Search Results (871)

Fog computing is increasingly preferred over cloud computing for processing tasks from Internet of Things (IoT) devices with limited resources. However, placing tasks and allocating resources in distributed and dynamic fog environments remains a major challenge, especially when trying to meet strict Quality of Service (QoS) requirements. Deep reinforcement learning (DRL) has emerged as a promising solution to these challenges, offering adaptive, data-driven decision-making in real-time and uncertain conditions. While several surveys have explored DRL in fog computing, most focus on traditional centralized offloading approaches or emphasize reinforcement learning (RL) with limited integration of deep learning. To address this gap, this paper presents a comprehensive and focused survey on the full-scale application of DRL to the task offloading problem in fog computing environments involving multiple user devices and multiple fog nodes. We systematically analyze and classify the literature based on architecture, resource allocation methods, QoS objectives, offloading topology and control, optimization strategies, DRL techniques used, and application scenarios. We also introduce a taxonomy of DRL-based task offloading models and highlight key challenges, open issues, and future research directions. This survey serves as a valuable resource for researchers by identifying unexplored areas and suggesting new directions for advancing DRL-based solutions in fog computing. For practitioners, it provides insights into selecting suitable DRL techniques and system designs to implement scalable, efficient, and QoS-aware fog computing applications in real-world environments. Full article

Real-time coordination of heterogeneous multi-agent systems in dynamic and partially observable environments poses significant challenges. To address this, we propose a framework that integrates fuzzy inference systems with real-valued genetic algorithms to optimize decision-making under strict time constraints and sensory uncertainty. We evaluate the proposed method in the RoboCup Soccer Simulation 2D League, where 22 autonomous agents coordinate through a fuzzy-evaluated action sequence search. Spatial heuristics are encoded as fuzzy rules, and optimization based on genetic algorithms refines evaluation function parameters according to performance metrics such as number of shots, goal area entries, and scoring rates. The resulting control strategy remains interpretable; spatial heat maps reveal emergent behaviors such as coordinated positioning and ridgeline passing patterns near the penalty area. The experiments against established RoboCup teams, serving as benchmarks, demonstrate the competitive performance of our trained agents while enabling analyses of evolving decision structures and agent behaviors. Our method provides a transparent and adaptable framework for controlling heterogeneous agents in uncertain real-time environments, with broad applicability to robotics, autonomous systems, and distributed control systems. Full article

Background: Pediatric tracheostomy decisions are challenging for clinicians and parents, especially when a child’s survival/neurodevelopmental outcome is uncertain. Better understanding of parents’ values over the decision period is crucial for clinical decision-making. Objective: To describe parents’ values during tracheostomy decision-making for their critically ill child and to identify opportunities to improve parent–clinician shared decision-making (SDM). Methods: We thematically analyzed 12 semi-structured interviews with parents who recently faced a tracheostomy decision for their critically ill child. Three study team members with qualitative expertise reviewed the transcripts, identifying key topics independently. A codebook was developed, and data were coded. Key research questions guided analysis, with findings iteratively reviewed by the study team. Results: We identified parents’ values at the three time points: when the decision was introduced, during their deliberations of it, and when the ultimate decision was made. Initially, parents resisted tracheostomy because it threatens normalcy. They valued proof of a need for tracheostomy and information with certainty. As certainty for tracheostomy increased over time, parents’ hope focused on reversibility of tracheostomy and improvement in normalcy compared to current status. They concurrently worried about practical issues such as emergencies, home care, and finances. Key considerations driving the final decision included best interest of the child, perceived benefits of tracheostomy compared to its downsides or other options, and potential for better quality of life and longer life. Conclusions: Parents’ dynamic values shifting with clinical uncertainty suggests opportunities to improve SDM by attending to parents’ individualized needs and managing expectations. Full article

