Comparative analysis of AHP-TOPSIS and GA-TOPSIS methods for selection of raw materials in textile industries

Textile industry plays a very dominant role in the economic activities of many countries. The quality of textile goods primarily depends on the quality of the raw material i.e. fibre. In India, most of the textile industries are cotton based. As cotton is a natural fibre, it has lot of variability in its properties. Besides, the overall quality of cotton fibre depends on factors like strength, elongation, fineness, length, short fibre content etc. Therefore, determination of quality value of cotton is basically a Multi-criteria Decision Making (MCDM) problem. Traditionally, Fibre Quality Index (FQI), which is a multiplicative expression incorporating major cotton fibre properties, is used in the textile industry to determine the overall quality. However, equal weights are considered for all the cotton fibre properties (decision criteria) which is questionable as different cotton fibre properties have different influence on the yarn qualities. MCDM techniques are yet to be applied for solving the problem of cotton fibre quality evaluation. In this project, cotton fibres has been graded and selected based on AHPTOPSIS and GA-TOPSIS approaches. In case of AHP-TOPSIS approach, AHP has been used to elicit the weights of various cotton fibre properties (decision criteria) and then TOPSIS has been applied to rank various alternatives as per the closeness index value. In the GA-TOPSIS approach, the weights of the cotton fibre properties (decision criteria) have been determined by the genetic algorithm. The correlation coefficient between the quality of cotton fibre and yarn strength has been taken as the fitness function. Finally, a comparative analysis has been performed between the efficacies of the two approaches. Determination of technological value of cotton fibre is an appealing field of textile research. The quality of final yarn is largely (up to 80%) influenced by the characteristics of raw cotton [1]. However, the level of influence of various cotton fibre properties on yarn quality is diverse and it changes with the yarn manufacturing technology. Besides, a cotton may have conflicting standards in terms of different quality criteria. Therefore, the grading and selection of cotton fibres in terms of different quality criteria will certainly not be the same. This will make the situation more intricate and application of multiple criteria decision making (MCDM) methods can probably deliver a plausible solution. The solution must produce an index of quality value of cotton fibre and the index should incorporate most of the important cotton fibre parameters. The weights of the fibre parameters should commensurate with their importance on the final yarn quality. Based on the HVI (high volume instrument) results, multiplicative indexes like fibre quality index (FQI) and spinning consistency index (SCI) are used in textile industries to determine the quality value of cotton [2-5]. However, in case of FQI, all the cotton fibre properties are considered to have equal importance on the yarn quality. This assumption is practically not valid. On the other hand, SCI has been developed by USDA (United States Department of Agriculture) based on their research on Upland and Pima cotton and therefore the applicability of SCI on Indian cotton is questionable. To overcome these drawbacks of the existing cotton fibre grading and selection system, an attempt will be made in this project to apply MCDM techniques to solve the stated problem. In case of AHP-TOPSIS approach, AHP will be used to elicit the weights of various cotton fibre properties (decision criteria) based on the perception of the decision maker (expert) and then TOPSIS will be used to rank various alternatives as per the closeness index value. In the GA-TOPSIS approach, the weights of the various cotton fibre properties (decision criteria) will be determined by the genetic algorithm. The correlation coefficient between the strength of yarn and quality of cotton fibre will be taken as the fitness function. Finally, a comparative analysis will be performed between the efficacies of the two approaches. 2 Overview of Multi-criteria Decision Making Multi-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 weighted sum model (WSM), weighted product model (WPM), the analytic hierarchy process (AHP), revised AHP, technique for order preference by similarity to ideal solution (TOPSIS) and elimination and choice translating reality (ELECTRE) can be used in engineering decision making problems