The research paper presents a blend of theoretical framework and practical applications. In the paper, 11 enablers of Six Sigma are identified from literature survey. The required data was collected from five specialists, factory managers and maintenance managers who have worked in Six Sigma fields. Findings-Variable F8 (Committed workforce) is known as the influential and Variable F1 (ETML) is known as the effective. Other elements can be claimed to have a partial influence. Originality/value-knowing the key enablers and relationship among them can help organizations to develop Six Sigma competences. It is one of the foremost attempts to model enablers of Six Sigma. The paper provides useful insights to the Six Sigma implementers, consultants, and researchers.
Today, manufacturers need to pay attention to the emotions of real users in order to design and manufacture of desired products so that buyers are able to differentiate a product from other products and show their satisfaction through their purchasing .In this study, through Kansei engineering approach, we will evaluate the real emotions and feelings of users and modify them through appropriate design specifications for using in packing design of new health product of Firouz Health Group (L.L.C). In this regards, based on Kansei engineering methodologies in first and second steps with the help of expert designers, desired domain and kansei words will be identified and also screened. Then, Certified vendors, due to the perception of the demands of the users, are requested to determine the technical characteristics of the users and finally the collected data using Pareto diagram is analyzed and featured properties are selected and using these properties, we adopt the selected products. In next step, for synthesizing kansei words and product properties through questioning with presence of certified developers and vendors, we continue to analyze and identify properties that cover Kansei words with appropriate and exact mode. They can be used in presenting desired model in the next step. In the fifth and final step using Topsis model, we will select the best option as the best product between designed model and selected products that are identified in third step.
The Internet of Vehicles (IoV) is a new framework for intelligent transportation systems. One of its goals is to improve safety and increase the quality of service for passengers. Topology changes in IoV present significant challenges to the function of safety applications. Most routing algorithms offer their solution, independent of infrastructure or based exclusively on infrastructure. Although the lack of access to roadside units disrupts infrastructure-based algorithms, methods that do not use infrastructure will also not enjoy the benefits of high-speed data transmission. This research proposes the reliable and infrastructure-independent routing (RIIR) routing algorithm to increase the reliability of real-time routing in the IoV. The RIIR, in addition to simultaneous covering capability to use intelligent infrastructure for accelerating data transmission and flexibility in conditions of lack of access to infrastructure, by introducing three new criteria entitled "history of vehicle movement," "conformity of vehicle speed with the harmonic speed average of nearby vehicles," and "number of reliable neighbors," scores vehicles and then selects the most suitable vehicles as route members. The RIIR measures the conformity of the previous presence of vehicles on the current route and the stability of their link with adjacent vehicles. Furthermore, it prevents congestion by halting the spread of route request messages at intersections not leading to the destination. The efficiency of the RIIR has been theoretically proven, and extensive simulations with multiple scenarios in SUMO and NS3 show the superiority of the RIIR in increasing the packet delivery rate and reducing route failure, average delay, and control overhead.
The aim of this paper is to present mathematical models optimizing all materials flows in supply chain. In this research a fuzzy multi-objective nonlinear mixed-integer programming model with piecewise linear membership function is applied to design a multi echelon supply chain network (SCN) by considering total transportation costs and capacities of all echelons with fuzzy objectives. The model that is proposed in this study has 4 fuzzy functions. The first function is minimizing the total transportation costs between all echelons (suppliers, factories, distribution centers (DCs) and customers). The second one is minimizing holding and ordering cost on DCs. The third objective is minimizing the unnecessary and unused capacity of factories and DCs via decreasing variance of transported amounts between echelons. The forth is minimizing the number of total vehicles that ship the materials and products along with SCN. For solving such a problem, as nodes increases in SCN, the traditional method does not have ability to solve large scale problem. So, we applied a Meta heuristic method called Genetic Algorithm. The numerical example is real world applied and compared the results with each other demonstrate the feasibility of applying the proposed model to given problem, and also its advantages are discussed.
Energy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The most important objective of the present study is to minimize the energy consumption of sensors and increase the IoT network's lifetime by applying multi-objective optimization algorithms when selecting cluster heads and routing between cluster heads for transferring data to the base station. In the present article, after distributing the sensor nodes in the network, the type-2 fuzzy algorithm has been employed to select the cluster heads and also the genetic algorithm has been used to create a tree between the cluster heads and base station. After selecting the cluster heads, the normal nodes become cluster members and send their data to the cluster head. After collecting and aggregating the data by the cluster heads, the data is transferred to the base station from the path specified by the genetic algorithm. The proposed algorithm was implemented with MATLAB simulator and compared with LEACH, MB-CBCCP, and DCABGA protocols, the simulation results indicate the better performance of the proposed algorithm in different environments compared to the mentioned protocols. Due to the limited energy in the sensor-based IoT and the fact that they cannot be recharged in most applications, the use of multi-objective optimization algorithms in the design and implementation of routing and clustering algorithms has a significant impact on the increase in the lifetime of these networks.
Nowadays, recommender systems have reshaped the ways of information filtering between websites and the users in order to identify the users' interests and generate product suggestions for the active users. Recommender systems are generally divided into three groups: Contentbased, Knowledge-based, and collaborative-based, and in some cases hybrid. The main idea of collaborative filtering is that they predict a user's interest in new items based on the recommendations of other people with similar interests. This Approach does not require having knowledge about items. Collaborative filtering has two main types: Memory-based and Model-based . Memory based Collaborative filtering makes use of user rating dataset to compute similarity index between set of users or set of items. The main purpose of this article is to offer a Memory-based Collaborative recommender system in order to optimize the results of Collaborative filtering algorithm. In the proposed method, the combination of fuzzy Grey Wolf Optimizer algorithm and Lion Optimization Algorithm is used to find the most similar users to the target user. The results of the proposed method confirmed a significant increment in Precision, Recall and F-measure in comparison with baseline methods.
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