As manufacturing and logistics-oriented supply chains continue to expand in scale and complexity, and the coupling between their physical execution layers and information–decision layers deepens, the resulting high interdependence within the system significantly increases overall fragility. Driven by key technological barriers, economies of scale, and the trend toward resource centralization, supply chains have increasingly evolved into centralized structures, with critical functions such as decision-making highly concentrated in a few focal firms. While this configuration may enhance coordination under normal conditions, it also significantly increases dependency on focal nodes. Once a focal node is disrupted, the intense task, information, and risk loads it carries cannot be effectively dispersed across the network, thereby amplifying load spillovers, coordination imbalances, and information delays, and ultimately triggering large-scale cascading failures. To capture this phenomenon, this study develops a coupled network model comprising a Physical Network and an Information and Decision Risk Network. The Physical Network incorporates a tri-load coordination mechanism that distinguishes among theoretical operational load (capacity), actual production load (production output), and actual delivery load (order fulfillment), using a load sensitivity coefficient to describe the asymmetric propagation among them. The Information and Decision Risk Network is further divided into a communication subnetwork, which represents transmission efficiency and delay, and a decision risk subnetwork, which reflects the diffusion of uncertainty and risk contagion caused by information delays. A discrete-event simulation approach is employed to evaluate system resilience under various failure modes and parametric conditions. The results reveal the following: (1) under a centralized structure, poorly allocated redundancy can worsen local imbalances and amplify disruptions; (2) the failure of a focal firm is more likely to cause a full network collapse; and (3) node failures in the Communication System Network have a greater destabilizing effect than those in the Physical Network. Full article

As global e-commerce expands, efficient cross-border logistics services have become essential. To support the evaluation of logistics service providers (LSPs), we propose HD-CBDTOPSIS (Technique for Order Preference by Similarity to Ideal Solution with heterogeneous data and cloud Bhattacharyya distance), a hybrid multi-criteria group decision-making (MCGDM) model designed to handle complex, uncertain data. Our criteria system integrates traditional supplier evaluation with cross-border e-commerce characteristics, using heterogeneous data types—including exact numbers, intervals, digital datasets, multi-granularity linguistic terms, and linguistic expressions. These are unified using normal cloud models (NCMs), ensuring uncertainty is consistently represented. A novel algorithm, improved multi-step backward cloud transformation with sampling replacement (IMBCT-SR), is developed for converting dataset-type indicators into cloud models. We also introduce a new similarity measure, the Cloud Bhattacharyya Distance (CBD), which shows superior discrimination ability compared to traditional distances. Using the coefficient of variation (CV) based on CBD, we objectively determine criteria weights. A cloud-based TOPSIS approach is then applied to rank alternative LSPs, with all variables modeled using NCMs to ensure consistent uncertainty representation. An application case and comparative experiments demonstrate that HD-CBDTOPSIS is an effective, flexible, and robust tool for evaluating cross-border LSPs under uncertain and multi-dimensional conditions. Full article

Facing the global competitive market and ever-changing customer demands, manufacturers must navigate intense competition and uncertain demand while striving to enhance customer satisfaction. As a result, the demand for customized products has become a crucial design consideration. To respond accurately and swiftly in a competitive market, manufacturers must focus on customer needs, analyze market trends and competitor information, and leverage data analysis as a reference for new product development and design. This study presents a new product development model by integrating quality function deployment (QFD), decision-making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and fuzzy set theory. It first uses a 2-tuple fuzzy DEMATEL to identify significant interrelationships among factors. A revised house of quality (HOQ) is then constructed to map relationships among customer requirements (CRs), engineering requirements (ERs), and the influences of CRs on ERs. To address uncertainty in human judgment, fuzzy set theory is incorporated into the ANP. The integrated model can determine the relative importance of the ERs. The proposed model is applied to touch panel development, and the results are recommended to the R&D team for new product development. Full article

Real-time statistical inference plays a pivotal role in maritime Command and Control (C2) environments, particularly for applications such as satellite-based object detection and underwater signal interpretation. These contexts often require online multiple hypothesis testing mechanisms capable of sequential decision-making while preserving statistical rigor. A primary concern is the control of the False Discovery Rate (FDR), as erroneous detections can impair operational effectiveness. In this study, we investigate the robustness of the Levels based On Recent Discovery (LORD) algorithm under adversarial conditions by introducing controlled perturbations to the data stream—specifically, missing or corrupted p-values derived from simulated Gaussian distributions. Inspired by developments in corruption-aware multi-armed bandit models, we formulate adversarial scenarios and propose defense strategies that modify the LORD algorithm’s threshold sequence and integrate an online Benjamini–Hochberg procedure. The results, based on extensive Monte Carlo simulations, demonstrate that even a single missing p-value can trigger a cascading effect that reduces statistical power, and that our proposed mitigation strategies significantly improve algorithmic resilience while maintaining FDR control. These contributions advance the development of robust online statistical decision-making tools for real-time maritime surveillance systems operating under uncertain and error-prone conditions. Full article

