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Showing 3 results for Mostafaeipour
Implementation of Traditional (S-R)-Based PM Method with Bayesian Inference
Mohammad Saber Fallah Nezhad, Ali Mostafaeipour,
Volume 25, Issue 1 (IJIEPR 2014)
Abstract
In order to perform Preventive Maintenance (PM), two approaches have evolved in the literature. The traditional approach is based on the use of statistical and reliability analysis of equipment failure. Under statistical-reliability (S-R)-based PM, the objective of achieving the minimum total cost is pursued by establishing fixed PM intervals, which are statistically optimal, at which to replace or overhaul equipments or components. The second approach involves the use of sensor-based monitoring of equipment condition in order to predict occurrence of machine failure. Under condition-based (C-B) PM, intervals between PM works are no longer fixed, but are performed only “when needed”. It is obvious that Condition Based Maintenance (CBM) needs an on-line inspection and monitoring system that causes CBM to be expensive. Whenever this cost is infeasible, we can develop other methods to improve the performance of traditional (S-R)-based PM method. In this research, the concept of Bayesian inference was used. The time between machine failures was observed, and with combining Bayesian Inference with (S-R)-based PM, it is tried to determine the optimal checkpoints. Therefore, this approach will be effective when it is combined with traditional (S-R)-based PM, even if large number of data is gathered.
Determining the Tariffs of Physicians Using a Combined Model of Data Mining Techniques and Fuzzy Logic in Health Insurance
Amir Akbarzadeh Janatabad, Ahmad Sadegheih, Mohammad Mehdi Lotfi, Ali Mostafaeipour,
Volume 33, Issue 1 (IJIEPR 2022)
Abstract
The health insurance system can play an effective role to control health expenditures. The purpose of this study is to provide a model for estimating the physician visit tariffs. To achieve this goal, a hybrid model was used. fuzzy logic is the most appropriate tool for controlling systems and deriving rules for the relationship between inputs and outputs. So, the output of the data mining techniques enter the fuzzy logic as an input variable. The data were collected from the Health Insurance Organization of Iran in two sections including the physicians' costs and physicians' deductions. Owing to the techniques used in this model, NN had the least error, as compared to other data mining techniques (0.0034 and 0.0013, respectively). After defining the variables, membership functions and fuzzy logic rules, the accuracy of the whole control model was confirmed by random data. This research has dealt with the domains of health insurance , their connections and defining effective variables better and more extensively than the other studies in the field.
Identifying and Prioritizing the Factors Affecting Renewable Energy Development: A Case Study
Ali Mostafaeipour, Ghasem Ghorbannia Ganji, Hasan Hosseini-Nasab, Ahmad Sadegheih,
Volume 34, Issue 4 (IJIEPR 2023)
Abstract
Compared to coal and other fossil fuels, renewable energy (RE) sources emit significantly less carbon dioxide (CO2). In this sense, switching to such sources brings many positive effects to the environment through mitigating climate change, so the terms green energy and clean energy, have been derived from these constructive environmental impacts. Given the utmost importance of RE development, the primary objective of this study was to identify and prioritize the effective RE development strategies in Mazandaran Province, Iran, using different methods, including the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, along with other decision-making techniques. Recruiting a team of 11 industrial and academic experts, the strategies to implement in this region were developed in line with the RE development plans. For this purpose, the Multi-Criteria Decision-Making (MCDM) methodologies were utilized within the gray fuzzy environment to manage the existing uncertainties. The Gray-Additive Ratio Assessment System (Gray ARAS) was further applied to rank the main factors at each level. According to the SWOT analysis and the Stepwise Weight Assessment Ratio Analysis (SWARA) outcomes, among the major factors shaping RE development in Mazandaran Province, Iran, the economic criterion, with the final weight of 0.24, was ranked first; and then the geographical and environmental criteria, having the final weights of 0.23 and 0.19, were put in the second and third places, respectively. In this regard, appropriate location, with the final weight of 0.226, was ranked first; and subsequently pollution reduction and energy production costs, receiving the final weights of 0.103 and 0.094, were the second and third sub-criteria, respectively. As a final point, the validation results based on the Gray-Weighted Aggregated Sum Product Assessment (Gray-WASPAS) and ranking obtained through the Gray-ARAS were confirmed.
 
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