Selection of Agricultural Machinery Based on Improved CRITIC-Entropy Weight and GRA-TOPSIS Method

Abstract

In view of the problems existing in the process of agricultural machinery selection, such as inadequate decision-making information, strong subjectivity and quantification difficulty of the index weight assignment, and accuracy deficiency of the selection results, a model on the selection of agricultural machinery based on the improved CRITIC-entropy weight and GRA-TOPSIS method was established in this study. Through analysis, based on the construction of a comprehensive evaluation index system for the selection of agricultural machinery, the combined weight value was determined by combining the weights obtained using the improved CRITIC method and the weights obtained using the entropy weight method. The grey relational analysis method was also combined with the TOPSIS method. The power machinery combination with 88.2 and 73.5 kW of the 68th Regiment of the Fourth Division of Xinjiang Production and Construction Corps was used as an example for verification to determine the optimal power machinery combination. Results indicated that the ranking results were consistent, and the GRA-TOPSIS method was of the greatest degree of discrimination, which was conducive to the selection and evaluation of agricultural machinery equipment. Moreover, the equipment selection results were determined after the comprehensive ranking of machinery types under the different subjective preferences of decision makers was performed.

A New Model Supporting Stability Quality of Materials and Industrial Products

“…The concept of the model is based on the integration of selected instruments of quality management and decision support methods in a fuzzy decision-making environment. These techniques were: SMART(-ER) method [30], method of choice of the team of experts [12,31], brainstorming (BM) [32,33], Ishikawa diagram (causes and effects) with the 5M rule [6][7][8][9], the technique of importance in Liker scale [5,7], average arithmetic, and Grey Relational Analysis (GRA) [34][35][36][37][38][39].…”

“…The purpose is to determine the main causes, i.e., having the maximum weight, the most impact on incompatibility. The GRA method has application for a small number of data (that is, even 4 data), where it is a common phenomenon during the analysis of causes of incompatibility [38,39].…”

“…The choice of the GRA method resulted from its application to supporting decisions in the fuzzy (uncertain) area [34,35], where it is adequate to verify the importance of causes of incompatibility causes (determined subjectively by team of experts [36,37]. Furthermore, the GRA method has application for a small number of data (i.e., even 4 data), where it is a common phenomenon during analysis of causes of incompatibility [38,39], except that it conditioned versatility of the proposed model, where it is possible to verify both a large and a small number of causes. Ultimately, it is possible to determine adequate improvement actions.…”

“…• the minimum number of potential causes determined for a single category (5M) in the Ishikawa diagram is equal to 4 causes [36,37,39]; • the minimum number of all second-order causes in the Ishikawa diagram should be equal to 4 causes [34,35,39]; • Second-order causes are causes having the greatest impact on incompatibility of all potential causes [12,38];…”

“…the causes of potential incompatibility may or may not be of equal importance to each other, i.e., they may have the same or different influence (severity) on the occurrence of incompatibility [12,39]; • verification of the impact of causes on occurred the incompatibility is supported by the process of importance of causes on the Likert scale by selected teams of experts [12,38].…”