The Indian fiber inspection industry has a unique method for determining the technical value of cotton fiber or its overall quality. They use the existing fiber quality assessment (FQI), spinning consistency assessment (SCI) and premium-disburse assessment (PDI). The method determines the technical value of cotton fiber and proposes a new measurement method based on multi-criteria decision making (MCDM) technology, which is worth learning.
The Indian industry believes that the analysis of the technical value of cotton yarn and the strength of yarn can determine the efficacy of these methods. However, after analysis, it is found that the existing three methods have different grade correlations. Nevertheless, their proposed MCDM method can be used to improve the technical value of cotton yarn and yarn toughness.
The identification of cotton fiber technology value is a very interesting area in Indian textile research. They believe that the quality of the yarn depends mainly on the characteristics of the raw cotton, which is affected by up to 80%. However, the quality of the yarn is affected by various fiber characteristics to a large extent, depending on the spinning manufacturing technology. For different quality standards, the standard for measuring cotton quality may conflict with other standards. Therefore, the quality of cotton fiber is measured by these different quality standards, and the different results are unquestionable. As a result, the identification of Indian cotton fiber technology values ​​is more complicated, so they may adopt a multi-criteria decision-making (MCDM) technology as a viable solution.
The solution must give an indication of the technical value of the cotton fiber or its overall quality, and the index should contain all important fiber parameters. These fiber parameters play a significant role in the final quality of the yarn. By convention, the quality values ​​of cotton fibers are identified by these three fiber parameters (ie, fiber grade, fiber length, and fiber fineness).
The development of fiber testing instruments such as high-volume cotton test equipment (HVI) and advanced fiber information systems (AFIS) is also a huge revolution in the cause of fiber testing. If HVI is used, it is entirely feasible to identify most of the quality characteristics of the bales in two minutes. Based on HVI's efficient functions, comprehensive assessments such as fiber quality assessment (FQI) and spinning consistency assessment (SCI) can be used to identify the technical value of cotton, which will play a key role in carefully planned fiber selection options.
In addition, the Indian industry has set a new method for identifying the technical value of cotton, which refers to the multi-level analysis (AHP) in multi-criteria decision making (MCDM) technology. They also believe that the identification of cotton technical value also depends on the three traditional methods, namely fiber quality assessment (FQI), spinning consistency assessment (SCI) and premium-discount assessment (PDI). After comparing the cotton fiber grades obtained by the above four identification methods with the yarn spinning strength grades, the grade correlation can be analyzed.
Overview of Multi-Criteria Decision Making (MCDM) and Multi-Level Analytic Method (AHP)
Multi-criteria decision-making is a familiar branch of operations research that deals with decision-making issues, including the determination of several decision criteria and the determination of a limited number of choices.
Depending on the complexity of the situation, various multi-criteria decision techniques, such as weighted total addition (WSM), weight multiplication (WPM), analytic hierarchy process (AHP), revised analytic hierarchy process, similar to ideal solutions TOPSIS and the combination of AHP and selection techniques (ELECTRE) can be used to solve engineering decision problems.
The analytic hierarchy process proposed by Indian operations researcher Satie is the most controversial method in multi-criteria decision-making technology. Although some researchers have worried or questioned the theoretical basis of AHP, practice has proved that AHP is one. A very effective decision making method. The reason why AHP is so popular is because it can be handled properly for both subjective and objective issues. The weighting criteria and alternative scores can be derived from the structural analysis of the pairwise comparison matrix, so the pairwise comparison matrix is ​​the core of the analytic hierarchy process.
Detailed operation instructions of the analytic hierarchy process
The first step: build a hierarchical structure of the problem.
First, the overall goal of the problem is positioned at the top of the hierarchy framework, and the decision plan is placed at the bottom of the framework. The relevant attributes of the problem, such as the criteria, and the sub-criteria are placed at the top level of the top and bottom of the frame. The number of intermediate levels depends on the complexity of the problem.
Step 2: Generate relevant data for comparing alternative selections.
The determiner is first required to develop a pairwise comparison matrix for each level in the hierarchical framework while each actual activity relative to the next higher level. Secondly, in the case of using the level analysis, if a certain problem involves M types of alternatives and N kinds of evaluation criteria, then the determiner constructs a decision matrix of N MxM-order alternatives and an NxN-order criterion evaluation. matrix. Finally, a determination matrix of MxN order is constructed using the relative numbers of alternatives corresponding to each rating criterion. Using the analytic hierarchy process, in the systematic approach, the allocation can be appropriately adjusted by observing the proportional relationship between the real numbers from one to ten and their reciprocals.
To compare two criteria (or alternatives) that correspond to higher-level performance, use the Heidi scale relationship. The technical value of the cotton fiber obtained by various methods and the grade correlation coefficient (Rs) between the technical value of the cotton and the yarn toughness can be observed by observation, ranging from a minimum of 0.098 to a maximum of 0.817. In general, the lowest ratio is the FQI model, and the highest is the PDI model. The multiplicative AHP model, which can be seen as a variant of the traditional FQI model, has a fairly high ratio of 0.738 rupees for æ°–22 and 0.716 rupees for æ°–30. The ratio of the SCI model is more general, 0.401 for æ°–22 and 0.459 for æ°–30.
The traditional FQI model can be regarded as a basic multiplication model, in which all standard weights (Wj) can be regarded as a unity. However, in practice, this assumption is completely ineffective because the effects of various fiber properties on yarn performance are not exactly the same. Therefore, in the multiplicative model, due weight should be given to the weight of different decision criteria. The modification of the multiplication AHP model proposed here can increase the ratio. To determine the value of the resulting value, the best technical method for identifying cotton is the premium-discount index.
However, in the premium-discount index model, the determinant clearly knows that the coefficient value used to judge the influence of fiber properties on the yarn toughness is the coefficient value, and whether the coefficient value is standard or not has not been determined. To determine the true accuracy of the premium-discount model, this model can be applied to new test samples, and none of these samples are used to develop applications related to fiber properties and yarn toughness.
Speaking of the multiplication AHP model, the relative weight of cotton fiber performance is obtained from the pairwise comparison matrix, and the entries are based on the previous experience of the decision makers, and the AHP model does not have any specific knowledge.
From this point of view, the Multi-Level Analytic Method (AHP) is a fairly flexible method of identification. As long as the value of the person has certain prior knowledge of the problem, the method is applicable to any situation.
To identify the technical value of cotton, Indian researchers have proposed a new multiplication hierarchical analysis model. This method uses a variant of the traditional FQI formula and increases the grade correlation between cotton technical value and yarn toughness. It is more logical to mix the appropriate weights of cotton performance into the multiplication formula for all cotton performances with equal weight.
The use of multi-level analytical methods to identify standards has played a key role in determining the value of Indian fiber values. The four methods mentioned here, the premium-discount method can reflect the superiority of the detection technology of cotton technology value, and the multi-level analysis, SCI and FQI model detection are the three identification methods still retained today. Of course, Indian cotton fiber researchers are able to comply with international standards for domestic fiber products as soon as possible, and they also refer to other multi-criteria determination methods.
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