جستجوی مقالات مرتبط با کلیدواژه « Fuzzy Theory » در نشریات گروه « صنایع »

Risk management improves and increases the speed of development and optimal implementation of the company's strategy to achieve a competitive advantage. Risk identification and assessment are known as one of the main tools of safety management, which helps the safety manager better select risk reduction measures and standardization of automobile oil filter by creating a suitable information platform. In this regard, evaluating and analyzing failure modes and their effects is an appropriate tool for risk management and improving product quality. Due to the weaknesses of the traditional method the complexities of the fuzzy method, a new type of risk non-priority is presented by assigning different weights to each of the risk factors under uncertainty and the proposed method is compared with the traditional and fuzzy methods simultaneously. The purpose of this study is to analyze the failure mode and risks in operation and various stages of manufacturing automobile oil filter, then to prioritize and compare risks by applying the fuzzy theory method and robust data envelopment analysis. Oil filter is an essential part of the automobile that its standardization reduces fuel consumption, improves engine performance and consequently decreases environmental pollution. This research has used the combination of Failure Mode and Effect Analysis( FMEA) method for analyzing the reliability of the oil filter and fuzzy theory has been used to record experts' opinions on failure modes and calculate the risk priority of each subsystem under uncertainty. In order to eliminate the existing defects, a new method is introduced for calculating the risk priority number in the failure mode and effect analysis based on the data envelopment analysis method. In this research, the robust optimization method covers the results of the data envelopment analysis (DEA) and is less complex than the fuzzy method has been used. The results of the case study indicate that the proposed model is more effective and reliable than the traditional and fuzzy Risk Priority Number (RPN) and also the proposed method has less has complexity than the fuzzy method. This method provides a complete ranking and convincing prioritization of failure modes. After calculating the RPNs, the operations related to the spiral tube, fiber folding, ring bending and cutting, and fiber folding are the highest number of RPNs, respectively, and their corrective actions were also determined.

The production and service functions in the innovation system emphasize producing new products. As any innovation system’s final output must ultimately produce new products and deliver new services, the considered function is essential in the innovation system. This study seeks to extract the challenges and give solutions to improve the production and deliver services in the innovation system. Producing and delivering services does not occur in a vacuum and should occur in a context called the Supply Chain (SC). In this paper, qualified experts were interviewed to discover the challenges and solutions to improve production. Fifteen elite researchers in the innovation field then discussed the results in focus sessions and refined practical solutions after an in-depth review of the extracted information. The role of responsible institutions was determined, and the necessary indicators were extracted to evaluate the factors of innovative production after providing the required infrastructure to achieve production promotion. One of the Fuzzy Multi-Attribute Decision Making (FMADM) techniques is also used to prioritize the discovered solutions based on the importance of influencing the producing promotion. Finally, a model is presented for improving the production and service functions in the innovation system. The results showed the main solution is to collaborate and create efficient integration at different SC levels.

Constant unit procurement cost is one of the main assumptions in the classic inventory control policies. In the realistic world and practice, suppliers sometimes face increase in the price of a known item. In this paper, an inventory model for items with a known one-time-only price increase under fuzzy environment is presented by employing trapezoidal fuzzy numbers to find the optimal solution. We developed three different policies on the basis of methods such as α-cuts, for defuzzification of internal parameters before solving the model, and Vujosevic, for difuzzification of the external parameters after solving it. In the first policy, we integrated α-cuts method and Parametric Non-Linear Programming ( ) problems to attain the Membership Functions ( ) of external variables in the primary model for achieving the optimal solution. These variables were reached by internal parameters through two-phase maximum/minimum non-linear programming problems and the external variables were approximate fuzzy numbers. Under the other two policies, we used defuzzification techniques of Centroid of Gravity ( ), Signed Distance ( ), and the Maximum Degree of Membership ( ) to attain crisp numbers. The optimal order policies by the three methods were compared and numerical computations showed that the efficiency of the first method (i.e., the presented one) was considerably better than that of the other two methods. In fact, the first method selected the optimal and attractive strategies by allocating membership functions to different α-cuts and provided the Decision Maker ( ) with great information to decide and select the best strategies. The methods were validated by a numerical example. The main aim of this model was determining the special ordering range and net costs saving quantity (involving ordering, holding, and purchasing costs). The time of ordering for positive net costs saving was calculated.

Nowadays, production systems seek to integrate production and maintenance activities. An effective maintenance plan can improve maintenance stability and system performance. Machines that stop for repairing operation impose a high cost on the system. On the other hand, there are always some intangible situations during a production process in which repairing activities can be carried out. If they are detected, system productivity can be improved. The main purpose of this study is specifying Maintenance Opportunity Window (MOW) in job-shop production systems. For this purpose, mathematical models and formulae were developed in order to determine the MOW in a way that they could provide maximum repairing time for the machine and, as a result, the lowest disturbance occurring in production. This model also determines the number of lost products during PM. Considering the manpower of maintenance and M/M/1//k queueing model, the terms required for repairs are addressed. Finally, numerical experiments on and sensitivity analysis of critical parameters of the model, such as the initial level of the buffers and processing rates of the machines, are considered. Model validation is carried out by comparison of the results with a simulation model. In this study, some suggestions for improving the system are proposed.

The present study proposes an integrated model for hub location problem in a Multi-location, Multi-period, Multi-commodity (3M), three echelon supply chain. The problem is formulated as a mixed integer programming model and solved using GAMS software. As the developed model is a mixed integer non leaner programming and NP-hard, a new algorithm for re-formulation is proposed to change it to a mixed integer leaner programming and also a new heuristic algorithms is proposed to solve it in a reasonable time. To prove the applicability of the model, the well-known real CAB data set is used. Numerical examples show the benefit of the proposed model in both solution time and result quality.

FMEA (Failure Mode and Effect Analysis) refers to a proactive quality tool that enables the identification and prevention of the potential failure modes of a product or process. However, in executing traditional FMEA, the difficulties such as vague information, relative importance ratings, decisions on same ratings, and opinion difference among experts arise which reduce the validity of the results. This paper presents a fuzzy logic based FMEA depending on fuzzy IF-THEN rules over traditional FMEA to make it precise and give proper maintenance decision. Here, the Risk Priority Number (RPN) is calculated and compared to the Fuzzy Risk Priority Number (FRPN) to give maintenance decision. Furthermore, the FMEA of Reach Stacker Crane (RST) is presented to demonstrate the proposed Fuzzy FMEA.

Reliability is one of the most important factors of complex systems which play a crucial role in performance of modern systems. In this study, a novel algorithm for estimating reliability of ready-to-use systems in designing phase for designed lifetime is proposed. At first stage, the related studies are checked, and then fundamental theories of each section are presented. According to the particular structure of ready-to-use systems and Markov Chain conditions, a new model based on Markov method and Fuzzy approach is suggested. The performance of proposed model is validated by testing on a real system. Therefore, the reliability and mean time to failure of the industrial system is estimated by the algorithm. Finally, practical suggestions are recommended for optimizing the system reliability.