Background/Objectives: Medication discontinuation attributable to adverse drug reactions (ADRs) and/or inefficacy remains a concern of psychotropic medications among children and adolescents. Pharmacogenetic (PGx) testing has been proposed to individualize treatment, although its utility remains uncertain. We retrospectively evaluated whether PGx testing of two key metabolism genes (i.e., CYP2C19 and CYP2D6) explains reported episodes of ADRs and treatment inefficacy experienced by children and adolescents with diverse mental health conditions. Methods: PGx testing of CYP2C19 and CYP2D6 was conducted for 100 participants before, during, or after the use of psychotropic medication(s) that have clinical practice guidelines supporting PGx-guided dosing. The theoretical impact on medication dosing was reviewed in the context of clinical guidelines. We then evaluated whether the PGx-inferred metabolizer phenotype was consistent with reported ADR and/or treatment inefficacy. Results: If PGx testing had been performed before the start of treatment, 43% (35/82) of participants would have been recommended dose adjustments or alternative therapy of at least one medication. PGx test results corroborated 8% (6/76) of ADR events and 3% (2/61) of inefficacies. However, no single participant had all prior reported ADRs or inefficacies explained by the results of CYP2C19 nor CYP2D6 testing. Conclusions: Reactive testing of CYP2C19 and CYP2D6 provided limited insight into isolated incidents of psychotropic medication intolerance in this population. No individual’s PGx test results explained all episodes of ADR or suboptimal response. Variation in drug metabolism genes alone does not provide an explanation for multiple episodes of inefficacy or adverse reaction. In the setting of child and adolescent psychiatry, PGx testing is best suited for preemptive use to complement clinical decision making. Full article

Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments. Full article

This theoretical study rigorously investigates how generative artificial intelligence reshapes decision-making in social entrepreneurship by modeling the opportunity search process through the lens of optimal stopping theory. Social entrepreneurs often face high uncertainty and resource constraints, requiring them to strategically balance the cost of continued searching with the chance of identifying socially impactful opportunities. This study develops a formal model that captures two core mechanisms of generative AI: reducing search costs and increasing the probability of mission-aligned opportunity success. The theoretical analysis yields three key findings. First, generative AI accelerates the optimal stopping point, allowing social entrepreneurs to act more quickly on high-potential opportunities by lowering cognitive and resource burdens. Second, the influence of increased success probability outweighs that of reduced search costs, underscoring the strategic importance of insight quality over efficiency in socially embedded contexts. Third, the benefits of generative AI are amplified in uncertain environments, where it helps navigate complexity and mitigate information asymmetry. These insights contribute to a deeper conceptual understanding of how intelligent technologies transform the cognitive and strategic dimensions of social entrepreneurship, and they offer empirically testable propositions for future research at the intersection of AI, innovation, and mission-driven opportunity pursuit. Full article

Rough sets and fuzzy sets are two complementary approaches for modeling uncertainty and imprecision. Their integration enables a more comprehensive representation of complex, uncertain systems. However, existing rough fuzzy sets models lack the expressive power to fully capture the interactions among structural uncertainty, cognitive hesitation, and multi-level granular information. To address these limitations, we achieve the following: (1) We propose intuitionistic fuzzy covering rough membership and non-membership degrees based on maximal description and construct a new single-granulation model that more effectively captures both the structural relationships among elements and the semantics of fuzzy information. (2) We further extend the model to a multi-granulation framework by defining optimistic and pessimistic approximation operators and analyzing their properties. Additionally, we propose a neutral multi-granulation covering rough intuitionistic fuzzy sets based on aggregated membership and non-membership degrees. Compared with single-granulation models, the multi-granulation models integrate multiple levels of information, allowing for more fine-grained and robust representations of uncertainty. Finally, a case study on real estate investment was conducted to validate the effectiveness of the proposed models. The results show that our models can more precisely represent uncertainty and granularity in complex data, providing a flexible tool for knowledge representation in decision-making scenarios. Full article

