reliability

The objective of this research is to optimize defense and attack strategies in sensitive systems with series-parallel structures and virtual components using a game theory approach. In this framework, the defender faces resource constraints, and the attacker aims to maximize losses.

To achieve the optimal balance between the attacker and defender strategies, a mathematical model based on game theory is developed. This model considers features such as heterogeneous components, series-parallel reliability structures, the use of active and cold standby components, and the creation of artificial targets to deceive the attacker. Also, a nonlinear model is presented for defense optimization.

The proposed model is able to identify Nash equilibrium in defense and attack strategies and determine the optimal combination of defense resource allocation, virtual target deployment, and active redundancy mechanisms. Numerical results confirm the effectiveness of the simultaneous use of artificial targets and standby components in increasing system reliability.

 For the first time, this study presents an innovative integration of reliability theory, deception-based defense strategies, and game theory for the design of sensitive systems. The developed model provides the ability to simultaneously analyze the behavior of the attacker and the defender in an environment with high uncertainty.

This paper presents an innovative model for the simultaneous optimization of redundancy allocation and condition-based maintenance in series-parallel load-sharing systems. The primary objective of the model is to determine the optimal level of redundancy so that costs are minimized while system reliability constraints are met.

In this research, stochastic dependencies between system components are considered using the proportional hazards model and tempered failure rates to assess reliability accurately. Additionally, transition probability matrices are used to determine the optimal maintenance limits for each subsystem, and periodic inspections are performed. The proposed model is solved using MATLAB, and its performance is evaluated under four different scenarios: 1) a baseline model without redundancy or stochastic dependencies, 2) redundancy allocation without stochastic dependencies, 3) stochastic dependencies without redundancy, and 4) the proposed model.

The results show that the proposed model achieves an optimal balance between cost and reliability, reducing both failure and maintenance costs. Compared to the various scenarios, the proposed model demonstrates superior performance in optimizing costs and enhancing reliability. The findings also emphasize the importance of simultaneously considering stochastic dependencies and redundancy allocation to improve system performance.

Originality/Value:

 This research introduces a novel approach by simultaneously considering stochastic dependencies and redundancy allocation in series-parallel load-sharing systems. The proposed model significantly improves system performance and reduces failure and maintenance costs. It underscores the importance of integrating these two factors in optimizing complex engineering systems.

This study aims to improve diesel engine reliability estimation by using a risk-based model that incorporates key environmental factors, especially wear particles in engine oil, for more accurate analysis than traditional time-based methods.

The Proportional Hazards Model (PHM) was used to assess engine reliability based on wear particles in oil. The Harrell and Lee test checked model assumptions, and the Wald test validated coefficients. Reliability was then compared across two engine groups under different conditions.

The study's results showed that incorporating risk factors, such as the level of wear particles in engine oil, increases the accuracy of reliability estimation for diesel engines. Specifically, it was found that engine age, maintenance status, and operational conditions significantly impact reliability, such that worn-out engines reach lower levels of reliability more quickly. The proposed model, by providing a more precise analysis, can serve as an effective tool for optimizing maintenance scheduling and preventing unexpected failures in industrial systems.

Originality/Value:

 This research's primary distinction lies in the integration of qualitative data related to the internal condition of the engine (wear particles in oil) with advanced statistical models of PHM, which has been less addressed in previous studies. This approach, by creating a link between condition-based data analysis and reliability analysis, opens new horizons for condition-based maintenance planning.

A few studies have addressed the failure or reliability of machines regarding cell formation problems. The general assumption in cell formation is that most machines are 100% reliable; however, they are not in a practical situation. Machine failure can severely affect the system performance and cause a delay in the scheduled date. A cellular manufacturing system is a philosophy among group technology ones, which is controlled by dividing a large system to multiple smaller sub-systems and facilitate manufacturing system management. This study presents a mathematical programming model for production planning problems in industrial units with reliability that prepares the conditions to utilize alternative routes for parts, which minimizes the lost costs along with maintenance costs. Since the considered problem is an NP-hard one, a genetic algorithm is used to solve the model. The presented mathematical model minimizes system costs, and the costs related to intra- and inter-cellular movements and maintenance by minimizing the costs of machine failures.

Today, the increasing progress in technology, the expansion and development of human needs for sustainable technologies have caused attention to electric energy to be in the center of attention more than in the past. Therefore, increasing the reliability of electrical systems in the power industry is very important. The purpose of this research is to provide a mathematical model to calculate and increase reliability in the power distribution network. This research is practical in terms of its purpose and results, and in terms of the method and nature of implementation, it is based on operational research that was conducted using mathematical modeling and using Python software based on data from 1398 to 1402. The findings show that parameters such as generators, high pressure and low pressure busbars, 20 kV to 400 V power transformers, communication cables, capacitors, generators and UPS are more important in calculating the reliability of this network. Therefore, according to the purpose and the corresponding limitations, a suitable mathematical model has been presented for each parameter. The results show that after 50 repetitions and simulations, the ultra-emergency line has a higher importance and rank in reliability among the four output feeders. Also, based on the presented model, it has been observed that the entire 20 kV line under investigation has 0.67 degrees of reliability. The results of this research can be considered as a suitable basis for the implementation of research and operational projects in wide radial networks in the electricity industry.

