Facility layout design problem considers the departments’ physcial layout design with area requirements in some restrictions such as material handling costs, remoteness and distance requests. Briefly, facility layout problem related to optimization of the layout costs and working conditions. This paper proposes a new multi objective simulated annealing algorithm for solving of the unequal area in layout design. Using of the different objective weights are generated with entropy approach and used in the alternative layout design. Multi objective function takes into the objective function and constraints. The suggested heuristic algorithm used the multi-objective parameters for initialization. Then prefered the entropy approach determines the weight of the objective functions. After the suggested improved simulated annealing approach applied to whole developed model. A multi-objective simulated annealing algorithm is implemented to increase the diversity and reduce the chance of getting layout conditions in local optima.

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.

Decision-making under uncertainty is one of the most critical challenges in disaster management, where human risk perception is the most important factor that shapes responses to crises. This paper introduces a hybrid risk-based decision model that integrates behavioral science, probabilistic risk assessment, and machine learning in an analysis of how individuals and organizations make high-stakes decisions under uncertainty. Our model fuses cognitive biases with dynamic risk evaluation to capture real-time fluctuations in decision-making behavior during disasters. While conventional models rely on the basis of rational decision-making, we introduce bounded rationality, emotional triggers, and social influence factors to make it more realistic. Bayesian networks and agent-based simulations are applied in the proposed framework to forecast adaptive decision pathways and gain insight into the evolution of uncertainty perception over time. The model is tested by case studies on natural disasters, pandemics, and industrial accidents and uncovers the key patterns of delays in response, overconfidence, and risk-averse attitudes. Extensive results show how decision inertia, misinformation, and cognitive overload under stress violate many axioms of traditional risk models. We propose a new "risk-perception feedback loop" by integrating real-time information with AI-driven adaptive decision-making tools, aiming at enhanced resilience within disaster scenarios. The study blends computational modelling with psychological insight, yielding a revolutionary approach to both understanding and better decision-making in the face of extreme uncertainty, with a strong possibility of profoundly smarter disaster response systems and significantly more effective risk communication strategies.

Predicting and managing stock and demand balance is a critical aspect of supply chain management. In this paper, we propose a predictive analytics approach that leverages deep Q-learning algorithm to optimize stock and demand balance. Traditional forecasting methods often struggle to capture the complex dynamics and uncertainties associated with stock and demand fluctuations. The deep Q-learning algorithm, a reinforcement learning technique, offers a promising solution by learning optimal decision-making policies through interaction with the environment. The proposed approach utilizes historical stock and demand data to train a deep Q-learning model, enabling it to make accurate predictions about future stock levels and demand patterns. By considering factors such as seasonality, trends, and external variables, the model learns to adjust stock levels to meet demand while minimizing excess inventory or stock outs. To validate the effectiveness of the proposed approach, a case study conducted using real-world data from a supply chain network. The results demonstrate that the deep Q-learning algorithm outperforms traditional forecasting methods, achieving higher accuracy in predicting stock and demand balance. The implications of this research are significant for supply chain managers and decision-makers. By incorporating predictive analytics with the deep Q-learning algorithm, companies can enhance their inventory management strategies, reduce holding costs, minimize stock outs, and improve customer satisfaction.