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fuzzy method

در نشریات گروه برق
تکرار جستجوی کلیدواژه fuzzy method در نشریات گروه فنی و مهندسی
تکرار جستجوی کلیدواژه fuzzy method در مقالات مجلات علمی
  • Ehsan Allah Khoshkhoy Nilash, Mansour Esmaeilpour *, Behrooz Bayat, Alireza Isfandyari Moghaddam, Erfan Hassannayebi
    Fixed capital facility processes have many steps, control points, and approvals with long durations. In this regard, banks with more awareness and knowledge by analyzing and evaluating their processes can do better than their competitors in improving them and providing customer service. To tackle this challenge, process mining is one of the effective and efficient methods for analyzing processes, i.e., discovering and evaluating their quality. This paper aims to find and evaluate the model of the fixed capital facilities acceptance process based on the mentioned method. The proposed six-step method includes event logs preparation, process model discovery, evaluation and compliance checking, results analysis, analysis based on the fuzzy method, and comparison of results. The discovered process model is evaluated based on the quality dimensions of the process model, namely precision, fitness, simplicity, and generalization. Also, the results obtained from different methods are compared with each other. In addition to the discovery of the process model, one of the results was the heuristic algorithm having the best performance in terms of the mentioned criteria, with a value of 0.833. Particularly, it excelled in precision with a value of 0.656. The genetic algorithm, with a value of 0.946, exhibited the best fitness performance. Another result is the superior performance of the fuzzy technique compared to other methods. Furthermore, bottlenecks, activities with the highest repetition in a case, and branches and users with the most significant role in the process were identified.
    Keywords: Fixed Capital Facilities, Discovery, Process Mining, Fuzzy Method, Process Model Quality
  • Kianoosh Kianfar, Mahnaz Ahadzadeh Namin *, Akbar Alam Tabriz, Esmaeil Najafi, Farhad Hosseinzadeh Lotfi
    In this study, the multi-objective programming (MOP) method was used to solve network DEA (NDEA) models with assumption that, negative data is considered for the proposed NDEA model which consists of semi-negative and semi-positive input and output. At first, two stage and then k stage production models were formulated with consideration of negative data. In the multi-objective programming, two separate objective functions including the divisional efficiencies and the overall efficiency of the organization are modeled. In comparison to conventional DEA with negative data, the advantage of the proposed NDEA models is consideration of intermediate processes and products, in order to calculate the organization's overall efficiency. However, in conventional DEA, sub-stages of the organizations are neglected. To measure the efficiencies of an organization regarding interactive internal process, two case studies were investigated by application of the NDEA-MOP method with negative data. Case study 1 is focused on units with two stages having semi-negative and semi-positive indexes. In case study 2, units with three stages are evaluated. These units also have semi-negative and semi-positive indexes. The overall efficiency of each unit is calculated using the proposed models. Fuzzy approach as a solution procedure is applied.
    Keywords: Data envelopment analysis, Network DEA, semi, positive data, semi, negative data, overall efficiency, Fuzzy method
  • Mozhgan Toulabi *, Shahram Javadi

    A sensor network is made up of a large number of sensors with limited energy. Sensors collect environmental data then send them to the sink. Energy efficiency and thereby increasing the lifetime of sensor networks is important. Direct transfer of the data from each node to the central station will increase energy consumption. Previous research has shown that the organization of nodes in clusters and selection the appropriate cluster head increases the network lifetime. In this study, clustering, determine to cluster heads and the sink movement on the predefined paths has been done with fuzzy method. There are two inputs for the fuzzy model; residual energy of the node and distance from the sink. The output is priority of cluster heads. Sink moves base on the highest priorities on the predefined paths. Then by using genetic algorithm, the number of clusters, shape type and area is optimized. Fitness function is based on network lifetime.

    Keywords: Wireless Sensor Network, lifetime, fuzzy method, Genetic Algorithm, mobile sink
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