The purpose of this research is to design a perishable crops supply chain network. Furthermore, the disruption risk in the production sector and the protection strategy are considered to increase the reliability of the network. The goal of the proposed model is to maximize the total profit of the supply chain while considering the time to send the products to the consumers.

In this research, a mixed-integer non-linear programming model is proposed to optimize crops supply chain network design. Moreover, a real case study has been implemented in Iran to prove the applicability of the addressed model.

The results and sensitivity analysis of the mathematical model indicate that by planning the planting and harvesting While optimizing the chain's profit, it is possible to respond well to society's product demand. Moreover, considering the disruptions and protection of products brings us closer to real-world conditions and reduces the amount of waste and related adverse environmental effects.

Originality/Value: 

Agriculture is one of the most important and influential sectors in the economy of any country, it plays a vital role in its political and economic independence. The growing global demand to provide food for the human population has caused a significant increase in cultivation. However, the amount and time of production should be consistent with the amount of product demand and avoid a shortage or excess of products. Considering that there is currently no comprehensive planning for the production of perishable crops in the country, the necessity of studying supply chain models in the agricultural sector and specifically the production and distribution of perishable crops is created. In this connection, the design of the supply chain network of the mentioned products from planting to distribution is presented in this study.

In this research, a multi-product and multi-period closed-loop supply chain design problem with three objectives of profitability, social responsibility, and reliability under uncertain conditions has been considered.

Triangular fuzzy numbers have been used for non-deterministic parameters, and a robust probabilistic programming approach with the Me scale has been used to deal with fuzzy constraints. The proposed approach eliminates the need for iterative consideration by decision-makers by providing unlimited choices from the optimism-pessimism spectrum. The mathematical model developed in this research is of mixed integer linear programming type, which is implemented in GAMS software to solve it and find Pareto optimal solutions.

The accuracy of the overall performance of the proposed model has been evaluated with four examples (based on the coefficients of the objective functions) from a case study in the aluminium industry. The sensitivity analysis of the demand parameter showed that the proposed model achieved more economic profit, less social responsibility, and less reliability with increasing demand.

The variability of the justified decision space in the Me criterion has helped to solve the supply chain network design problem more flexibly and closer to reality through the possibility of exchange between the objective function and the risk-taking level of the managers.

Despite the importance of High Reliability Organizations (HROs), it has not been sufficiently addressed in our country. The design of scientific frameworks that provide the possibility of a correct understanding of these organizations and the measurement and evaluation of their various dimensions is decisive in the situational awareness of organizations and the possibility of effective organizational decisions and the presentation of improvement programs. With the aim of answering this need, this research provides a comprehensive framework for evaluating the maturity of High Reliability Organizations. In this research, a Meta-Synthesis method has been used, during which, based on the seven-step method of Sandelowski and Barroso, related sources and models have been examined and analyzed, and finally, a five-level model with a comprehensive approach has been designed. At each level, the characteristics of achieving the maturity level are presented. This model can be a basis for evaluating and analyzing the maturity level of High Reliability Organizations, as well as designing improvement and development programs for these organizations.

The Permasin engine is a critical mission-oriented system, the failure of which will cause the failure of the main mission. Comprehensive studies on its reliability can be a step towards improving the performance of this system. The aim of the article is to analyze the reliability of the Permasin engine in the specified scenarios. In this article, first of all, the structure of Permasin engine as the studied system is described and the breakdown structure of the product is drawn for this system, and according to the mission-oriented nature of this system, there are 2 time scenarios, the basis of floating performance, definition and failure rate for sensitive parts from the standard. MilHDB-217 and NPRD-95 will be calculated in two optimistic and pessimistic modes. By using the breakdown structure, the connection of specific system components and the reliability block diagram are drawn in the Reliability Workbench software. Finally, the reliability of the subsystems and the studied system is calculated separately for both working scenarios. The results show that drive and motor subsystem has the lowest level of reliability compared to other subsystems in rated power.

Customers now care more than ever about the sustainability and reliability of products packaging. This research considers a multi-product and multi-period closed-loop supply chain design problem with three objectives of profitability, social responsibility, and reliability under uncertain conditions. Triangular fuzzy numbers have been used for non-deterministic parameters and a robust probabilistic programming approach with Me scale has been used to deal with fuzzy constraints. The proposed approach eliminates the need for iterative consideration by decision-makers by providing unlimited choices from the optimism-pessimism spectrum. The mathematical model developed in this study is of mixed integer linear programming type, which is implemented Augmented Epsilon Constraint (AEC) method in GAMS software to solve it and find Pareto optimal solutions. The accuracy of the overall performance of the proposed model has been evaluated with four examples (based on the coefficients of the objective functions) from a case study in the Carton-Making Industry. The obtained results indicate the existence of a conflict between the three objective functions. With this account, decision-makers should demand lower profits for increased environmental protection and improved reliability compared to the situation where only the economic aspect is considered. The variability of the justified decision space in the Me criterion has helped to solve the supply chain network design problem more flexibly and closer to reality through the possibility of exchange between the objective function and the risk-taking level of the managers.