demonstrate the superiority of the proposed method, a comparative study has also been conducted with other existing methods, which results in the minimum transportation cost.

Bilevel optimization for the regulation of shared resources under uncertainty

2026-08-24T10:43:25+08:00

We study normal-form noncooperative games corresponding to Common-Pool Resource (CPR) situations. A social planner aims to regulate the use of the resource and acts as a leader in a two-stage game. The followers are non-cooperative players who choose a Nash equilibrium representing optimal extraction levels. Due to uncertainty about the state of the resource, in this article we consider the possibility that production, which depends on the aggregation of strategies, is a set-valued mapping. We examine appropriate selections of this mapping and analyze the corresponding two-stage games where a social planner regulates the use of the resource and act as a leader. We prove the existence of a Stackelberg-Nash equilibrium and discuss its properties.

Binary extended theta operation of binary soft sets

2026-01-23T10:13:52+08:00

Binary soft set theory, first introduced by Açıkgöz and Taş in 2016, has become widely accepted as a technique for addressing and modeling uncertainty. Numerous theoretical and practical problems have been solved using this approach. Scholars have shown sustained interest in the theory's core concepts and operations since its inception. In this study, we propose the binary extended theta operation, a special binary soft set operation, and provide a thorough analysis of its basic algebraic features. We also study the distribution of this operation over certain types of binary soft set operations. By considering its algebraic properties and distribution rules, we show that, when combined with specific binary soft set operations, the binary extended theta operation forms many important algebraic structures within the collection of binary soft sets over the universe under certain conditions. The fundamental conceptual difference between the proposed binary extended theta operation and existing binary extended operations in the literature is that unlike approaches based on positive information aggregation, the theta operation systematically extracts negative information through common parameters and offers a unique and complementary tool, particularly for decision problems requiring reliable elimination, risk exclusion, and error detection. Further applications, including cryptology and decision-making, rely on operations of binary soft sets, making this theoretical subject essential from both theoretical and practical perspectives.

Proper edge coloring of direct product of path and star fuzzy graphs

2025-12-22T11:35:19+08:00

This research explored adjacent vertex distinguishing proper edge coloring (AVDPEC) in the context of the direct product of fuzzy graphs, with particular emphasis on combinations of fuzzy path and fuzzy star graph. In this study, the classical coloring theory of crisp graphs were applied to fuzzy graphs, as membership values introduce an additional layer of intricacy to them. The main contribution of this research is an algorithm for the direct product of fuzzy path and fuzzy star networks, which acquires appropriate edge coloring that distinguishes adjacent vertices utilizing fuzzy membership values. In applications where recognizing between entities or tasks is crucial, this algorithm ensures that each adjacent vertex pair in the fuzzy graph product is identified by the specific colors of their incident edges. By using fuzzy membership values, we provided a versatile structure for managing complicated systems with incomplete or probabilistic interactions between nodes. We also introduced two significant applications of this edge coloring technique: load balancing in data networks and task parallelism in parallel computing. All things considered, our work strengthens the concept of AVDPEC in uncertain graphs, which benefits graph theory both hypothetically and essentially.

Stakeholder bias and group decision dynamics: mitigating cognitive biases with an integrated consensus-building process‎

2025-10-16T16:14:11+08:00

This study examines how stakeholder-driven preferential amplification affects outcomes in participatory group decision-making (DM) when allocation consequences have material significance. We structured development priorities for local value chains using the Analytic Network Process (ANP) and directly compared the pairwise judgments of value-chain representatives with those of other panel members to assess incentive alignment effects. Pre-consensus analysis revealed substantial divergence between stakeholder and non-stakeholder evaluations, indicating strong preferential positioning within the ANP structure under high-stakes conditions. We then implemented a structured Delphi-based iterative consensus refinement process and returned aggregated judgments (geometric means) to participants across two rounds to enable controlled reconsideration and revision. Post-iteration analysis shows a marked reduction in between-group divergence. Effect size assessment confirms moderation of extreme preferential positions rather than their complete elimination, consistent with the limited statistical power of small stakeholder subgroups. These findings demonstrate that participatory group DM frameworks remain essential for inclusive governance. However, when incentives are directly linked to allocation outcomes, decision architects should design balanced panel structures and incorporate structured consensus-feedback mechanisms to enhance robustness, transparency, and stability in ANP-based policy applications.

