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DETERMINATION OF THE TECHNOLOGICAL VALUE OF COTTON FIBRE:

A COMPARATIVE STUDY OF THE TRADITIONAL AND MULTIPLE-CRITERIA DECISION-MAKING APPROACHES

Abhijit Majumdar1, Prabal Kumar Majumdar2 & Bijan Sarkar3

1College of Textile Technology, Berhampore 742 101, India Email: abhitextile@rediffmail.com

2College of Textile Technology, Serampore 712 201, India Email: pkm5@rediffmail.com

3Department of Production Engineering, Jadavpur University Kolkata 700 032, India

Email: bijonsarkar@email.com

Abstract

This paper presents a comparative study of the methods used to determine the technological value or overall quality of cotton fibre. Three existing methods, namely the fibre quality index (FQI), the spinning consistency index (SCI) and the premium-discount index (PDI) have been considered, and a new method has been proposed based on a multiple-criteria decision-making (MCDM) technique. The efficacy of these methods was determined by conducting a rank correlation analysis between the technological values of cotton and yarn strength. It was found that the rank correlation differs widely for the three existing methods. The proposed method of MCDM (multiplicative AHP) could enhance the correlation between the technological value of cotton and yarn strength.

Key words:

analytic hierarchy process, cotton fibre, fibre quality index, premium-discount index, spinning consistency index, technological value

Introduction

Determining the technological value of cotton fibre is an interesting field of textile research. The quality of final yarn is largely influenced (up to 80%) by the characteristics of raw cotton [1]. However, the level to which various fibre properties influence yarn quality is diverse, and also changes depending on the yarn manufacturing technology. Besides, a cotton may have conflicting standards in terms of different quality criteria. Therefore, the ranking or grading of cotton fibres in terms of different quality criteria will certainly not be the same. This will make the situation more complex, and applying multiple-criteria decision-making (MCDM) methods can probably deliver a plausible solution. The solution must produce an index of technological value or overall quality of cotton fibre, and the index should incorporate all the important fibre parameters. The weights of the fibre parameters should be commensurate with their importance on the final yarn quality.

Traditionally, three fibre parameters have been used to determine the quality value of cotton fibre. These are grade, fibre length and fibre fineness. The development of fibre testing instruments such as the High Volume Instrument (HVI) and the Advanced Fibre Information System (AFIS) has revolutionised the concept of fibre testing. With the HVI it is pragmatically possible to determine most of the quality characteristics of a cotton bale within two minutes. Based on the HVI results, composite indexes such as the fibre quality index (FQI) and spinning consistency index (SCI) can be used to determine the technological value of cotton; this can play a pivotal role in an engineered fibre selection programme [2-3].

In this paper, a new method of determining the technological value of cotton using a multiplicative analytic hierarchy process (multiplicative AHP) of the MCDM method is postulated. The technological value of cotton was also determined by the three traditional methods, namely the fibre quality index (FQI), the spinning consistency index (SCI) and the premium-discount index (PDI). The ranking of


cotton fibres produced by these four methods was compared with the ranking of final yarn tenacity, and a rank correlation analysis was carried out.

Overview of MCDM and AHP

Multiple Criteria Decision Making is a well-known branch of Operations Research (OR), which deals with decision problems involving a number of decision criteria and a finite number of alternatives. Various MCDM techniques, such as the weighted sum model (WSM), the weighted product model (WPM), the analytic hierarchy process (AHP), the revised AHP, the technique for order preference by similarity to an ideal solution (TOPSIS), and elimination and choice translating reality (ELECTRE), can be used in engineering decision-making problems, depending upon the complexity of the situation [4- 8] The Analytic Hierarchy Process (AHP), introduced by Saaty [9-12], is one of the most frequently discussed methods of MCDM. Although some researchers [13-16] have raised concerns over the theoretical basis of AHP, it has proven to be an extremely useful decision-making method. The reason for AHP’s popularity lies in the fact that it can handle the objective as well as subjective factors, and the criteria weights and alternative scores are elicited through the formation of a pair-wise comparison matrix, which is the heart of the AHP.

Details of AHP methodology  Step 1:

Develop the hierarchical structure of the problem. The overall objective or goal of the problem is positioned at the top of the hierarchy, and the decision alternatives are placed at the bottom. Between the top and bottom levels are found the relevant attributes of the decision problem such as criteria and sub-criteria. The number of levels in the hierarchy depends on the complexity of the problem.

Step 2:

Generate relational data for comparing the alternatives. This requires the decision maker to formulate pair-wise comparison matrices of elements at each level in the hierarchy relative to each activity at the next, higher level. In AHP, if a problem involves M alternatives and N criteria, then the decision maker has to construct N judgment matrices of alternatives of M x M order and one judgment matrix of criteria of N x N order. Finally, the decision matrix of M x N order is formed by using the relative scores of the alternatives with respect to each criterion. In AHP, the relational scale of real numbers from 1 to 9 and their reciprocals are used to assign preferences in a systematic manner. When comparing two criteria (or alternatives) with respect to an attribute in a higher level, the relational scale proposed by Saaty [9-12] is used. The scale is shown in Table 1.

Table 1. The fundamental relational scale for pair-wise comparisons





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