topsis algorithm

This research aims to provide a model to compress the schedule by selecting the optimal activities in terms of cost, time, quality and risk using TOPSIS method. The current research is applied in terms of results and its results can be used in the direction of developing project management techniques and moving towards the application of engineering techniques in improving the performance of organizations implementing construction and investment projects. A library is divided. The library method was used to collect information related to the subject literature and the background of the research, and the field method was used to collect information to investigate the research objectives. In this research, the data collection tool is a questionnaire. The statistical population of the research for the questionnaire included 5 construction experts from the private sector, among whom the questionnaire was distributed. In an optimization problem with balancing time, cost, quality and risk, the appropriate implementation methods for performing a set of activities of a project from the beginning to the end should be chosen in such a way that the factors of time, cost, quality and risk of the whole project implementation are optimized. In this research, TOPSIS algorithm was used to solve the problem.

Image noise removal is very important in most image processing analysis. Among various noises that can damage the image, Gaussian noise is one of the most notable. There are several ways to reduce image noises, including transformation-based methods, filter-based methods, and non-local methods.In most of the methods, only one criterion (i.e. signal-to-noise ratio) is used to reduce the image noise, and even some methods do not retain image quality and structural details of the image such as lines and edges.In this paper, the TOPSIS method was used to rank different filters of Gaussian noise filtering based on various criteria that influence the evaluation of image quality.In fact, regarding the standard deviation degree of the Gaussian noise, it was intended to know what kind of noise removal filter which contains the considered criteria should be used. The computational results of the proposed method were evaluated on different images with various standard deviation of Gaussian noise, and consequently, the filter has been specified corresponding to the degree of Gaussian noise for the application.

Sand entering production wells is one of the oldest problems faced by oil companies and one of the toughest to solve. Production of sand during oil production causes severe operational problem for oil producers. Several techniques have been used for sand production control in sandstone reservoirs. Sand screens often serve as the primary barrier to sand production from weakly consolidated reservoirs. Screen technology has improved significantly in the areas of sand retention, mechanical integrity, and quality control. As future field developments trend towards more hostile environments, requiring fewer wells at higher rates, the overall reliability and longevity of sand screens will be increasingly more important in this high cost environment. Screening of IOR methods is a multi-criteria decision making process, and the Multi-Criteria Decision Making (MCDM) method as a systematic statistical method, can be applied in this regard. In this paper, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as one of the methods under the MCDM category was used to screen an Iranian oil well. The screening method was employed for 5 different sand control techniques using a wide range of properties and conditions. The analysis used a database of successful IOR projects across the Ahvaz and Mansuri fields. The relative importance of the reservoir parameters was determined based on the Analytic Hierarchy Process (AHP) at eight importance levels. The findings showed that Resin – Aromatic is the best method for that well then Slotted liner, ESS, Resin and Gravel Pack are in next ranks.