استفاده از روش GIS-AHP برای ارزیابی مستعد بودن زمین در زراعت ذرت در منطقه نیمه خشک ، ایران
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 17
هدف از این مطالعه تهیه نقشه های در زمینه استعداد زمین در زراعت ذرت در خاک های آهکی و شور در دشت مرودشت ، ایران است. برای تخمین وزنی ویژگی های خاک ، اقلیم و توپوگرافی از روش چندمعیاره ای از فرآیند سلسله مراتبی تحلیلی (AHP) استفاده شده است. طبق نتایج، بافت خاک بیشترین ضریب وزنی ویژه (0.20) را در زراعت ذرت نشان داد و پس از آن، هدایت الکتریکی (121/0) ، شیب (1 2 0) و pH (1111/0) بیشترین ضریب وزنی را نشان داد. نقشه مستعد بودن اراضی نشان داد که 38.72٪ (76.646.7 هکتار) از اراضی کشاورزی مورد مطالعه، خاک مستعدی در تولید ذرت داشتند یعنی در طبقه مناسب ، 26.89٪ (53216.0 هکتار) در طبقه متوسط و 9/23٪ (47473 هکتار) در طبقه کمی مناسب قرار گرفتند. حدود ٪ 41/10 (4/2086٪) منطقه مورد مطالعه مناسب زراعت ذرت نبود. می توان دریافت که داده های مربوط به ویژگی های خاک ، آب و هوا و توپوگرافی به نظر متخصصان محلی، اولین قدم در کشت محصولات زراعی است.
کلمات کلیدی: داده های خاک | مدل سازی | توپوگرافی | روش AHP | GIS | مرودشت
|مقاله ترجمه شده|
How to finance for establishing hydrogen refueling stations in China? An analysis based on Fuzzy AHP and PROMETHEE
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Fuel Cell Vehicles are considered as a promising alternative for future sustainable transportation, while the deployment of hydrogen refueling stations is one of the major barriers that blocking the commercial introduction of Fuel Cell Vehicles. Since the establishment of hydrogen infrastructures not only requires quite a large investment, but also needs efficient project management and operation. Therefore, how to finance and operate hydrogen infrastructures is a difficult question for decision makers. In this study, it introduced four business models for financing and operating hydrogen refueling stations: Build-Operate- Transfer, Transfer-Operate-Transfer, Public-Private-Partnership, and Asset- Backed Securitization, and identified six criteria for prioritizing them. Then, it employed Fuzzy Analytic Hierarchy Process to determine the weight for the criteria, and compared the performance of these models with respect each criterion. Finally, the method of Preference Ranking Organization Method for Enrichment Evaluations was used to determine the priority of each model for financing hydrogen refueling stations, and a Sensitivity Analysis was conducted to find the most appropriate model in different situations. The results indicated that financing difficulty, project risks, and financing costs are the most important factors for strategic investors to involve in financing hydrogen refueling stations. Among the four financing models, Public-Private-Partnership and Transfer-Operate-Transfer models are turned out to be more preferable for financing hydrogen infrastructure in China. Some policy implications have also been provided for the establishment of hydrogen refueling stations.
Keywords: Hydrogen refueling stations | Financing models | Fuzzy-AHP | PROMETHEE | Sensitivity analysis
The application of multi-criteria decision analysis methods into talent identification process: A social psychological perspective
استفاده از روشهای تصمیم گیری چند معیار در فرآیند شناسایی استعداد: چشم انداز روانشناختی اجتماعی-2020
This case study offers a new insight into application of multiple-criteria decision-making methods (MCDM) to social identity issues in the context of talent management. This study used MCDM to help a high-tech company to identify potential talents in its sale and marketing team (n=54). MCDM adjusted subjective information consisted of intangible organisational political issues into a transparent, objective benchmark. The transparency and consistency of this evaluation process reduced potential social identity disruption between individuals or groups. Furthermore, the involvement of multiple decision-makers (both managers and employees) in the talent identification procedure enhanced employees motivation for further development.
Keywords: Talent management | Multi-criteria decision making | AHP | Flowsort | GAIA | Visual management
Taxonomical classification of barriers for scaling agile methods in global software development environment using fuzzy analytic hierarchy process
طبقه بندی طرح موانع برای مقیاس گذاری روش های چابک در محیط توسعه جهانی نرم افزار با استفاده از فرآیند سلسله مراتبی تحلیلی فازی-2020
Increasingly, software development organizations are scaling agile practices in the global software development (GSD) environment in order to meet the requirements of the quickly changing and regularly developing business environment. The main objectives of this study are to investigate the key barriers and develop a prioritization-based taxonomy of the barriers for scaling agile development in the GSD environment. Total twenty-two barriers were extracted from the available literature and categorized into five categories, i.e. ‘‘human resources management’’, ‘coordination’’, ‘‘technology’’, ‘‘project management’’, and ‘‘software methodology’’. In the next phase, the identified barriers and their categories were further validated using the questionnaire survey. In the final phase, fuzzy-AHP method, a multi-criterion decision making (MCDM) technique, was applied to prioritize and taxonomy of identified barriers and their related categories was designed. The contribution of this study is not limited to investigate the barriers, but it also provides the roadmap to tackle the issues related to the scaling agile methods in the GSD environment.