In today’s dynamic and highly uncertain environment, organizations, particularly in the aviation sector, face increasing challenges that demand resilient, flexible, and data-driven change management decisions. Responding to the growing need for structured approaches to managing complex uncertainties—geopolitical tensions, economic volatility, social shifts, rapid technological advancements, environmental pressures and regulatory changes—this research proposes a theoretical change management model for aviation service providers, such as airports. Integrating three analytical approaches, the model offers a robust, multi-method approach for supporting sustainable transformation under uncertainty. Normative analysis using Bayesian decision theory identifies influential external environmental factors, capturing probabilistic relationships, and revealing causal links under uncertainty. Prescriptive planning through scenario theory explores alternative future pathways and helps to identify possible predictions, offer descriptive evaluation employing fuzzy comprehensive evaluation, and assess decision quality under vagueness and complexity. The proposed four-stage model—observation, analysis, evaluation, and response—offers a methodology for continuous external environment monitoring, scenario development, and data-driven, proactive change management decision-making, including the impact assessment of change and development. The proposed model contributes to the theoretical advancement of the change management research area under uncertainty and offers practical guidance for aviation organizations (airports) facing a volatile external environment. This framework strengthens aviation organizations’ ability to anticipate, evaluate, and adapt to multifaceted external changes, supporting operational flexibility and adaptability and contributing to the sustainable development of aviation services. Supporting aviation organizations with tools to proactively manage systemic uncertainty, this research directly supports the integration of sustainability principles, such as resilience and adaptability, for long-term value creation through change management decision-making. Full article

Intelligent driving is a key technology in the field of automotive manufacturing due to its advantages in environmental protection, energy efficiency, and economy. However, since the intelligent driving model is an uncertain multi-input multi-output dynamic system, especially in an interactive environment, it faces uncertainties such as non-uniform trial lengths, unknown nonlinear parameters, and unknown control direction. In this paper, an adaptive iterative learning control method is proposed for vehicle systems with non-uniform trial lengths and asymmetric control gain matrices. Unlike the existing research on adaptive iterative learning for non-uniform test lengths, this paper assumes that the elements of the system’s control gain matrix are asymmetric. Therefore, the assumption made in traditional adaptive iterative learning methods that the control gain matrix of the system is known or real, symmetric, and positive definite (or negative definite) is relaxed. Finally, to prove the convergence of the system, a composite energy function is designed, and the effectiveness of the adaptive iterative learning method is verified using vehicle systems. This paper aims to address the challenges in intelligent driving control and decision-making caused by environmental and system uncertainties and provides a theoretical basis and technical support for intelligent driving, promoting the high-quality development of intelligent transportation. Full article

In engineering and decision sciences, trapezoidal-valued neutrosophic fuzzy numbers $(TzVNFNs)$ have become effective tools for managing imprecision and uncertainty in multi-attribute group decision-making (MAGDM) problems. This work introduces accumulation operators based on the Dombi t-norm $(D{T}_n)$ and Dombi t-conorm $(D{T}_{cn})$ specifically designed for $TzVNFNs$. These operators enhance the flexibility, consistency, and fairness of the aggregation process. To demonstrate their practical applicability, we propose three novel geometric aggregation operator’s namely, the trapezoidal-valued neutrosophic fuzzy Dombi weighted geometric ($TzVNFDWG$), the trapezoidal-valued neutrosophic fuzzy Dombi ordered weighted geometric ($TzVNFDOWG$), and the trapezoidal-valued neutrosophic fuzzy Dombi hybrid Geometric ($TzVNFDHG$) operators. These are incorporated into a systematic MAGDM framework to support the selection of optimal locations for charging stations. Comparative analysis with current decision-making methodologies highlights the efficacy and benefits of the suggested method. The suggested method provides a flexible and mathematically based choice framework designed for uncertain condition. Full article

End-users in a decision-oriented Internet of Things (IoT) healthcare system are often left in the dark regarding critical security information necessary for making informed decisions about potential risks. This is partly due to the lack of transparency and system security awareness end-users have in such systems. To empower end-users and enhance their cybersecurity situational awareness, it is imperative to thoroughly document and report the runtime security controls in place, as well as the security-relevant aspects of the devices they rely on, while the need for better transparency is obvious, it remains uncertain whether current systems offer adequate security metadata for end-users and how future designs can be improved to ensure better visibility into the security measures implemented. To address this gap, we conducted table-top exercises with ten security and ICT experts to evaluate a typical IoT-Health scenario. These exercises revealed the critical role of security metadata, identified the available ones to be presented to users, and suggested potential enhancements that could be integrated into system design. We present our observations from the exercises, highlighting experts’ valuable suggestions, concerns, and views, backed by our in-depth analysis. Moreover, as a proof-of-concept of our study, we simulated three relevant use cases to detect cyber risks. This comprehensive analysis underscores critical considerations that can significantly improve future system protocols, ensuring end-users are better equipped to navigate and mitigate security risks effectively. Full article