Open AccessCOPRAS ve MAIRCA Yöntemleriyle Çorum İlinde Kurulabilecek Kompost Tesislerinin Optimallik Sıralaması
Author(s) -
Sinan Dündar,
Hüdaverdi Bircan,
Hasan Eleroğlu
Publication year - 2022
Publication title -
türk tarım - gıda bilim ve teknoloji dergisi
Language(s) - English
Resource type - Journals
ISSN - 2148-127X
DOI - 10.24925/turjaf.v9isp.2523-2531.4918
Subject(s) - ranking (information retrieval) , compost , product (mathematics) , production (economics) , cluster analysis , cluster (spacecraft) , rank (graph theory) , decomposition , livestock , agricultural science , environmental science , computer science , mathematics , engineering , waste management , geography , statistics , forestry , economics , ecology , geometry , combinatorics , machine learning , biology , macroeconomics , programming language
The compost product, which is a biologically active substance, emerges as a result of microbial decomposition of organic materials under controlled conditions. This product, which is used for the improvement of soil structure and the development of agricultural products, also offers opportunities in terms of minimizing the damage caused by organic wastes to the environment. It is important to encourage efforts for compost production, especially in terms of both disposal and economic evaluation of wastes generated in animal production farms. Determining the most suitable location of a facility for the utilization of animal wastes as compost, which will be obtained from livestock enterprises scattered in different geographical areas, will be an essential study in terms of minimizing operating costs. For such a facility, it would be an appropriate approach to use multi-criteria decision making methods to choose among predetermined facility location alternatives. In this study, a total of 17 facility location alternatives with 83,163 cattle potential in Çorum province were ranked according to the criteria determined and weighted by means of SWARA method. The optimal ranking of 17 alternatives determined by K-Means clustering analysis was carried out by COPRAS and MAIRCA methods. According to the ranking results obtained from both methods, it was determined that cluster number 6 was in the first rank, cluster number 4 was in the second rank, and cluster number 3 was in the third rank.