Keywords: Global software development | Agile development | Scaling barriers | Fuzzy-analytic hierarchical process
Risk evaluation of large-scale seawater desalination projects based on an integrated fuzzy comprehensive evaluation and analytic hierarchy process method
ارزیابی ریسک پروژه های آب شیرین کن در مقیاس بزرگ بر اساس یک ارزیابی جامع فازی یکپارچه و روش فرآیند سلسله مراتبی تحلیلی-2020
Desalination projects play a vital role in the water supply of coastal regions with scarce water resources. The risks associated with desalination projects are worth investigating, especially for large-scale projects. This paper presents the risk identification and evaluation processes of large-scale desalination projects. Two levels of risk indicators are identified and the first-level risks include water intake and outfall risk, processing risk, financial risk and circumstance risk. With the identified risk indicators, an integrated fuzzy comprehensive evaluation (FCE) and analytic hierarchy process (AHP) method is introduced to conduct quantitative risk evaluations for large-scale desalination projects. Twenty experts in desalination-related fields are invited to vote to determine the weighting vectors for the FCE through the AHP. They also participate in deciding the membership matrixes in the FCE for three practical desalination projects. The evaluation results indicate that the overall risks of all the considered projects are at the “Very low” level. Finally, to diminish the potential risks, several instructions and recommendations are suggested that depend on the evaluation outcomes. It is expected that the current risk evaluation research will make remarkable contributions to the risk management and control of large-scale desalination projects and further promote the development of the desalination industry.
Keywords: Desalination project | Risk identification | Risk evaluation | Fuzzy comprehensive evaluation | Analytic hierarchy process
GIS-based groundwater potential mapping in Shahroud plain, Iran: A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches
نقشه برداری پتانسیل آب زیرزمینی مبتنی بر GIS در دشت شاهرود ایران: مقایسه بین روشهای آماری (دو متغیره و چند متغیره) ، داده کاوی و MCDM-2019
In arid and semi-arid areas, groundwater resource is one of themost importantwater sources by the humankind. Knowledge of groundwater distribution over space, associated flow and basic exploitation measures can play a significant role in planning sustainable development, especially in arid and semi-arid areas. Groundwater potentialmapping (GWPM) fits in this context as the tool used to predict the spatial distribution of groundwater. In this researchwe tested four GIS-basedmodels for GWPM, consisting of: i) randomforest (RF); ii) weight of evidence (WoE); iii) binary logistic regression (BLR); and iv) technique for order preference by similarity to ideal solution (TOPSIS) multi-criteria. The Shahroud plain located in Iran, was selected to research thewater scarcity and overexploitation of groundwater resources over the past 20 years. In this research, using Iranian Department ofWater ResourcesManagement data, and extensive field surveys, 122 groundwaterwell datawith high potential yield of ≥11m3 h−1 were selected for GWPM. Specifically, we generated four different models selecting 70% (n=85) of thewells and validated the resulting GWPmaps upon the complementary 30% (n=37).A total of fifteen ground water conditioning factors to explain the groundwater well distribution over the Shahroud plain were selected. From the Advanced Land Observing Satellite (ALOS), a DEM(30mresolution) was extracted to calculate a set of morphometric propertieswhichwere combinedwith thematic ones such as land use/land cover (LU/LC) and Soil Type (ST). Results show that in RF (LU/LC), LR (ST), and AHP (Slope) are the most relevant contributors to groundwater occurrence. After that, using the natural break method, final maps were divided into five susceptibility classes of very low, low,moderate, high, and very high. The accuracy of modelswas ultimately tested using prediction rate (validation data), success rate (training data) and the seed cell area index (SCAI) indicators. Results of validation show that BLR with prediction rate of 0.905 (90.5%) and success rate of 0.918 (91.8%) had higher accuracy than WoE, RF and TOPSIS models with respective prediction rates of 0.885, 0.873 and 0.870 (88.5%, 87.3%, and 87%) and success rate of 0.900, 0.889, and 0.881 (90%, 88.9%, and 88.1%). SCAI results show that all models have acceptable classification accuracy although BLR outperformed the other models in terms of accuracy. Results show that the combination of remote sensing (RS) data and geographic information system (GIS) with new approaches can be used as a powerful tool in GWPM in arid and semi-arid areas. The results of this investigation introduced a potential novel methodology that could be used by decision-makers for the sustainable management of ground water resources.
