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Pedological criterion affecting desertification in alluvial fans was investigated, for which the map of units was prepared by crossing maps of land use, geology, slope classes and grid layer created by the extension of ET Geo-Wizards in ArcGIS 10.3. Three indices of salinity, erodibility, and permeability of soil were considered and classified. Weights of criteria and consistency ratio were calculated by the AHP method and ELECTRE I method was used to prioritize the options. After creating the weighted super matrix and calculating the concordance and discordance matrix, the difference between dominance and defeat values were calculated. The results showed that the difference in values obtained from AHP-ELECTRE I technique varied from -15 to 16. The alluvial fans were classified into three classes of I, III, and IV from the viewpoint of pedological criterion affecting desertification by using AHP-ELECTRE I technique. Results showed that 71.99% of the area was in the low desertification potential, while 2.19% and 25.82% were in the high and very high desertification potential, respectively.

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The implementation of enterprise resource planning is complex, and the failure rate remains excessive. This study proposes a prediction approach based on FDEMATEL and FMCDM for ERP success. The literature has reported over 30 critical success factors for ERP implementation, but companies typically are not aware to exploit them. At first, about 21CSFs from literature and expert opinion are elaborated into key performance indices, which are categorized in three groups; organizational, tactical and technical factors. These factors associated with each stage of ERP implementation by ten local ERP experts. Second, a three stage ERP implementation model is proposed. Further, it uses fuzzy DEMATEL and the evaluation of ten experts to evaluate weighting of each CSFs. At the next step after using FMCDM method to obtain possible rating of success of ERP performance, remedial actions are identified to see if the performance is below expectation. The proposed approach is helpful to predict the success of ERP without actually adopted ERP in companies. Iran Khodro Khorasan Company is selected to illustrate the effects of the assessment model, which is also currently is using consulting company specializing in ERP implementation services. With further evaluation by local experts, the model has the potential to serve as a guideline for ERP implementation (even in other countries). Results confirmed that organizations considering organizational factors less than other factors. some of important factors are top management support, identification of ERP objects, change management, sufficient financial budget, project management, sufficient implementation team, effective communication and training.

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In order to ensure sustainable activities, companies independent upon working with defrent supplier's .In the manufacturing industries, raw material and assembly parts main comprise as much as 70 percent of production cost. For that, a purchasing department can play a vital role in reducing costs, and a purchasing manager's skill in performing his/her responsibility of choosing proper suppliers can greatly contribute to achieving that goal. This article focuses on the above-mentioned subject due to the importance of supplier choice for companies. Since the first times in history when the need for choosing proper suppliers was felt, many different methods and strategies have been utilized for such a task. Because of nature of that challenge, decision makers are faced with more than one criteria and high number of such criteria makes the challenge even more complex for them. Multi-criteria decision making has frequently been one of the approaches used by decision makers in the evaluation and selection of suppliers. In the research of this thesis a model of fuzzy multi-criteria and non-additive fuzzy integral has been presented for the supplier selection. The advantage of this model is that with the aid of Fuzzy Analytic Network Process the effects of the criteria on each other can be analyzed and with the fuzzy measure and fuzzy integral methods these interdependent relation between effects can be eliminated.

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Abstract Selecting a portfolio has always been a significant issue in Financial Management. The models presented for selecting the best portfolio have some deficiencies and after some time, their deficiencies would be revealed and they will be replaced by some other models. One of the problems with those models is neglecting the multifaceted indices and dimensions for final evaluation of portfolio, and these efficiencies will bring the validity of the evaluation results under question. In order to remove these efficiencies, one can use DEA (Data Envelopment Analysis) technique which is one of the MCDM (Multi-Criteria Decision Making) techniques. In this paper, two models have been presented; one finds the most efficient portfolio and the other one finds the most inefficient. In this paper, 95 companies now present in Tehran Stock Market have been investigated. The results demonstrate that out of those 95 companies, seven companies are efficient and 8 companies are utterly inefficient. Keywords: Data Envelopment Analysis, Multi-Criteria Decision Making (MCDM), Portfolio