Bias regulation in algorithmic decision systems: a metaheuristic intervention for business development decisions

2026-09-01T11:07:51+08:00

Metaheuristic decision systems can develop exploration-exploitation imbalance under cognitive bias. Existing decision research examines outcomes, adaptive search control, and bias reduction but gives limited attention to internal search structure. This study develops the Metaheuristic-Based Bias Intervention Module as a decision analysis artifact that combines genetic algorithm and particle swarm optimization search with cognitive bias mechanisms, bounded parameter control, and structural indicators. Controlled experiments compare a genetic algorithm, a fixed hybrid configuration, and the proposed module across simulated business development allocation scenarios. Results show that the proposed module preserves decision accuracy and bias reduction while improving symmetry and convergence stability relative to the fixed hybrid configuration. Recorded parameter changes confirm controller activity, while calibration tests and comparisons with entropy, proportional balance, and population diversity support symmetry as a measure of search structure. Exchange symmetry contributes a measurable property of search allocation under cognitive bias and distinguishes structural balance from outcome quality. Integrating adaptivity, symmetry, stability, bias reduction, and accuracy creates a process-level diagnostic structure for decision analysis. Practical relevance lies in a proposed diagnostic logic for search concentration and potential intervention points in partner selection, investment prioritization, and opportunity evaluation. The computational framework provides a foundation for subsequent organizational research on contextual calibration, managerial utility, and decision quality.

Fuzzy approach to analyzing a three-factor experiment with binary levels

2026-01-15T13:34:25+08:00

A factorial experiment is a widely used statistical analysis technique in experimental studies in agricultural and engineering sciences. The classical factorial experiment design considers several factors at different crisp levels, which are quantitatively measurable. However, factor levels are often imprecise, vague, incomplete, or described linguistically. In such cases, fuzzy statistics are used instead of classical statistics when analyzing data with uncertain observations. In this paper, we propose an extension of the classical factorial design for analyzing experiments with fuzzy observations. The experimental data are represented using triangular fuzzy numbers and transformed using the α-cut interval approach, resulting in lower-level and upper-level factorial models. These models are analyzed separately to evaluate the effects of factors and their interactions under uncertainty. The results are also compared with classical ANOVA applied to defuzzified data to highlight the advantages of incorporating uncertainty through fuzzy representations. Furthermore, the robustness of the proposed approach is examined through sensitivity analysis across different α levels. The findings demonstrate that the proposed fuzzy factorial approach provides a more flexible framework for analyzing experimental data in the presence of imprecision and uncertainty.

Decision-oriented explainable artificial intelligence: a PDR-based review of methods, applications, and emerging frontiers

2026-06-29T10:06:29+08:00

Explainable artificial intelligence (XAI) is increasingly used in consequential decisions, but method selection still requires evidence about predictive reliability, explanatory fidelity, and stakeholder usefulness. This latent Dirichlet allocation (LDA)-assisted structured review used the OpenAlex Works metadata application programming interface (API) to retrieve 4,200 English-language records published from 2018 to 13 July 2026. Sequential DOI, exact-title, and fuzzy-title deduplication retained 3,166 records; title, abstract, and keyword screening retained 694; eligibility assessment retained 666; and 666 records entered the final document-term matrix. Candidate LDA models with k = 5 − 16 were compared using c_v and u_mass coherence, conventional perplexity, matched-topic stability, and Jensen–Shannon separation. The selected k = 8 solution achieved c_v = 0.5226, stability = 0.6809, and mean topic separation = 0.5824. Manual inspection of the top terms and documents identified themes concerning robust counterfactual generation, responsible and regulated decision support, interpretable clinical risk prediction, industrial, cybersecurity, and infrastructure XAI, causal and actionable algorithmic recourse, visual and multimodal medical explanation, clinical XAI adoption, trust, and workflow, and human-centered XAI evaluation and taxonomy. These evidence-derived themes are integrated with the adopted Predictive-Descriptive-Relevance (PDR) framework as a decision-oriented synthesis lens. The review contributes a reproducible literature map, a critical method comparison, a stakeholder-centered cross-domain matrix, and a research roadmap for causal, robust, uncertainty-aware, multimodal, generative, foundation-model XAI.