Keywords: Random forest | Weight of evidence | Binary logistic regression | Decision making | Semi-arid region
Vessel traffic scheduling method for the controlled waterways in the upper Yangtze River
روش برنامه ریزی ترافیک کشتی برای آبراه های کنترل شده در رودخانه یانگ تسه بالایی-2019
An Inland Waterway Transportation System is often claimed to be safe, efficient and economic. However, traffic congestion and vessel collision have become serious threats to marine safety, especially in the one-way waterways. The aim of this paper is to rank the vessels in the controlled waterways of the upper Yangtze River and to generate the optimal traffic commands for each vessel so as to ensure marine safety and traffic efficiency. In this study, the proposed approach is based on the combination of Fuzzy Analytical Hierarchy Process (FAHP) with an Expert System (ES). Vessel data collected from Automatic Identification Systems (AIS) are first analyzed by experts, and some external environment and internal factors are selected as Significant Influencing Factors (SIFs). FAHP is then used for modelling a hierarchical structure and determining the weight of each SIF. Finally, the sorted vessel sequence and appropriate traffic commands can be achieved using ES. The experiment result shows that the waiting time of vessels is averagely decreased about 22 min compared with the existing Traffic Signal Revealing System. This will not only significantly improve the efficiency and accuracy of the vessel traffic scheduling, but also increase the waterway capacity by reducing the travelling time.
Keywords: Vessel scheduling | Waterway transportation | One-way waterway | FAHP | ES
A fuzzy decision support system for managing maintenance activities of critical components in manufacturing systems
یک سیستم پشتیبانی تصمیم گیری فازی برای مدیریت فعالیت های نگهداری از قطعات مهم در سیستم های تولید-2019
Management of critical components in manufacturing systems aims at managing components with very low reliability or the highest risk which can cause disruptions in manufacturing. This study presents a method for identifying critical components and a decision support tool for managing maintenance activities of critical components in manufacturing systems. Unlike the traditional reliability function, the proposed method uses the duty cycle, utilization rate of capacity, safety stock effect, and redundancy effect. It also has the ability to calculate some of the costs associated with the reliability and maintenance. In addition to the proposed method, an expert system as a decision support tool has also been proposed to assist in managing maintenance activities of critical components. The proposed method and the developed decision support system have been tested with a real data set taken from an industrial company and a randomly generated data set. The results have shown that the proposed method has a more realistic impact on component reliability compared to the traditional reliability function. The experimental results have validated the credibility of the proposed decision support system to manage maintenance activities of critical components. Besides, two comparison tables have shown that the proposed decision support system outperforms some approaches such as ANN, FMEA, FMECA, and AHP.
Keywords: Critical component management | Maintenance management | Reliability | Fuzzy logic | Decision support sys
An expert fuzzy system for management of railroad bridges in use
سیستم فازی تخصصی برای مدیریت پل های راه آهن در حال استفاده-2019
Management of railroad bridges constitutes a typical decision-making problem which arises from the necessity to allocate limited financial resources to maintenance and repair work. It is a multiple criteria issue consisting in the arrangement of bridges with regards to the level of structure degradation which is decisive for the safety level of their users. So far, despite a large number of suggested calculation models, mainly of probabilistic character, no method has been noticed that would allow to rank any number of bridges taking into consideration a full variety of structure and material solutions. Therefore, in order to meet the need for a more effective model, an innovative approach has been proposed using the aggregated calculation algorithm which combines the modified Extent Analysis Fuzzy Analytic Hierarchy Process (EA FAHP) method and the Dominant Analytic Hierarchy Process (DAHP) one. It is a decision-making and calculation model which allows to reflect the actual assessment processes in a more favourable manner, as well as to take into account the diversity of solutions with respect to materials and structure of bridges. An example was given of the application of the suggested method on the selected group of railroad bridges in Poland that have been in use for over 50 years. The selected structures have more diverse structures of decks and are more prone to damage than road bridges. Due to the lack of algorithms similar to the one discussed within this paper, it was verified in a limited scope with the use of two different methods by way of comparing the accordance of bridge ranking which is of importance for making a decision concerning the performance of maintenance and repair work.
Keywords: Bridge management system (BMS) | Existing railway bridges | Assessment | Maintenance | Extent analysis fuzzy analytic hierarchy process | (EA FAHP) | Dominant analytic hierarchy process (DAHP) | Multi-criteria decision analysis (MCDA) | Fuzzy group decision making (FGDM)
Application of machine learning to laboratory safety management assessment
کاربرد یادگیری ماشین در ارزیابی مدیریت ایمنی آزمایشگاهی-2019
This paper proposes a new method to evaluate laboratory safety management based on machine learning that aims to address the shortcomings of traditional laboratory safety evaluation methods, including poor accuracy, large influence of human factors, and lack of a unified evaluation system. In this paper, the safety data of laboratories in Southwest University were collected using a safety checklist, and the weight of each factor affecting laboratory safety was analyzed using a Fuzzy Analytic Hierarchy Process (FAHP). The simulation results showed that the model gave a more accurate and reasonable safety risk level of the laboratory, verified the rationality and feasibility of the established method, and realized potential loopholes in the process of laboratory management and college students operations. According to the evaluation results, the machine learning principle and the existing maintenance knowledge of the evaluation knowledge base are applied to provide effective measures and suggestions for users to complete the process of risk assessment. Through risk assessment, the professional skill and the incident control could be improved. The model was easy to operate and has a good application value for ensuring the personal and property safety of laboratory users.
Keywords: Machine learning | Fuzzy mathematics | Laboratory safety management evaluation | Risk reduction