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The rapid increase of urban population and waste production is one of the most important environmental problems in developed and developing countries. Million of tons of waste material are produced annually in the world, the correct disposal and management of which presents a main concern in the human societies. With the increase in population, and industrialization of societies accompanied by economic growth, the production of waste material has increased throughout the world. It is predicted that the population of the world will increase 2 to 3 Milliard people in the next 30 years. This means that urban waste material production will increase 3 to 4 times. Along with health and environmental adverse effects of the landfill site, emissions of carbon dioxide, the intensification of the process of global warming on a global scale also will follow. Absence appropriate supervision over management and accumulation of these outputs can engender many environmental problems, especially for people who live near the landfill to be followed. Thus, appropriate urban landfill site selection is a main issue related to the stability of cities and human environments. Geographical information systems (GIS) have appeared as useful, computer-based tools for the spatial operations like entry, storage, manipulation, analysis, and display of geographical data. Since GIS can manage a large amount of spatial data, it can serve as an ideal tool in the siting studies. In addition to the use of GIS in the landfill site selection studies, multi-criteria evaluation method to deal with the issues that decision makers and experts face with in landfill site selection is used. Combination of GIS and multi-criterion evaluation method provides a valuable tool for the solution of landfill site selection problems. In this study, fuzzy logic based on Weighted Linear Combination (WLC) were used for urban waste landfill site selection in Arak city in ARC GIS 10.1 software environment with respect to the ecological, and socio-economic parameters.
In this research, reviewing the international obligations in landfill site selection and using environmental expert views on weighting the effective ecologic, and socio-economic criteria in landfill site selection it was revealed that ecologic sub-criteria, including depth of groundwater resources, distance from surface water resources, and flood potential were of great importance in urban landfill siting. Moreover, among the socio-economic sub-criteria, distance from rural and urban population centers were vital in urban landfill site selection. In the present study, after the production of the final suitability map with fuzzy logic based on WLC, the spatial desirability was divided into 5 classes. The spatial desirability for landfill sites lies in the fuzzy membership degree of between 0.3 and 0.87. Around 11033 polygons with an area of 4146.03 square kilometers were found whose spatial suitability in the fuzzy membership degree of 0.3 to 0.87. The most spatial suitability lie in the fuzzy membership degree between 0.69 to 0.87. The spatial suitability of these areas with their fuzzy membership degree is as follows: very low suitability with the fuzzy membership degree of 0.3 to 0.47, low suitability with the fuzzy membership degree of 0.47 to 0.56, moderate suitability with the fuzzy membership degree of 0.56 to 0.63, high suitability with the fuzzy membership degree of 0.63 to 0.69, and very high suitability with the fuzzy membership degree of 0.69 to 0.87. The area of the existing polygons and their number are as follows: very low suitability polygons with the area of 270.73 km2 and number of 328 polygons, low suitability polygons with the area of 683.48 and number of 1687 polygons km2, moderate suitability polygons with the area of 1187.02 km2 and number of 3006, high suitability polygons with the area of 1212.44 km2 and number of 3954 polygons, very high suitability polygons with the area of 792.36 km2 and number of 2058 polygons.

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With economic development and population growth, the global need for energy is increasing steadily. Fossil fuels are the most commonly used fuel in the world, but their resources are limited. Therefore, for sustainable development, the need to use renewable energy sources is felt more than ever. Solar energy recognized as the most important and most affordable one. In Iran, the availability of suitable climate and sunlight in many areas and seasons has provided a good basis for using this kind of energy. The purpose of this research is to utilize a MCDM approach for evaluating the potential of different regions in Qazvin province for the establishment of a solar power station. In this regard, several evaluation criteria were identified and their importance was determined by the AHP method and then their uncertainty is modeled using fuzzy theory. Then, a potential map was developed using OWA and TOPSIS methods. Finally, the result of utilizing AHP-OWA method is compared with AHP-TOPSIS. Comparison of the weights of indicators shows the weather factors as important ones. In addition, according to the research findings, the Takestan region was recognized as a good area for establishing a solar power plant. Based on previous studies, construction of a 100MW solar power station in this area has been confirmed. This means that the proposed method is acceptable to be used by decision-makers as an effective tool.
 
 

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One of the most fundamental economic issues for holding companies is capital allocation. Typically, investors in selecting investment alternatives follow conflicting preferences and goals simultaneously. Therefore, developing a model based on available information can help decision makers to identify the most important competitive factors and focus their attention on the improvement of performance. However, several techniques have been introduced to determine the most important components. Analytical hierarchy process (AHP), a branch of multi criteria decision making (MCDM) methods, is a powerful tool for ranking a set of elements. Nevertheless, the AHP is disable to take into account the uncertainty involved in the process of decision making. On the other hand, intuitionistic fuzzy sets (IFS) are capable of handling the vagueness and ambiguity by using the scale of the pairwise comparisons represented by the IFS. The IFS-AHP (a combination of the IFS and AHP method) can lead to more precise description of the problem under consideration since the IFS is robust in describing complexity and uncertainty. Therefore, the IFS-AHP technique has much more advantages in comparison with the conventional AHP or fuzzy AHP. To demonstrate the potential application of the proposed approach, a real case study on ranking the critical factors influencing the Investment Options in the Holding Companies is illustrated. The results show that criterion C6 (Risk) with value of 0.1451 is the most important factor in Holding Companies.