درخت حوزه‌های تخصصی

The supply chain of perishable products has high complexity due to the short lifespan of the products. For this reason, it is necessary to evaluate various issues in the supply chain such as location, inventory and logistics in an integrated manner by using flexible tools. The purpose of this research is to present an optimization approach based on event-factor-based discrete hybrid simulation for the integrated optimization of facility location, inventory control, fleet composition and routing in the supply chain of perishable products.The optimization process is performed using meta-heuristic algorithms. In order to conduct a case study, the supply chain of one of the largest producers of dairy products in the country has been considered, and due to the high demand and production of ice cream, this product has been considered as a perishable product of the supply chain. The results show that the integrated optimization of the research topic leads to a significant improvement in product waste, reducing the time of processing and delivering orders, and reducing the number of fleets.

Objective : The construction industry has been increasingly criticized for its poor sustainability performance in recent decades, creating a chance for the sector to play a key role in global sustainability efforts. Rapid technological advancements and increasing construction project complexities have driven the need for flexible, sustainability-focused project management frameworks. This study introduces a fuzzy inference system designed to evaluate construction project sustainability, built on insights from extensive literature and expert input. Methods : To design the proposed model, the system inputs—criteria for evaluating the sustainability level of construction projects at various layers—were first identified. Next, the necessary if-then rules were developed based on expert opinions. The system output was determined in alignment with the research’s final objective. By offering a comprehensive assessment of construction project sustainability, the model enables organizations to identify their strengths and weaknesses, assess their current position, and make informed decisions to enhance their sustainable performance.  Results : The output of the research includes a detailed analysis of the sustainability performance of construction projects. The designed model, along with its measurement tools, provides an opportunity for leaders and managers in the construction industry who are concerned about sustainability to gradually enhance their sustainability status and advance the sustainability level of projects. This model consists of three subsystems named the Direction, Execution, and Results subsystems. The aforementioned subsystems are the result of a literature review and are considered inputs to the final level of the model. Conclusion : The designed model serves as a tool to identify and implement improvement methods and potential areas for project advancement from a sustainability perspective. By utilizing this model, the quality of project execution in line with sustainability indicators, while addressing all three dimensions—economic, social, and environmental—improves continuously and proportionately.

The widespread adoption of Android phones has heightened concerns about user privacy. This research presents an Adaptive Privacy Management System (APMS) that integrates Machine Learning (ML) models with Differential Privacy techniques to enhance privacy protection. The APMS monitors application behavior and employs ML algorithms to detect anomalies and enable context-aware privacy enforcement. Differential Privacy ensures that sensitive data remains protected through the addition of noise and privacy-preserving computations. Experimental results demonstrate that the APMS achieves a 92.5% accuracy rate in detecting the privacy leakage. The anomaly detection model, using Random Forest, shows high accuracy (92.5%), recall (89.5%), and precision (73.9%), effectively identifying both normal and anomalous behaviors. Additionally, the impact of noise on data utility, controlled by the privacy budget (ε), is manageable. The results show that APMS is a robust system for safeguarding user confidential information, contributing to a more secure and privacy-centric Android ecosystem.