The purpose of this article is to consider system safety and reliability analysts to evaluate the risk associated with item failure modes. The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (O), severity (S) and detectability (D) of an item failure mode. Because of the subjective, qualitative and dynamic nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic and system dynamics methodology is proposed. In the proposed approach, severity is replaced by dependency parameter then, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and they are evaluated in fuzzy inference engine, which makes use of well-defined rule base and fuzzy logic operations to determine the value of parameters related to system’s transfer functions. The fuzzy conclusion is then defuzzified to get transfer function for risk and failure rate. The applicability of the proposed approach is investigated with the help of an illustrative case study from the automotive industry.

Clustering is a critical data analysis and it is a popular data mining technique. This paper presents a rapid Ant based clustering-genetic algorithm (RAC-GA) with local search to solve clustering problem. GA and local search are used as a global and local search to obtain better results. The proposed algorithm is evaluated by testing on some of the well-known real-world datasets, and the results are compared with other popular heuristics in clustering, such as GA, SA, TS, ACO and RAC. The results show strong improvement both in quality solution and process time area, especially in process time which is much less than previous algorithms

During the past few years, there have been growing interests in using email for delivering various types of messages such as social, financial, etc. There are also people who use email messages to promote products and services or even to do criminal activities called Spam email. These unwanted messages are sent to different target population for different purposes and there is a growing interest to develop methods to filter such email messages. This paper presents a method to filter Spam email messages based on the keyword pattern. In this article, a multi-agent filter trade based on the Bayes rule, which has benefit of using the users’ interest, keywords and investigation the message content according to its topic, has been used. Then Nested Neural Network has been used to detect the spam messages. To check the authenticity of this proposed method, we test it for a couple of email messages, so that it could determine spams and hams from each other, effectively. The result shows the superiority of this method over the previous ones including filters with Multi-Layer Perceptron that detect spams.

The Ad hoc network is one of the multistep-based self-organizing networks, which are dynamically changing and are taken more into account as the ways of connecting the terminals through the development of wireless communication terminals. We are faced with numerous challenges in designing a wireless network such as the dynamic topology, common and limited bandwidth, and the limited energy. The nodes are moving according to the continuous changes in the topology and the source-to-destination paths are completely broken. Therefore, the repeated route discovery enhances the delay and overload of routing. Thus, it is essential to consider the link stability while designing the path in order to choose the routing protocol. Providing the multiple paths may lead to the better performance than a path. The transmission energy control in the wireless Ad hoc networks is the option for the level of transmission energy in order to transmit each node packet in this system. Therefore, transmission energy control affects the wireless medium interface. Because of choosing the appropriate protocol, the routing operation can be improved and the energy consumption can be controlled properly as well as enhancing the durability and longevity of network. The main objective of this study is to enhance the network longevity. The proposed algorithm in this research considers the combination of 2 parameters including the rate of node energy and number of steps in Fuzzy System applied on AOMDV Protocol, which is a Multipath Routing Protocol. The results of simulation also indicate the improved performance of proposed algorithm (AOMDV-F) compared to AODV and AOMDV Protocols in NS2 simulator.

In this article, a production inventory model is developed to determine the retailer’s optimal replenishment decisions under two levels of trade credit and limited storage space. Two levels of trade credit refers that the supplier provides to his/her retailer a permissible delay period to settle the account and the retailer also in turn provides a delay period to his/her customer for paying of purchasing goods. The retailer’s credit period M offered by supplier is greater than customers credit period N provides by the retailer. This paper investigates retailer optimal replenishment policy under finite replenishment rate by minimizing the total annual inventory cost. Three theorems are developed to determine optimal cycle time, optimal relevant cost and optimal order quantity for retailers accurate and rapidly. A numerical example is used to analyze the validity of propose model, Sensitive analysis shows managerial decisions for retailer.

The complexity of large-scale projects has led to numerous risks in their life cycle. This paper presents a new risk evaluation approach in order to rank the high risks in large-scale projects and improve the performance of these projects. It is based on the fuzzy set theory that is an effective tool to handle uncertainty. It is also based on an extended VIKOR method that is one of the well-known multiple criteria decision-making (MCDM) methods. The proposed decision-making approach integrates knowledge and experience acquired from professional experts, since they perform the risk identification and also the subjective judgments of the performance rating for high risks in terms of conflicting criteria, including probability, impact, quickness of reaction toward risk, event measure quantity and event capability criteria. The most notable difference of the proposed VIKOR method with its traditional version is just the use of fuzzy decision-matrix data to calculate the ranking index without the need to ask the experts. Finally, the proposed approach is illustrated with a real-case study in an Iranian power plant project, and the associated results are compared with two well-known decision-making methods under a fuzzy environment.

In construction cost management system, normally earned value management (EVM) is applied as an efficient control approach in both status detection and estimation at completion (EAC) cost forecasting. The traditional approaches in EAC predictions normally extend the current situation of a project to the future by employing pervious performance factor. The proposed approach of this paper considers both qualitative and quantitative factors affecting the EAC prediction. The proposed approach of this research not only estimates the completion of the project, but also it can generate accurate forecast for the entire future periods using a fuzzy neural network model. The model is also implemented for a real-world case study and yields encouraging preliminary results.

The fast industrial development increases different types of risks for the industries. Many risk factors are inherent in the implementation of advanced manufacturing technologies (AMTs). Industries are developing methodologies for risk prevention and protection. The present research focuses to identify various risks that could influence the implementation of AMTs, and develop a framework to mitigate them. For this framework, interpretive structural modeling(ISM) has been used to depict the relationship and priority among the various risks. This research provides a path for managers and indicates the dominant risks on the basis of higher driving power. Also, this research classifies the relationship among various risks in AMTs implementation according to their driving power and dependence. The risks have been categorized into four categories as autonomous risks, linkage risks, dependent risks and independent risks. The proposed hierarchal model would help the management to effectively handle and develop strategies against the risks and hence new and latest technologies can be adopted with ease and effectiveness.

This paper studies the problem of single machine stochastic scheduling with random processing times, deterministic due dates and an independent setup time. The jobs are also deteriorated based on the position, which their processes are done. The objective function is to find a schedule of jobs, which minimizes the expected value of maximum lateness. A branch and bound scheme is presented to solve the problem analytically and a simulated annealing meta-heuristic (SA) is also provided for solving the problem in larger scales. Computational experiments demonstrate that the proposed SA is capable of finding near optimal solutions with very low gap.

Highly automated CNC end milling machines in manufacturing industry requires reliable model for prediction of tool flank wear. This model later can be used to predict the tool flank wear (VBmax) according to the process parameters. In this investigation an attempt was made to develop an empirical relationship to predict the tool flank wear (VBmax) of carbide tools while machining LM25 Al/SiCp incorporating the process parameters such as spindle speed (N), feed rate (f), depth of cut (d) and various % wt. of silicon carbide (S). Response surface methodology (RSM) was applied to optimizing the end milling process parameters to attain the minimum tool flank wear. Predicted values obtained from the developed model and experimental results are compared, and error & LT; 5 percent is observed. In addition, it is concluded that the flank wear increases with the increase of SiCp percentage weight in the MMC.